Multi-energy complementary medium and long term scheduling method and system based on improved cuckoo optimization algorithm

Abstract

The invention discloses a multi-energy complementary medium and long term scheduling method and system based on an improved cuckoo optimization algorithm, and relates to the technical field of energy power generation. The problems that according to an existing multi-energy complementary power generation system dispatching method with hydropower as a main regulation energy form, a dynamic planning method or a basic optimization algorithm is often adopted based on research results of reservoir dispatching optimization, so that a feasible solution is difficult to efficiently find in complex constraint conditions, and a model is prone to falling into local optimum are solved. The method comprises the following steps: collecting characteristic data of each cascade power station, data of a photovoltaic power station and power generation characteristic data of a wind power plant, calculating historical / predicted values of runoff, irradiation resources and wind power resources in a cascade power station drainage basin in a period, and preprocessing the data; constructing a medium and long term scheduling model taking minimum output maximization and total revenue maximization as targets; and solving the medium and long term scheduling model according to an improved cuckoo optimization algorithm to obtain an optimal scheduling strategy. The method is applied to energy scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of energy generation, and in particular to a mid- and long-term scheduling method for multi-energy complementarity based on an improved cuckoo optimization algorithm. Background Art

[0002] With the continuous growth of global energy demand and the improvement of environmental protection awareness, the use of renewable energy has received more and more attention. As clean and renewable energy, hydropower, wind power and solar power are inexhaustible and are an important direction for future energy development. However, these energy sources are intermittent and unstable, which brings challenges to the stable operation of the power grid. In order to improve the peak load regulation and absorption capacity of the power grid, the multi-energy complementary power generation system came into being. It integrates different types of energy to achieve complementary utilization of energy and improve the stability and economy of the entire system.

[0003] The dispatching method of multi-energy complementary power generation system with hydropower as the main regulating energy form often adopts dynamic programming method or basic optimization algorithm based on the research results of reservoir dispatching optimization. However, the former may encounter "dimensionality curse" when dealing with complex systems and it is difficult to find the optimal solution. The latter often encounters the following problems when dealing with high-dimensional and complex optimization problems:

[0004] (1) It is difficult to efficiently find feasible solutions under complex constraints. After entering the feasible domain, constraints are prone to violation during the iteration process. For example, in a hydropower system, the reservoir water storage capacity, power generation, etc. may violate physical constraints. Constraint violation will cause the solution to fail to be implemented in actual applications or pose a safety hazard. At the same time, when the constraints are violated, the optimization process will lose its original directionality and effectiveness, and may even require retrospective adjustments, which greatly reduces the solution efficiency. For multi-energy complementary power generation systems, the effectiveness of the scheduling plan is directly related to the coordinated use of energy and the stability of the system. If constraint violation causes the scheduling plan to fail to meet actual needs or produces an unstable scheduling mode, it may affect the safe operation and economic scheduling goals of the entire power system.

[0005] (2) The model is prone to falling into local optimality and other problems. In medium- and long-term scheduling, the uncertainty and complexity of many factors that need to be considered put forward higher requirements for scheduling algorithms. Summary of the invention

[0006] The present invention aims at the problems that the existing multi-energy complementary power generation system scheduling method with hydropower as the main regulating energy form often adopts the dynamic programming method or uses the basic optimization algorithm based on the research results of reservoir scheduling optimization, which leads to the difficulty in efficiently finding feasible solutions under complex constraints and the model is prone to fall into local optimality. A multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm is proposed, and the method includes:

[0007] S1: Collect characteristic data of each cascade power station, photovoltaic power station data, wind farm power generation characteristic data, and historical / predicted values ​​of flow rate, irradiation resources, and wind resources in the basin of the cascade power station within the calculation period, and pre-process the data;

[0008] S2: Construct a medium- and long-term dispatch model with the goal of maximizing minimum output and overall benefits;

[0009] S3: Solve the medium- and long-term scheduling model based on the improved cuckoo optimization algorithm to obtain the optimal scheduling strategy.

[0010] Furthermore, a preferred embodiment is proposed, in which the characteristic data of each cascade power station in step S1 include: an upstream water level storage capacity curve, a downstream discharge water level curve, and a unit operation characteristic curve.

