Multi-type power supply cooperative scheduling operation method, equipment, medium and product

Abstract

The invention discloses a multi-type power supply cooperative scheduling operation method and device, a medium and a product, and relates to the field of new energy. The method comprises the following steps: firstly, establishing a power supply side output fluctuation minimum objective function O1, a new energy abandoned energy cost minimum objective function O2 and constraint conditions thereof, wherein the constraint conditions comprise constraint conditions of wind power generation, photovoltaic power generation and an energy storage power station; adopting a multi-target grey wolf optimization algorithm to solve an optimal solution set of new energy and energy storage power station output as an optimal solution of a new energy optimization layer; establishing a minimum target function O3 of dispatching operation energy consumption of the thermal power generating unit and constraint conditions thereof by combining the coal fuel quantity of the thermal power generating unit and the start-stop operation energy consumption; solving an optimal solution set of the start-stop state, the output state and the operation energy consumption of the thermal power generating unit by adopting an improved quantum particle swarm algorithm to serve as an optimal solution of a thermal power generating unit optimization layer; cooperative scheduling operation of multiple types of power supplies is guided based on the optimal solutions of the new energy optimization layer and the thermal power generating unit optimization layer, and the stability, accuracy, flexibility and economical efficiency of cooperative scheduling can be improved.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a method, device, medium and product for coordinated scheduling of multiple types of power sources. Background Art

[0002] Renewable energy generation such as wind power generation and photovoltaic power generation has the characteristics of uncertainty and intermittency, which leads to output fluctuations on the power supply side of the power system and increased operating energy consumption; at the same time, insufficient research on the flexibility adjustment capability of thermal power units has resulted in a low absorption rate of new energy in the power system. Therefore, in order to achieve large-scale development of new energy and high-proportion access to the grid, it is necessary to conduct in-depth research on the actual unit model and adjustment capability of the power supply side; at the same time, it is also necessary to establish a coordinated dispatching and operation strategy for multiple types of power sources to ensure the safe and stable operation of the power system and the rational and efficient use of various types of power sources. Summary of the invention

[0003] The purpose of this application is to provide a method, device, medium and product for coordinated dispatching of multiple types of power sources to improve the stability, accuracy, flexibility and economy of coordinated dispatching of multiple types of power sources in power systems.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a method for coordinated scheduling of multiple types of power sources, including:

[0006] The photovoltaic power generation, wind power and energy storage power stations connected to the grid are taken as the fluctuation reduction objects on the power supply side, and the minimum output fluctuation objective function O1 on the power supply side is established;

[0007] Establish the minimum objective function O2 for the cost of abandoned energy from new energy sources;

[0008] Establish constraints for O1 and O2, including wind power generation constraints, photovoltaic power generation constraints, and energy storage power station constraints;

[0009] Based on O1, O2 and their constraints, a multi-objective grey wolf optimization algorithm is used to solve the optimal solution set of the output of new energy and energy storage power stations as the optimal solution of the new energy optimization layer;

[0010] Combined with the coal fuel quantity and start-stop operation energy consumption of thermal power units, the minimum energy consumption objective function O3 and its constraint conditions for dispatching and operating thermal power units are established;

[0011] Based on O3 and its constraints, an improved quantum particle swarm algorithm is used to solve the optimal solution set of the start-stop state, output state and operating energy consumption of the thermal power unit as the optimal solution of the optimization layer of the thermal power unit;

[0012] Based on the optimal solution of the new energy optimization layer and the optimal solution of the thermal power unit optimization layer, the coordinated scheduling and operation of multiple types of power sources are guided; the multiple types of power sources include thermal power, wind power, photovoltaic power generation and energy storage power stations.

[0013] Optionally, the photovoltaic power generation, wind power and energy storage power stations connected to the grid are taken as fluctuation reduction objects on the power supply side, and the minimum output fluctuation objective function O1 on the power supply side is established, which specifically includes:

[0014] Using formula P cl,t =P f,t -P wpp,t -P pv,t -P esd,t Calculate the output power P that suppresses fluctuations on the power supply side at time t cl,t ; where P f,t is the system power load at time t; P wpp,t P is the actual wind power generation at time t; pv,t is the actual power generated by photovoltaic power generation at time t; P esd,t is the actual power generated by the energy storage power station at time t;

[0015] Using formula Calculate the average output power P that suppresses power supply fluctuations at time t clav,t ; where T dzq is the system scheduling cycle;

[0016] Establish the objective function of minimizing the output fluctuation of the power supply side Where min means taking the minimum value.

[0017] Optionally, the establishing of the minimum cost objective function O2 of the new energy abandonment specifically includes:

[0018] Establishing the lowest objective function for the cost of abandoned energy from new energy

[0019] where ρ wpp,t is the wind power price at time t; P′ wpp,t is the available wind power at time t; ρ pv,t is the photovoltaic power generation price at time t; P′ pv,t is the photovoltaic available power at time t.

[0020] Optionally, the constraint conditions for establishing O1 and O2 specifically include:

[0021] Establishing wind power generation constraints Where P wpp,i,t and P wpp,i,t-1 are the actual power generated by the i-th wind turbine at time t and time t-1 respectively; is the actual power upper limit of the i-th wind turbine; Z i,t and Zi,t-1 are the indicative state quantities of whether the unit equipment i is running at time t and time t-1 respectively; V wpp,up and are the lower and upper limits of the upward change rate of the wind turbine respectively; V wpp,dw and are the lower and upper limits of the downward change rate of the wind turbine respectively;

[0022] Establishing photovoltaic power generation constraints Where P pv,i,t and P pv,i,t-1 are the actual power generated by the i-th photovoltaic generator set at time t and time t-1 respectively; is the actual power upper limit of the i-th photovoltaic generator set; V pv,up and They are the lower and upper limits of the upward change rate of the photovoltaic power generation unit respectively; V pv,dw and They are the lower limit and upper limit of the downward change rate of the photovoltaic power generation unit respectively;

[0023] Constraints for establishing energy storage power stations in and are the actual power generated by the i-th energy storage power station at time t and time t-1 respectively; is the upper limit of energy storage of the i-th energy storage power station; and are the charging and discharging power of the i-th energy storage power station at time t; is the indicated state of the energy storage power station at time t, when it is 1, the energy storage power station is charging, and when it is 0, it is discharging; κ r is the unit charge of energy storage; e is the rated energy storage coefficient of the energy storage power station; and are the actual power generated by the i-th energy storage power station at the start and end times of each scheduling period respectively.

[0024] Optionally, based on O1, O2 and their constraints, a multi-objective grey wolf optimization algorithm is used to solve the optimal solution set of the output of new energy and energy storage power stations as the optimal solution of the new energy optimization layer, specifically including:

[0025] Taking O1 and O2 as the fitness functions of the multi-objective grey wolf optimization algorithm, taking wind power generation constraints, photovoltaic power generation constraints and energy storage power station constraints as the constraints of the multi-objective grey wolf optimization algorithm, the multi-objective grey wolf optimization algorithm is used for iterative optimization to obtain the optimal solution set of new energy and energy storage power station output as the optimal solution of the new energy optimization layer.

[0026] Optionally, the coal fuel quantity and the start-stop operation energy consumption of the thermal power unit are combined to establish the minimum energy consumption objective function O3 of the thermal power unit scheduling operation and its constraint conditions, which specifically include:

[0027] Using the formula Q p,i,t =a p +b p P i,t +c p P i,t 2 Calculate the coal consumption Q of the i-th thermal power unit at time t p,i,t ; where P i,t is the actual power generated by the i-th thermal power unit at time t; a P , b P and c P is the coal fuel quantity coefficient of thermal power units;

[0028] Establishing the minimum energy consumption objective function for dispatching thermal power units Where n is the number of thermal power units; Q i is the start-stop operation energy consumption of the i-th thermal power unit;

[0029] Set the regulation space and regulation rate constraints of thermal power units in P Lower,i and are the lower and upper limits of the regulation space of the i-th thermal power unit respectively; P i,t-1 is the actual power generated by the i-th thermal power unit at time t-1; and V up are the upper and lower limits of the upward adjustment rate of the i-th thermal power unit respectively; and V dw are the upper and lower limits of the downward adjustment rate of the i-th thermal power unit respectively;

[0030] Set constraints for starting and stopping thermal power units where t i,on is the startup and operation time of the i-th thermal power unit; and t i,on t i,on The upper and lower limits of t i,off is the shutdown duration of the i-th thermal power unit; and t i,off t i,off upper and lower limits.

[0031] Optionally, based on O3 and its constraints, an improved quantum particle swarm algorithm is used to solve the optimal solution set of the start and stop state, output state and operating energy consumption of the thermal power unit as the optimal solution of the thermal power unit optimization layer, specifically including:

[0032] Taking O3 as the fitness function of the improved quantum particle swarm algorithm, taking the regulation space and regulation rate constraints of the thermal power unit and the start and stop constraints of the thermal power unit as the constraints of the improved quantum particle swarm algorithm, the improved quantum particle swarm algorithm is used for iterative optimization to obtain the optimal solution set of the start and stop state, output state and operating energy consumption of the thermal power unit as the optimal solution of the optimization layer of the thermal power unit.

[0033] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for coordinated scheduling of multiple types of power sources.

[0034] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for coordinated scheduling and operation of multiple types of power sources.

[0035] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for coordinated scheduling and operation of multiple types of power supplies.