[0011] Furthermore, a preferred method is proposed, wherein step S2 comprises:

[0012]

[0013] Among them, TR is the total annual return, p i,t is the electricity price of the i-th power station in the t-th period, A i is the output coefficient of the i-th power station, Q i,t is the power generation flow of the i-th power station in the t-th period, H i,t is the average net water head of power generation of the ith power station in the tth period, T is the total number of calculation periods in a year, and n is the total number of cascade power stations.

[0014] Furthermore, a preferred method is proposed, in which the constraints of the medium- and long-term scheduling model in step S2 include: water balance constraints, reservoir water level constraints, reservoir discharge flow constraints, power station output constraints, output channel constraints, and non-negative constraints.

[0015] Furthermore, a preferred method is proposed, wherein the improved cuckoo optimization algorithm comprises: improving the traditional cuckoo search algorithm, introducing an elite strategy, Gaussian distribution, local search and adaptive adjustment mechanism.

[0016] Furthermore, a preferred embodiment is proposed, wherein step S5 comprises:

[0017] S51: Randomly generate the initial positions of cuckoo individuals in the population, involving n power plants in the medium- and long-term scheduling optimization, and 12 calculation cycles;

[0018] S52: Minimize the objective function and calculate the fitness value of each cuckoo individual;

[0019] S53: Generate a new cuckoo individual position through Levy flight and evaluate the fitness value of the new solution; if the fitness of the new solution is better than the original position, update the cuckoo position;

[0020] S54: Replace low-quality solutions with new random solutions;

[0021] S55: For the optimal solution of a single iteration, randomly replace a part of the bird's nests with the optimal bird's nests with probability pa / 4, take a smaller step size, and perform local search;

[0022] S56: For the optimal solution of a single iteration, randomly replace a part of the bird's nests with the optimal bird's nests with probability pa / 4, take a smaller step size, and perform local search;

[0023] S57: Keep the best bird nests at present;

[0024] S58: Repeat steps S51 to S57 until the maximum number of iterations is reached or the convergence condition is met.

[0025] Furthermore, a preferred method is proposed, in step S53, the Levi flight is:

[0026] x i (t+1)=x i (t)+αLevy(λ)

[0027] Among them, x i (t) is the position of the ith cuckoo in the tth generation; α is the step size scaling factor; Levy (λ) is the Levy distribution.

[0028] Based on the same inventive concept, the present invention also proposes a multi-energy complementary medium- and long-term scheduling system based on an improved cuckoo optimization algorithm, the system comprising:

[0029] The data acquisition unit is used to collect characteristic data of each cascade power station, photovoltaic power station data, wind farm power generation characteristic data, and historical / predicted values ​​of flow rate, irradiation resources, and wind resources in the basin of the cascade power station within the calculation period, and pre-process the data;

[0030] The dispatch model building unit is used to build a medium- and long-term dispatch model with the goal of maximizing minimum output and maximizing overall benefits;

[0031] The solving unit is used to solve the medium- and long-term scheduling model according to the improved cuckoo optimization algorithm to obtain the optimal scheduling strategy.

[0032] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a medium- and long-term scheduling method for multi-energy complementarity based on an improved cuckoo optimization algorithm as described in any one of the above items.

[0033] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm as described above are executed.

[0034] The present invention is beneficial in that:

[0035] The present invention improves the convergence speed and solution quality of the cuckoo optimization algorithm under complex constraints by improving the algorithm. The improved cuckoo optimization algorithm can better balance local search and global search, reduce the probability of falling into local optimum, and has a strong global search capability, and can find the global optimum or approximate optimal solution in the scheduling of multi-energy complementary systems.

[0036] Through the cuckoo optimization algorithm and the medium- and long-term scheduling model, the present invention can quickly handle the scheduling problems of various energy forms such as hydropower, wind power, photovoltaics, etc., while effectively considering various constraints (such as energy supply constraints, watershed water level constraints, equipment operation restrictions, etc.), and find a better scheduling strategy in a complex and changeable system. This method is more efficient than traditional dynamic programming and basic optimization algorithms, especially when facing diversified and dynamically changing energy inputs, and can provide efficient and flexible scheduling solutions.

[0037] Compared with traditional methods, the improved cuckoo optimization algorithm is more adaptable to the uncertainty of energy resources in multi-energy complementary power generation systems (such as fluctuations in wind speed and light), and can adjust the scheduling strategy in real time, thereby improving the overall stability and economy of the system.

[0038] The dispatching model of the present invention can realize the optimal dispatching of the multi-energy complementary power generation system by comprehensively considering the two goals of maximizing the minimum output and maximizing the overall benefit. Through the comprehensive optimization of this goal, it can not only ensure the stable operation of each power station, but also improve the benefit of the overall power generation system, and enhance the economy and sustainability of the system.

[0039] The present invention is applied to the field of energy dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a medium- and long-term scheduling method for multi-energy complementarity based on an improved cuckoo optimization algorithm as described in implementation mode 1;

[0041] Figure 2 This is a schematic diagram of the convergence of the algorithm described in Implementation Mode 11;

[0042] Figure 3 This is a schematic diagram of the medium- and long-term water level process of the reservoir described in Implementation Method 11. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0044] Implementation method 1, see Figure 1 This embodiment describes a medium- and long-term scheduling method for multi-energy complementarity based on an improved cuckoo optimization algorithm, the method comprising:

[0045] S1: Collect characteristic data of each cascade power station, photovoltaic power station data, wind farm power generation characteristic data, and historical / predicted values ​​of flow rate, irradiation resources, and wind resources in the basin of the cascade power station within the calculation period, and pre-process the data;

[0046] S2: Construct a medium- and long-term dispatch model with the goal of maximizing minimum output and overall benefits;

[0047] S3: Solve the medium- and long-term scheduling model based on the improved cuckoo optimization algorithm to obtain the optimal scheduling strategy.

[0048] Specifically: This implementation proposes a medium- and long-term scheduling method for multi-energy complementarity. First, the characteristic data of each cascade power station (including but not limited to the upstream water level storage capacity curve, the downstream discharge water level curve, the unit operation characteristic curve, etc.), photovoltaic power station, wind farm power generation characteristic data, and the historical / predicted values ​​of the flow rate, irradiation resources, and wind resources in the basin of the cascade power station during the calculation period are collected for data preprocessing; then, the intelligent algorithm is selected and improved, and the elite strategy, Gaussian distribution, local search and adaptive adjustment mechanism are introduced to improve the cuckoo algorithm, and the global search ability, convergence speed and consistency of the algorithm are improved by combining constraint penalties and constraint adjustments; then the objective function and boundary conditions are confirmed, and a medium- and long-term scheduling model is constructed; the optimal scheduling strategy is obtained by analyzing the Pareto frontier. This method can achieve medium- and long-term optimal scheduling and improve the scheduling efficiency and economy of the multi-energy complementary power generation system.

[0049] In this implementation, by improving the cuckoo optimization algorithm, its convergence speed and solution quality under complex constraints are improved. The improved cuckoo optimization algorithm can better balance local search and global search, reduce the probability of falling into local optimality, and has a strong global search capability, and can find the global optimal or approximate optimal solution in the scheduling of multi-energy complementary systems.

[0050] Through the cuckoo optimization algorithm and the medium- and long-term scheduling model, the method proposed in this embodiment can quickly handle the scheduling problems of various energy forms such as hydropower, wind power, photovoltaics, etc., while effectively considering various constraints (such as energy supply constraints, watershed water level constraints, equipment operation restrictions, etc.), and find a better scheduling strategy in a complex and changeable system. This method is more efficient than traditional dynamic programming and basic optimization algorithms, especially when facing diversified and dynamically changing energy inputs, and can provide efficient and flexible scheduling solutions.

[0051] Compared with traditional methods, the improved cuckoo optimization algorithm combined with the constructed medium- and long-term scheduling model can better adapt to the uncertainty of energy resources in multi-energy complementary power generation systems (such as fluctuations in wind speed, light, etc.), and can adjust the scheduling strategy in real time, thereby improving the overall stability and economy of the system.