[0036] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0037] The present application provides a method, device, medium and product for coordinated dispatching and operating of multiple types of power sources. The new energy optimization layer is set by constructing objective functions O1 and O2 for minimizing power supply output fluctuation and minimizing new energy abandonment cost. The MOGWO algorithm is used to search and solve the multi-objective optimization problem of the new energy optimization layer, and the optimal solution set of the output of new energy and energy storage power stations is obtained, thereby improving the absorption capacity of multiple types of power systems for new energy power generation. The thermal power unit optimization layer uses the IQPSO algorithm to search for the problem of insufficient flexibility adjustment ability of thermal power units on the basis of the above-mentioned two-layer optimization of the new energy optimization layer, and takes the lowest energy consumption of thermal power unit scheduling operation as the objective function O3 for solving, so as to obtain the optimal solution set of the operation start-stop state, output state and operation energy consumption of the thermal power unit. The coordinated dispatching and operation of multiple types of power sources is guided based on each optimal solution set, which can significantly improve the stability, accuracy, flexibility and economy of the coordinated dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flowchart of a method for coordinated scheduling of multiple types of power sources in this application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] Thermal power generation will still play an active role on the power supply side in the short term. In order to cope with the high proportion of new energy generation and consumption, it is necessary to further optimize and improve in terms of building data models, deep load regulation, and flexibility regulation. In order to fully absorb wind power and photovoltaic power, the power system must have sufficient spare margins, and if necessary, it is necessary to plan corresponding energy storage equipment. At the same time, the new energy consumption process will inevitably cause fluctuations in the regulation frequency of thermal power units, and long-term low-load operation may occur, which is extremely unfavorable for the maintenance of thermal power units. Therefore, most units will consider adopting measures such as abandoning wind and abandoning light to ensure the safe and stable operation of the system during actual operation, but this will greatly reduce the absorption rate of new energy and make it difficult to cope with the general trend of high proportion of new energy access to the grid. Because new energy such as wind power and photovoltaic power are affected by environmental factors and have uncertainty, and their distribution is greatly affected by the region, the system needs to have a high coordinated scheduling optimization capability after accessing the power system. Therefore, it is necessary to deeply study the dynamic characteristics of thermal power generation, regulation capabilities, and the rate of change of new energy power generation during the scheduling process, and construct a coordinated scheduling strategy for multiple types of power sources to ensure the reasonable and efficient use of various types of power sources in the system. In this regard, the present application proposes a method, device, medium and product for coordinated scheduling of multiple types of power sources to improve the stability, accuracy, flexibility and economy of coordinated scheduling of multiple types of power sources in the power system.

[0042] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0043] In an exemplary embodiment, Figure 1 As shown, a method for coordinated scheduling and operation of multiple types of power sources is provided, including the following steps 1 to 7.

[0044] Step 1: Take the grid-connected new energy photovoltaic power generation, wind power and energy storage power stations as the fluctuation reduction objects on the power supply side, and establish the minimum output fluctuation objective function O1 on the power supply side.

[0045] The regulation capacity of thermal power units is analyzed. Under the load of electricity consumption, the power system takes the photovoltaic power generation, wind power and energy storage power station connected to the grid that can be flexibly charged and discharged as the fluctuation reduction objects on the power supply side. The power combination of the above three power sources is defined as the output power P that suppresses the fluctuation of the power supply side. cl Based on this, the minimum objective function O1 of power supply side output fluctuation is established as follows:

[0046]

[0047] in,

[0048]

[0049] P cl,t =P f,t -P wpp,t -P pv,t -P esd,t (3)

[0050] In the formula, P cl,t is the output power that suppresses fluctuations on the power supply side at time t; P f,t is the system power load at time t; P wpp,t P is the actual wind power generation at time t; pv,t is the actual power generated by photovoltaic power generation at time t; P esd,t is the actual power generated by the energy storage power station at time t; T dzq is the system scheduling period; P clav,t It is the average output power that suppresses fluctuations on the power supply side at time t; min means taking the minimum value.

[0051] Step 2: Establish the objective function O2 for minimizing the cost of energy abandonment of new energy.

[0052] Power forecasting is performed on new energy photovoltaic power generation and wind power generation to obtain power forecast data. Under the premise of ensuring the stability of the power system, the system needs to obtain the maximum absorption of new energy. Therefore, the cost of new energy abandonment is set to the lowest, so as to obtain the maximum absorption, and the objective function O2 of the lowest cost of new energy abandonment is established as follows:

[0053]

[0054] where ρ wpp,t is the wind power price at time t; P′ wpp,t is the available wind power at time t; ρ pv,t is the photovoltaic power generation price at time t; P′pv,t is the photovoltaic available power at time t.

[0055] Step 3: Establish constraints for O1 and O2, including wind power generation constraints, photovoltaic power generation constraints, and energy storage power station constraints.

[0056] The wind power generation constraints of O1 and O2 are:

[0057]

[0058] Where P wpp,i,t and P wpp,i,t-1 are the actual power generated by the i-th wind turbine at time t and time t-1 respectively; is the actual power upper limit of the i-th wind turbine; V wpp,up and are the lower and upper limits of the upward change rate of the wind turbine respectively; V wpp,dw and are the lower and upper limits of the downward change rate of the wind turbine respectively. i,t and Z i,t-1 are the indicative state quantities of whether the unit equipment i is running at time t and time t-1 respectively. In this application, for the convenience of expression, wind turbines, photovoltaic generators and thermal power units can all be represented by the subscript i. Therefore, the unit equipment i in the formula can refer to the i-th wind turbine, the i-th photovoltaic generator or the i-th thermal power unit, whichever is more suitable for the constraint object corresponding to the formula. Z i,t or Z i,t-1 When the value is 1, it indicates that the corresponding unit equipment i is in operation state, and when the value is 0, it indicates that it is in shutdown state.