[0052] The dispatch model proposed in this implementation mode can realize the optimal dispatch of the multi-energy complementary power generation system by comprehensively considering the two goals of maximizing the minimum output and maximizing the overall benefit. Through the comprehensive optimization of this goal, it can not only ensure the stable operation of each power station, but also improve the benefits of the overall power generation system and enhance the economy and sustainability of the system.

[0053] Implementation method 2. This implementation method is a further limitation of the multi-energy complementary medium- and long-term scheduling method based on the improved cuckoo optimization algorithm described in implementation method 1. The characteristic data of each cascade power station in step S1 include: upstream water level storage capacity curve, downstream discharge water level curve, and unit operation characteristic curve.

[0054] This embodiment is explained in conjunction with the first embodiment. In this embodiment, by considering the upstream water level storage capacity curve and the downstream discharge water level curve in detail, the hydraulic dispatching process of the reservoir can be simulated more accurately. The units of each cascade power station have different operating characteristics under different loads and hydrological conditions. Adjusting the dispatching plan based on these characteristics can make the power station more efficient and ensure the stability and optimal economy of the system.

[0055] By improving the cuckoo optimization algorithm, its ability to solve complex scheduling problems can be further enhanced. Especially in the medium- and long-term scheduling of multi-energy complementary systems, when it involves the coordination and optimization of multiple energy sources (such as hydropower, wind power, solar power, etc.), the improved algorithm can better handle the coupling relationship between different energy types and find the global optimal or near-optimal scheduling solution.

[0056] The multi-energy complementary system involves the joint dispatch of multiple energy sources, usually including different types of power generation equipment such as hydropower, wind power, solar power, and even thermal power. By comprehensively considering the power generation characteristics and demand changes of each energy source, this method can reasonably arrange the operation of each power station, ensure the coordination and complementarity between multiple energy sources in the medium and long term, and maximize the overall benefits of the system. For example, hydropower can provide rapid regulation capabilities when power supply is tight, while wind power and solar power can provide relatively stable power output during non-peak hours. By optimizing scheduling, the use of traditional fossil energy can be effectively reduced and environmental impact can be reduced.

[0057] This method can make accurate medium- and long-term scheduling plans based on the characteristic data of cascade power stations. This not only includes the current power generation capacity, but also takes into account possible hydrological changes, equipment maintenance, energy demand fluctuations and other factors. Therefore, this scheduling method can better adapt to different external environments (such as climate change, energy demand fluctuations, etc.) and ensure the flexibility and stability of the system.

[0058] Implementation method 3: This implementation method is a further limitation of the multi-energy complementary medium- and long-term scheduling method based on the improved cuckoo optimization algorithm described in implementation method 1. Step S2 includes:

[0059]

[0060] Among them, TR is the total annual return, p i,t is the electricity price of the i-th power station in the t-th period, A i is the output coefficient of the i-th power station, Q i,t is the power generation flow of the i-th power station in the t-th period, H i,t is the average net water head of power generation of the ith power station in the tth period, T is the total number of calculation periods in a year, and n is the total number of cascade power stations.

[0061] In the construction of the medium- and long-term dispatch model, the goals are clearly set as "maximizing the minimum output" and "maximizing the overall benefit". This clear optimization goal helps to balance the contradiction between the power system's power output and benefits in the complex dispatch process, thereby achieving efficient operation of the system and maximizing economic benefits. This method involves multiple parameters (such as electricity price, output coefficient, power generation flow, net water head, etc.), which are closely related to the operation of the power station. By comprehensively considering these factors, the power station can be dispatched more accurately, the utilization efficiency of power generation resources can be optimized, and inefficient power generation or waste of resources due to improper dispatch of some power stations can be avoided.

[0062] At the same time, the dispatching method described in this embodiment fully considers the characteristics of "multi-energy complementarity", that is, the complementarity between different types of energy (such as hydropower, electricity, etc.). By reasonably dispatching the power generation combination of different energy sources, the power generation load of each power station can be effectively balanced, and the overall stability and reliability of the system can be improved, especially in different time periods or different climatic conditions, to achieve a smooth transition of power generation.

[0063] By taking the "total number of time periods in a year" as an important parameter, the method can flexibly dispatch according to different time periods, taking into account the fluctuations in power demand and market electricity prices, and dynamically adjust the power generation mode of the power station. This flexible scheduling strategy can better cope with market changes and uncertainties, and improve the adaptability and response capabilities of the system. The improved cuckoo optimization algorithm can overcome the local optimal trap of traditional optimization algorithms and find the global optimal solution by introducing mutation operations and adaptive mechanisms. It can explore in a multi-dimensional and complex scheduling space, quickly converge to the optimal solution or a near-optimal solution, and has good robustness, adapting to power station systems of different scales and different operating conditions.

[0064] This method takes into account the synergy of cascade power stations in the model. By introducing the total number of cascade power stations, the scheduling between different power stations can be better coordinated, thereby improving the operating efficiency of the entire power system. This cascade scheduling method can effectively adjust the power generation load of each power station and avoid resource waste or excessive load that may occur when the power station operates alone.

[0065] Implementation method four. This implementation method is a further limitation of the multi-energy complementary medium- and long-term scheduling method based on the improved cuckoo optimization algorithm described in implementation method two. The constraints of the medium- and long-term scheduling model in step S2 include: water balance constraints, reservoir water level constraints, reservoir discharge flow constraints, power station output constraints, output channel constraints, and non-negative constraints.

[0066] The multi-energy complementary scheduling method involves different types of energy systems (such as hydropower, wind power, solar power, etc.). Through reasonable scheduling, the complementarity of various energy sources can be fully utilized. Specifically, when the output of a certain energy source (such as wind power or solar power) is low, it can be supplemented by other energy systems such as hydropower, thereby ensuring the overall stability of the system and the reliability of energy supply.

[0067] Different energy sources have greater volatility and uncertainty in power generation, especially wind and solar energy. Through multi-energy complementarity, the fluctuations of various energy sources can be effectively balanced and the reliability and dispatch efficiency of the power system can be improved.

[0068] The water balance constraint ensures that the dispatching of the hydropower station not only meets the power generation demand, but also ensures the rational use of water resources to avoid over-exploitation or waste of water resources. The reservoir water level constraint ensures that the reservoir water level is within a reasonable range, which not only meets the power generation demand, but also avoids the reduction of power generation capacity due to too low water level, or the risk of overflow due to too high water level. By limiting the discharge flow of the reservoir, the reservoir is prevented from causing environmental damage or downstream flood risks due to excessive discharge of water. At the same time, this constraint helps to achieve a reasonable allocation of ecological water. The power station output constraint ensures that each power station operates between its maximum power and minimum power, avoiding equipment damage and achieving stable power generation capacity output. The output channel constraint takes into account the limitations of the grid load and transmission capacity. The output channel constraint ensures that electricity can be transmitted within an effective range to avoid accidents caused by overloaded transmission lines. Through non-negative constraints, negative power generation or energy storage status is avoided, ensuring the feasibility and stability of the system during the dispatching process.

[0069] By improving the cuckoo optimization algorithm and optimizing the scheduling plan multiple times, it is possible to reasonably allocate energy demand in different time periods according to actual needs, which can take into account both short-term power generation needs and medium- and long-term resource allocation and system stability.

[0070] Implementation method five. This implementation method is a further limitation of the multi-energy complementary medium- and long-term scheduling method based on the improved cuckoo optimization algorithm described in implementation method one. The improved cuckoo optimization algorithm includes: improving the traditional cuckoo search algorithm, introducing elite strategy, Gaussian distribution, local search and adaptive adjustment mechanism.

[0071] In the traditional cuckoo search algorithm, the solution is updated by random perturbation, but some potential excellent solutions may be missed. After the introduction of the elite strategy, the algorithm can ensure the stability of the optimal solution. Even in subsequent iterations, the search process can still rely on the current optimal solution to promote optimization. This helps to improve the effect of multi-energy complementary medium- and long-term scheduling, especially in the optimization of complex energy systems, and can maintain a good global search capability.

[0072] In the traditional cuckoo optimization algorithm, the search for solutions usually relies on random perturbations (such as uniform distribution). The introduction of Gaussian distribution can make the search process more focused on more effective areas of the solution space. Gaussian distribution has a high local concentration, which can more accurately explore high-quality areas in the solution space during the search process, reduce invalid searches, and avoid excessive randomness. This can effectively improve the optimization efficiency in multi-energy complementary scheduling, especially when it involves coordinated scheduling of multiple energy systems.