[0059] The photovoltaic power generation constraints are:

[0060]

[0061] Where P pv,i,t and P pv,i,t-1 are the actual power generated by the i-th photovoltaic generator set at time t and time t-1 respectively; is the actual power upper limit of the i-th photovoltaic generator set; V pv,up and They are the lower and upper limits of the upward change rate of the photovoltaic power generation unit respectively; V pv,dw and They are the lower limit and upper limit of the downward change rate of the photovoltaic power generation unit respectively;

[0062] The constraints of the energy storage power station are:

[0063]

[0064] in and are the actual power generated by the i-th energy storage power station at time t and time t-1 respectively; is the upper limit of energy storage of the i-th energy storage power station; and are the charging and discharging power of the i-th energy storage power station at time t; is the indicated state of the energy storage power station at time t, when it is 1, the energy storage power station is charging, and when it is 0, it is discharging; κ r is the unit charge of energy storage; e is the rated energy storage coefficient of the energy storage power station; and are the actual power generated by the i-th energy storage power station at the start and end times of each scheduling period respectively.

[0065] Step 4: Based on O1, O2 and their constraints, the multi-objective grey wolf optimization algorithm is used to solve the optimal solution set of the output of new energy and energy storage power stations as the optimal solution of the new energy optimization layer.

[0066] The development of the Multi-Objective GreyWolfOptimizer (MOGWO) algorithm is mainly based on the characteristics of wolf packs, that is, when the algorithm is applied, it will divide the optimization particles into four levels like the wolf pack level. The bottom layer is the ordinary optimization particles, the third layer is the solution optimized compared to the bottom layer particles, the second layer is the suboptimal solution, and the first layer is the optimal solution. The MOGWO algorithm has the characteristics of strong convergence, simple parameters and easy implementation, which is suitable for the multi-objective optimization solution process. Therefore, this application uses it to solve the optimal solution set of the output of new energy and energy storage power stations in the new energy optimization layer. The specific optimization steps are as follows.

[0067] Step 4.1: Initialize the population. Set appropriate initial wolf population parameters and randomly generate wolf optimization individuals; the population size is 100, the number of iterations is 100, and the number of archives is 100. The grid mechanism parameters are set as follows: the grid expansion parameter alpha is 0.1, the number of grids in each dimension nGrid is 10, the supervisor selection pressure parameter beta is 4, and the additional (to be deleted) storage member selection pressure is 2.

[0068] Step 4.2: Initial optimization particle group generates fitness value based on the set initial wolf population parameters, using O1 and O2 as fitness functions, and the constraints are the above equations (5), (6), and (7).

[0069] Step 4.3: Divide the population into four levels: α, β, χ, and δ; define the first level α as the optimal solution, that is, the new energy abandonment cost O2 is the lowest; the second level β is the suboptimal solution, that is, the new energy abandonment cost is the second lowest; the third level χ is the third optimal solution, that is, the new energy abandonment cost is the third lowest; and the other population individuals are δ.

[0070] Step 4.4: Update the position of the optimal wolf population. The specific update formula is:

[0071] X d,t+1 =X d,t -A d |C d X d,t -X d,t | (8)

[0072] In the formula, X d,t and X d,t+1 A represents the position of α, β or χ in the d-dimensional space at time t and time t+1 respectively; d and C d Update parameters for optimization.

[0073]

[0074] In the formula, X g,d,t+1 represents the global position at time t+1; X α,d,t+1 , X β,d,t+1 , X χ,d,t+1 They represent the positions of α, β, and χ at time t+1 respectively.

[0075] Step 4.5: Update parameter A d and C d .

[0076]

[0077] Where r1 and r2 are random numbers in the range [0,1]. k is the coefficient of the kth iteration.

[0078] Step 4.6: Update the optimal position. Update the fitness values ​​of all groups, and re-divide the levels according to the results until the iteration conditions are met, and output the value of the corresponding optimal solution.

[0079] During the optimization process, the update rules for specific groups are:

[0080]

[0081] Where c and c max are the number of iterations and the maximum number of iterations respectively. P It is an intermediate parameter in the calculation process.P,d,t and X P,d,t+1 They represent the position of the level P in the d-dimensional space at time t and time t+1 respectively.

[0082] By inputting the new energy power prediction data and regulation rate data involved in O1, O2 and their constraints, and taking O1 and O2 as the fitness function of the MOGWO algorithm, taking the wind power generation constraint (5), photovoltaic power generation constraint (6) and energy storage power station constraint (7) as the constraints of the MOGWO algorithm, the MOGWO algorithm is iterated to find the optimal solution set with the maximum new energy consumption and the minimum power output, that is, the optimal solution set of new energy and energy storage power station output is obtained as the optimal solution of the new energy optimization layer. Among them, the new energy power prediction data (such as P wpp,i,t , P pv,i,t , ) and regulation rate data (e.g. V wpp,up , V wpp,dw , V pv,up , V pv,dw and ) can be predicted by the existing new energy power prediction model.