[0073] The introduction of the local search mechanism can help the algorithm to conduct in-depth exploration of certain local areas in the solution space during the search process. The cuckoo optimization algorithm itself can find a good initial solution through global search, but when faced with complex scheduling problems, local optimization becomes particularly important. After finding a better solution, the local search mechanism can locally refine the solution to further improve the quality of the solution. In this way, it can ensure that the scheduling plan is more efficiently optimized in different time periods or regions, so as to better meet the specific needs of multi-energy complementary scheduling.

[0074] The adaptive adjustment mechanism can dynamically adjust the algorithm's parameters, such as mutation rate, step size, etc., according to the actual situation during the optimization process. This means that the algorithm can flexibly adjust the exploration strategy according to the progress of the current search in order to better adapt to the optimization needs of different stages. For example, a larger search step size may be required in the early stages to quickly explore a large range of solution spaces, while when approaching the optimal solution, a smaller step size can help fine-tune and avoid premature stagnation in the quality of the solution. Through this adaptive adjustment, the algorithm can converge to the optimal solution more efficiently, improving scheduling efficiency and solution quality.

[0075] Implementation method 6: This implementation method is a further limitation of the multi-energy complementary medium- and long-term scheduling method based on the improved cuckoo optimization algorithm described in implementation method 5. Step S5 includes:

[0076] S51: Randomly generate the initial positions of cuckoo individuals in the population, involving n power plants in the medium- and long-term scheduling optimization, and 12 calculation cycles;

[0077] S52: Minimize the objective function and calculate the fitness value of each cuckoo individual;

[0078] S53: Generate a new cuckoo individual position through Levy flight and evaluate the fitness value of the new solution; if the fitness of the new solution is better than the original position, update the cuckoo position;

[0079] S54: Replace low-quality solutions with new random solutions;

[0080] S55: For the optimal solution of a single iteration, randomly replace a part of the bird's nests with the optimal bird's nests with probability pa / 4, take a smaller step size, and perform local search;

[0081] S56: For the optimal solution of a single iteration, randomly replace a part of the bird's nests with the optimal bird's nests with probability pa / 4, take a smaller step size, and perform local search;

[0082] S57: Keep the best bird nests at present;

[0083] S58: Repeat steps S51 to S57 until the maximum number of iterations is reached or the convergence condition is met.

[0084] Implementation method 7: This implementation method is a further limitation of the multi-energy complementary medium- and long-term scheduling method based on the improved cuckoo optimization algorithm described in implementation method 5. The Levy flight in step S53 is:

[0085] x i (t+1)=x i (t)+αLevy(λ)

[0086] Among them, x i (t) is the position of the ith cuckoo in the tth generation; α is the step size scaling factor; Levy (λ) is the Levy distribution.

[0087] Implementation 8: A multi-energy complementary medium- and long-term scheduling system based on an improved cuckoo optimization algorithm described in this implementation includes:

[0088] The data acquisition unit is used to collect characteristic data of each cascade power station, photovoltaic power station data, wind farm power generation characteristic data, and historical / predicted values ​​of flow rate, irradiation resources, and wind resources in the basin of the cascade power station within the calculation period, and pre-process the data;

[0089] The dispatch model building unit is used to build a medium- and long-term dispatch model with the goal of maximizing minimum output and maximizing overall benefits;

[0090] The solving unit is used to solve the medium- and long-term scheduling model according to the improved cuckoo optimization algorithm to obtain the optimal scheduling strategy.

[0091] Embodiment 9: A computer device comprises a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes a medium- and long-term scheduling method for multi-energy complementarity based on an improved cuckoo optimization algorithm as described in any one of embodiments 1 to 7.

[0092] Implementation method ten, a computer-readable storage medium described in this implementation method, on which a computer program is stored, and when the computer program is executed by a processor, the steps of a multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm as described in any one of implementation methods one to seven are executed.