[0083] If the power supply side assumes that the adjustment rate and adjustment space of the thermal power unit and the power of the new energy are fixed when performing coordinated scheduling of multiple types of power sources, and simulates the power changes of the new energy units, the scheduling strategy obtained in this way will have problems of low accuracy or even system instability in practical applications. However, the relevant adjustment properties of the thermal power units, wind power units, and photovoltaic power generation units on the power supply side have dynamic characteristics, and the traditional assumed fixed constraints are difficult to meet the real-time changing scheduling system at this stage. Therefore, before performing the coordinated scheduling of multiple types of power sources, it is necessary to analyze the power supply side to provide reliable analysis data for subsequent coordinated scheduling. In this regard, before the coordinated scheduling of multiple types of power sources, this application analyzes the power supply of the thermal power units, wind power generation, and photovoltaic power generation on the power supply side according to the existing new energy power prediction model and the actual power identification data model of the thermal power units, and designs the corresponding algorithm program to obtain the adjustment space and adjustment rate of the thermal power units, the predicted power of the new energy, and the change rate of the new energy, so as to provide data and constraint restrictions for subsequent coordinated scheduling.

[0084] Step 5: Combine the coal fuel quantity and start-stop operation energy consumption of thermal power units to establish the minimum energy consumption objective function O3 and its constraints for scheduling operation of thermal power units.

[0085] Combining the low calorific value of coal fuel of thermal power units and the start-stop operation energy consumption of thermal power units, the minimum energy consumption objective function O3 of thermal power unit scheduling operation is established as follows:

[0086]

[0087] in,

[0088] Q p,i,t =a p +b p P i,t +c p P i,t 2 (13)

[0089] In the formula, Q p,i,t is the coal consumption of the i-th thermal power unit at time t; P i,t is the actual power generated by the i-th thermal power unit at time t; a P 、b P and c P is the coal fuel quantity coefficient of the thermal power unit. n is the number of thermal power units. i is the energy consumption of starting and stopping the i-th thermal power unit. i,t and Z i,t-1 They are the indicator status quantities of whether the i-th thermal power unit is running at time t and time t-1 respectively. When the value is 1, it means it is in the running (starting) state, and when the value is 0, it means it is in the stopping (shutdown) state.

[0090] Set the actual power and obtain the upper and lower limits of the change rate in the current state, and establish the regulation space and regulation rate constraints of the thermal power unit as follows:

[0091]

[0092] in P Lower,i and are the lower and upper limits of the regulation space of the i-th thermal power unit respectively; P i,t-1 is the actual power generated by the i-th thermal power unit at time t-1; and V up are the upper and lower limits of the upward adjustment rate of the i-th thermal power unit respectively; and V dw are the upper and lower limits of the downward adjustment rate of the i-th thermal power unit respectively.

[0093] The constraints for starting and stopping thermal power units are:

[0094]

[0095] where t i,on is the startup and operation time of the i-th thermal power unit; and t i,on t i,on The upper and lower limits of t i,off is the shutdown duration of the i-th thermal power unit; and t i,off t i,off upper and lower limits.

[0096] Step 6: Based on O3 and its constraints, the improved quantum particle swarm algorithm is used to solve the optimal solution set of the start and stop state, output state and operating energy consumption of the thermal power unit as the optimal solution of the optimization layer of the thermal power unit.

[0097] For the optimization layer of thermal power units, this application uses the improved quantum particle swarm optimization (IQPSO) algorithm for solution. According to the consumption of new energy in step 4, the existing thermal power unit actual power identification data model is used to calculate the thermal power unit power prediction data (for example, P i,t , P i,t-1 ), adjusting rate data (e.g. V up , V dw ) and conditioning spatial data (e.g. P Lower,i , ), and substitute them into formulas (12), (14), and (15) for calculation. The IQPSO algorithm is used, O3 is used as the fitness function, (14) and (15) are used as constraints, and the optimal solution set of the start-stop state, output state, and operating energy consumption of the thermal power unit is obtained through iterative optimization through the IQPSO algorithm, which is used as the optimal solution of the thermal power unit optimization layer.

[0098] The IQPSO algorithm optimization update rules are as follows:

[0099]

[0100] In the formula, G best is the global average optimal position, G num is the population size; X best,z and X z are the best historical position and updated position of the zth particle respectively; ε, is a random number between 0 and 1; φ and γ are the innovation coefficient and the expansion coefficient respectively; l best is the current optimal particle, zl and z max They are the current number of iterations and the upper limit of iterations respectively.

[0101] Improve the algorithm.

[0102]

[0103] Where, X q,z Update position for the zth generation of the qth dimension; η is a random number between 0 and 1; T dd It is an iteration cycle.

[0104]

[0105] In the formula, κ q,z is the zth generation position variable in the qth dimension; B s and T dd ' are the mutation iteration step and mutation cycle respectively. κ j Represents the position variable of the j-th generation particle.

[0106] The optimization layer of thermal power units takes the lowest energy consumption of thermal power units as the objective function, and uses the IQPSO algorithm to solve the optimal solution set of the new energy optimization layer. The scheduling simulation test is carried out through comparative experiments to verify the advantages of the multi-type power supply coordinated scheduling strategy in suppressing the fluctuation of unit output, improving the economic efficiency of scheduling operation and increasing the new energy consumption rate.

[0107] Step 7: Based on the optimal solution of the new energy optimization layer and the optimal solution of the thermal power unit optimization layer, guide the coordinated dispatching operation of multiple types of power sources; the multiple types of power sources include thermal power, wind power, photovoltaic power generation and energy storage power stations.

[0108] If the power supply side simplifies the constraints when conducting coordinated scheduling of multiple types of power sources, that is, the regulation rate and regulation space of the thermal power units and the power of new energy are assumed to be fixed, and the power changes of the new energy units are simulated, the scheduling strategy obtained in this way will have problems of low accuracy or even system instability in practical applications. Under the current general trend of carbon peak and carbon neutrality, new energy needs to be reasonably absorbed as much as possible without affecting the stability of the system when it is connected to the power grid for scheduling. Therefore, this application sets the new energy optimization layer based on the objective functions O1 and O2 mentioned above, which are the minimum output fluctuation on the power supply side and the lowest cost of new energy abandonment. The new energy optimization layer has two objective functions, namely, the minimum output fluctuation on the power supply side and the lowest cost of new energy abandonment. The MOGWO algorithm is used to find the best solution for the multi-objective optimization problem, and the optimal solution set for the optimal output of new energy and energy storage power stations is obtained, so as to further improve the absorption capacity of multiple types of power supply systems for new energy power generation.

[0109] At present, the main power generation units on the power supply side of China are still coal-fired thermal power units, and it is difficult to replace them with new energy, nuclear energy, hydropower and other energy sources in the short term. Therefore, the study of the reasonable call and economic call of thermal power units in the coordinated dispatch of multiple types of power sources will promote the goal of achieving carbon peak and carbon neutrality. Therefore, this application sets a thermal power unit optimization layer based on the goal of minimizing the energy consumption of thermal power unit dispatching operation. In order to solve the problem of insufficient flexibility adjustment ability of thermal power units, the thermal power unit optimization layer adopts the IQPSO algorithm to find the optimal solution for the start and stop status and unit output of the thermal power units on the basis of the above-mentioned two-layer optimization of the new energy optimization layer. Using the adjustment space and adjustment rate of the thermal power units studied and analyzed above as the optimization constraints, and taking the minimum energy consumption of thermal power unit dispatching operation as the objective function O3 for solution, the optimal solution set of the start and stop status, output status and total dispatching energy consumption (operating energy consumption) of the thermal power units can be obtained.

[0110] The invention provides a method for coordinated dispatching and operating multiple types of power sources, and studies the potential mining of coordinated supply guarantee of regulating resources such as multiple types of power sources and multiple energy storage, and studies the method for improving the coordinated supply guarantee capacity of "source-storage" with cross-regional regulating resources. The coordinated supply guarantee model of multiple types of power sources is divided into a power supply side analysis layer, a new energy optimization layer, and a thermal power unit optimization layer, and the objective functions are the minimum output fluctuation on the power supply side, the minimum cost of new energy abandonment, and the minimum energy consumption of thermal power unit scheduling operation. The model obtains the regulating capacity of the thermal power unit, the predicted power and the change rate of the new energy through the power supply side analysis layer; the new energy optimization layer mainly takes the minimum output fluctuation on the power supply side and the minimum cost of new energy abandonment as the objective function, and uses the MOGWO algorithm to solve the multi-objective function, and obtains the optimal solution set of the new energy optimization layer that meets the conditions after optimization and solution. Based on the optimal solution set of the new energy optimization layer, the thermal power unit optimization layer takes the minimum energy consumption of thermal power unit scheduling operation as the objective function, optimizes the start and stop and output of the thermal power unit, and solves it through the IQPSO algorithm. Finally, the effectiveness of the proposed multi-type power supply coordinated dispatching strategy is evaluated by comparing the results of the simulation example.

[0111] The three-layer multi-type power supply coordinated dispatching strategy with the power supply side analysis layer added in the present invention takes the minimum power supply side output fluctuation, the minimum new energy abandonment cost and the minimum thermal power unit dispatching and operating cost as the objective function, and coordinates the multi-type power supplies including wind, light, fire and storage. In order to improve the stability, accuracy, flexibility and economy of the coordinated dispatching of multi-type power supplies, it is necessary to analyze the power supply side before the coordinated dispatching of multi-type power supplies to provide reliable analysis data for the subsequent coordinated dispatching. Therefore, the purpose of the power supply side analysis layer is to analyze the thermal power units, wind power generation and photovoltaic power generation on the power supply side according to the existing model before the coordinated dispatching of multi-type power supplies, and design the corresponding algorithm program to obtain the regulation space and regulation rate of the thermal power units, the predicted power of new energy and the change rate of new energy, so as to provide data and constraint conditions for subsequent coordinated dispatching. The new energy optimization layer optimizes the new energy generation and energy storage charging and discharging with the minimum power supply side output fluctuation and the minimum new energy abandonment cost as the objective function, thereby optimizing the new energy consumption. The thermal power unit optimization layer further optimizes the unit combination and output state of the thermal power unit according to the optimal solution set of the new energy optimization layer.