[0093] Implementation Method 11: See Figure 2 and Figure 3 This embodiment provides a specific example of a medium- and long-term scheduling method for multi-energy complementarity based on an improved cuckoo optimization algorithm described in the first embodiment, and is also used to explain the second to seventh embodiments, specifically:

[0094] Taking a water-wind-solar complementary system as the research object, relevant characteristic data were collected and processed to obtain the upstream water level-storage capacity curve, downstream flow-water level curve, unit output limit line, electricity price in each time period, internal diameter flow in each calculation period, interval flow, average photovoltaic output, average wind power output and other data of the cascade hydropower station reservoir in the system.

[0095] Construct a medium- and long-term dispatch model with the goal of maximizing minimum output and overall benefits. The model does not consider the uncertainty of the power generation side in the medium and long term, and considers multiple constraints such as output channel restrictions, water level dispatch restrictions, water balance, downstream flow, unit output restrictions, ecological units and ecological flow.

[0096] 1) Objective function 1: The output of the multi-energy complementary system during the minimum output period is as large as possible, that is, the minimum output is maximized. The effect of this goal is to provide the power grid with the largest possible uniform and reliable output, while ensuring the power generation of the complementary system during the dry season.

[0097]

[0098] Where: NP is the minimum output (MW) of the entire multi-energy complementary system; A i is the output coefficient of the ith power station (derived from the unit efficiency curve, the same below); Q i,t is the power generation flow of the i-th power station in the t-th period (m 3 / s); H i,t is the average net water head of the i-th power station in the t-th period (m); T is the total number of calculation periods in a year (the calculation period is month, T = 12); n is the total number of cascade power stations; N S Provide power for photovoltaic power station; N w Providing power for wind power stations.

[0099] 2) Objective function 2: The multi-energy complementary system gives priority to ensuring the output of new energy units (photovoltaic and wind power), that is, the abandonment of wind and solar power is minimized.

[0100]

[0101] Where: P u,t is the unused power of the entire multi-energy complementary system (MW); Ps u,t Pw is the unused theoretical photovoltaic power generation power (MW); u,t It is the theoretical power generation capacity of unused wind power (MW).

[0102] 3) Objective function 3: The annual benefit of the multi-energy complementary system is maximized.

[0103]

[0104] Among them: TR is the total annual return; p i,t is the electricity price of the i-th power station in the t-th period.

[0105] 4) Constraint 1: Water balance constraint

[0106]

[0107] Where: V i,t+1 is the initial reservoir storage capacity of the ith power station in the t+1th period (m 3 );V i,t is the initial reservoir storage capacity of the ith power station in the tth period (m 3 );q i,t is the inflow flow of the ith power station in the tth period (m 3 / s); Q i,t is the power generation flow of the i-th power station in the t-th period (m 3 / s); S i,t is the water discharge flow of the ith power station in the tth period (m 3 / s); i,t is the other water consumption of the i-th power station in the t-th period (m 3 / s), in this example, since there is no other water demand, i,t The default value is ecological traffic Qst i,t , and the unified value is the ecological flow standard value;

[0108] 5) Constraint 2: Reservoir water level constraint, meeting water level scheduling requirements

[0109]

[0110] in: Z i,t is the lower limit of the water level at the beginning of time period t at power station i (m); is the upper limit of the water level at the beginning of time period t at power station i (m); Z i,t is the reservoir water level of power station i at the beginning of period t (m).

[0111] 6) Constraint 3: Reservoir discharge flow constraint

[0112]

[0113] in: is the minimum discharge flow rate (m 3 / s), in this example, the ecological flow rate is taken as the lower limit; is the discharge flow of the ith power station in the tth period (m 3 / s); is the maximum allowable discharge flow of the ith power station in the tth period (m 3 / s).

[0114] 7) Constraint 4: Power station output constraint

[0115]

[0116] Where: N i,min is the minimum output allowed by the ith power station (MW, depending on the type and characteristics of the turbine); N i,max is the maximum output of the ith power station (MW).

[0117] 8) Constraint 5: Output channel constraint. Each output channel has a maximum output limit.

[0118] 9) Constraint 6: Non-negative constraint, that is, all variables are greater than or equal to 0.