[0112] The problem of coordinated dispatch of multiple types of power sources should not only meet the multi-objectives of coordinated dispatch to achieve the purpose of coordinated optimization of power generation by multiple power sources on the power supply side, but also further study the system's ability to absorb new energy and the system's flexible adjustment ability when the proportion of new energy continues to expand. Therefore, the coordinated dispatch strategy of multiple types of power sources is a complex nonlinear optimization problem with multiple optimization objectives, multiple constraints, and internal variables including both discrete switch variables and continuous variables. Although ordinary genetic algorithms and particle swarm algorithms can be directly solved for such problems, the results of the solutions are relatively poor in convergence, and it is difficult to obtain satisfactory optimization results. The solution method of the present application is to add a power supply side analysis layer on the basis of a two-layer optimization algorithm, construct a three-layer coordinated dispatch strategy, and combine the MOGWO algorithm with the IQPSO algorithm for joint solution, which can obtain satisfactory optimization results. Based on the optimal solution of the new energy optimization layer and the optimal solution of the thermal power unit optimization layer, the coordinated dispatch operation of multiple types of power sources (including thermal power, wind power, photovoltaic power generation and energy storage power station) can be guided, which can significantly improve the stability, accuracy, flexibility and economy of the coordinated dispatch of multiple types of power sources in the power system.

[0113] As mentioned above, this application constructs a three-layer collaborative scheduling strategy for multiple types of power sources, including thermal power, wind power, photovoltaic power generation, and energy storage power stations, and uses the MOGWO algorithm and the IQPSO algorithm for hierarchical solution optimization. The collaborative scheduling strategy is further simulated, and the simulation results of the three-layer scheduling optimization strategy with the power side analysis layer added and the two-layer scheduling optimization strategy without the power side analysis layer added are compared to verify the effectiveness of the collaborative scheduling strategy proposed in this application.

[0114] In an exemplary embodiment, the present application also provides a computer device, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface, and a communication interface. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the multi-type power supply collaborative scheduling operation method is implemented.

[0115] In an exemplary embodiment, the present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for coordinated scheduling and operation of multiple types of power sources is implemented.

[0116] In an exemplary embodiment, the present application also provides a computer program product, including a computer program, which implements the method for coordinated scheduling of multiple types of power supplies when executed by a processor.

[0117] It can be understood by a person of ordinary skill in the art that all or part of the processes in the above-mentioned embodiment method can be completed by hardware related to computer program instructions, and the computer program can be stored in a non-volatile computer-readable storage medium, and the computer program can include the process of the embodiment of the above-mentioned method when executed. Among them, any reference to memory or other media in each embodiment provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0118] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0119] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for coordinated dispatching of multiple types of power sources, characterized in that: include: The photovoltaic power generation, wind power and energy storage power stations connected to the grid are taken as the fluctuation reduction objects on the power supply side, and the minimum output fluctuation objective function O1 on the power supply side is established; Establish the minimum objective function O2 for the cost of abandoned energy from new energy sources; Establish constraints for O1 and O2, including wind power generation constraints, photovoltaic power generation constraints, and energy storage power station constraints; Based on O1, O2 and their constraints, a multi-objective grey wolf optimization algorithm is used to solve the optimal solution set of the output of new energy and energy storage power stations as the optimal solution of the new energy optimization layer; Combined with the coal fuel quantity and start-stop operation energy consumption of thermal power units, the minimum energy consumption objective function O3 and its constraint conditions for dispatching and operating thermal power units are established; Based on O3 and its constraints, an improved quantum particle swarm algorithm is used to solve the optimal solution set of the start-stop state, output state and operating energy consumption of the thermal power unit as the optimal solution of the optimization layer of the thermal power unit; Based on the optimal solution of the new energy optimization layer and the optimal solution of the thermal power unit optimization layer, the coordinated scheduling and operation of multiple types of power sources are guided; the multiple types of power sources include thermal power, wind power, photovoltaic power generation and energy storage power stations.

2. The method for coordinated dispatching of multiple types of power sources according to claim 1 is characterized in that: The photovoltaic power generation, wind power and energy storage power stations connected to the grid are taken as the fluctuation reduction objects on the power supply side, and the minimum output fluctuation objective function O1 on the power supply side is established, which specifically includes: Using formula P cl,t =P f,t -P wpp,t -P pv,t -P esd,t Calculate the output power P that suppresses fluctuations on the power supply side at time t cl,t ; where P f,t is the system power load at time t; P wpp,t P is the actual wind power generation at time t; pv,t is the actual power generated by photovoltaic power generation at time t; P esd,t is the actual power generated by the energy storage power station at time t; Using formula Calculate the average output power P that suppresses power supply fluctuations at time t clav,t ; where T dzq is the system scheduling cycle; Establish the objective function of minimizing the output fluctuation of the power supply side Where min means taking the minimum value.

3. The method for coordinated dispatching and operating multiple types of power sources according to claim 2 is characterized in that: The establishment of the minimum cost objective function O2 of new energy abandonment specifically includes: Establishing the lowest objective function for the cost of abandoned energy from new energy where ρ wpp,t is the wind power price at time t; P w ' pp,t is the available wind power at time t; ρ pv,t is the photovoltaic power generation price at time t; P p ' v,t is the photovoltaic available power at time t.

4. The method for coordinated dispatching and operating multiple types of power sources according to claim 3 is characterized in that: The constraints for establishing O1 and O2 specifically include: Establishing wind power generation constraints Where P wpp,i,t and P wpp,i,t-1 are the actual power generated by the i-th wind turbine at time t and time t-1 respectively; is the actual power upper limit of the i-th wind turbine; Z i,t and Z i,t-1 are the indicative state quantities of whether the unit equipment i is running at time t and time t-1 respectively; V wpp,up and are the lower and upper limits of the upward change rate of the wind turbine respectively; and are the lower and upper limits of the downward change rate of the wind turbine respectively; Establishing photovoltaic power generation constraints Where P pv,i,t and P pv,i,t-1 are the actual power generated by the i-th photovoltaic generator set at time t and time t-1 respectively; is the actual power upper limit of the i-th photovoltaic generator set; V pv,up and They are the lower and upper limits of the upward change rate of the photovoltaic power generation unit respectively; V pv,dw and They are the lower limit and upper limit of the downward change rate of the photovoltaic power generation unit respectively; Constraints for establishing energy storage power stations in and are the actual power generated by the i-th energy storage power station at time t and time t-1 respectively; is the upper limit of energy storage of the i-th energy storage power station; and are the charging and discharging power of the i-th energy storage power station at time t; is the indicated state of the energy storage power station at time t, when it is 1, the energy storage power station is charging, and when it is 0, it is discharging; κ r is the unit charge of energy storage; e is the rated energy storage coefficient of the energy storage power station; and are the actual power generated by the i-th energy storage power station at the start and end times of each scheduling period respectively.

5. The method for coordinated dispatching and operating multiple types of power sources according to claim 4 is characterized in that: Based on O1, O2 and their constraints, the multi-objective grey wolf optimization algorithm is used to solve the optimal solution set of the output of new energy and energy storage power stations as the optimal solution of the new energy optimization layer, specifically including: Taking O1 and O2 as the fitness functions of the multi-objective grey wolf optimization algorithm, taking wind power generation constraints, photovoltaic power generation constraints and energy storage power station constraints as the constraints of the multi-objective grey wolf optimization algorithm, the multi-objective grey wolf optimization algorithm is used for iterative optimization to obtain the optimal solution set of new energy and energy storage power station output as the optimal solution of the new energy optimization layer.

6. The method for coordinated dispatching and operating multiple types of power sources according to claim 5 is characterized in that: The coal fuel quantity and the start-stop operation energy consumption of the thermal power unit are combined to establish the minimum energy consumption objective function O3 of the thermal power unit scheduling operation and its constraint conditions, which specifically include: Using the formula Q p,i,t =a p +b p P i,t +c p P i,t 2 Calculate the coal consumption Q of the i-th thermal power unit at time t p,i,t ; where P i,t is the actual power generated by the i-th thermal power unit at time t; a P , b P and c P is the coal fuel quantity coefficient of thermal power units; Establishing the minimum energy consumption objective function for dispatching thermal power units Where n is the number of thermal power units; Q i is the start-stop operation energy consumption of the i-th thermal power unit; Set the regulation space and regulation rate constraints of thermal power units in P Lower,i and are the lower and upper limits of the regulation space of the i-th thermal power unit respectively; P i,t-1 is the actual power generated by the i-th thermal power unit at time t-1; and V up are the upper and lower limits of the upward adjustment rate of the i-th thermal power unit respectively; and V dw are the upper and lower limits of the downward adjustment rate of the i-th thermal power unit respectively; Set constraints for starting and stopping thermal power units where t i,on is the startup and operation time of the i-th thermal power unit; and t i,on t i,on The upper and lower limits of t i,off is the shutdown duration of the i-th thermal power unit; and t i,off t i,off upper and lower limits.

7. The method for coordinated dispatching and operating multiple types of power sources according to claim 6 is characterized in that: Based on O3 and its constraints, the improved quantum particle swarm algorithm is used to solve the optimal solution set of the start and stop state, output state and operating energy consumption of the thermal power unit as the optimal solution of the thermal power unit optimization layer, which specifically includes: Taking O3 as the fitness function of the improved quantum particle swarm algorithm, taking the regulation space and regulation rate constraints of the thermal power unit and the start and stop constraints of the thermal power unit as the constraints of the improved quantum particle swarm algorithm, the improved quantum particle swarm algorithm is used for iterative optimization to obtain the optimal solution set of the start and stop state, output state and operating energy consumption of the thermal power unit as the optimal solution of the optimization layer of the thermal power unit.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for coordinated scheduling and operation of multiple types of power sources as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for coordinated scheduling and operation of multiple types of power sources described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for coordinated scheduling and operation of multiple types of power sources described in any one of claims 1 to 7 is implemented.

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