[0119] 10) Equivalence and simplification of objective function: In this example calculation, since photovoltaic and wind power should be prioritized and the abandonment of photovoltaic and wind power should be zero, objective function 2 is converted into constraint conditions. Objective function 1 can be converted into a constraint condition by taking this objective, and the water level of each regulating power station is limited to be above the corresponding water level of the guaranteed output. Since the expected wind and solar abandonment rate is zero, objective function 3 can be equivalently converted into the following formula (this calculation method is also applicable to multiple objectives. In this example, the optimization problem is simplified to a single-objective optimization problem):

[0120]

[0121] The traditional cuckoo search algorithm is improved by introducing elite strategy, Gaussian distribution, local search and adaptive adjustment mechanism to improve the global search capability and convergence speed of the algorithm.

[0122] The elite strategy selects the best cuckoo individuals for targeted search, the local search performs enhanced search for the best individuals, and the adaptive adjustment mechanism dynamically adjusts the search parameters according to the iterative process.

[0123] The improved cuckoo optimization algorithm is used to solve the medium- and long-term scheduling model and obtain the optimal scheduling strategy. Figure 2 As shown:

[0124] 1) Initialization: Randomly generate the initial positions (solutions) of the cuckoo individuals in the population. This medium- and long-term scheduling optimization involves n power plants and 12 calculation cycles.

[0125]

[0126] 2) Fitness evaluation: Minimize the objective function and calculate the fitness value (objective function value) of each cuckoo individual.

[0127] 3) Generate new solution: Generate new cuckoo individual positions through Lévy flight and evaluate the fitness value of the new solution. If the fitness of the new solution is better than the original position, update the cuckoo position.

[0128] Levy flight is a random walk with the characteristics of long jumps and short steps. Its mathematical model is:

[0129] xi(t+1)=xi(t)+αLevy(λ)

[0130] Where xi(t) is the position of the ith cuckoo in the tth generation; α is the step size scaling factor; Levy(λ) is the Levy distribution, usually expressed as Levy(λ)u=t-λ(1<λ≤3). The Levy distribution can be generated by the following formula:

[0131] Levy(λ)=|v|1 / βu

[0132] Among them, uN(0,σu2); vN(0,σv2); σu=(Γ((1+β) / 2)β2(β-1) / 2Γ(1+β)sin(πβ / 2))1 / β.

[0133] 4) Nest replacement: replace low-quality solutions with new random solutions.

[0134] 5) Local search: For the optimal solution of a single iteration, randomly replace a part of the bird's nest with the optimal bird's nest with probability pa / 4, take a smaller step size, perform local search, and improve iteration efficiency:

[0135] α′=α / 4

[0136] 6) Bird nest update: Randomly replace a portion of the bird nest locations with probability pa to increase the diversity of the population.

[0137] 7) Elite Retention: Retain the best (nelite) bird nests.

[0138] 8) Iterative update: Repeat the above steps until the maximum number of iterations is reached or the convergence condition is met.

[0139] 9) Get the result.

[0140] Figure 2 For the convergence of the algorithm, the algorithm can quickly enter the feasible domain and obtain the optimal solution with fewer iterations. Figure 3 The long-term water level process of Longtou Reservoir is solved. The blue line is the optimized water level, and the yellow line is the water level scheduling to ensure the output curve.

[0141] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A medium- and long-term scheduling method for multi-energy complementarity based on an improved cuckoo optimization algorithm, characterized in that: The method comprises: S1: Collect characteristic data of each cascade power station, photovoltaic power station data, wind farm power generation characteristic data, and historical / predicted values ​​of flow rate, irradiation resources, and wind resources in the basin of the cascade power station within the calculation period, and pre-process the data; S2: Construct a medium- and long-term dispatch model with the goal of maximizing minimum output and overall benefits; S3: Solve the medium- and long-term scheduling model based on the improved cuckoo optimization algorithm to obtain the optimal scheduling strategy.

2. According to claim 1, a multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm is characterized in that: The characteristic data of each cascade power station in step S1 include: an upstream water level storage capacity curve, a downstream discharge water level curve, and a unit operation characteristic curve.

3. According to claim 1, a multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm is characterized in that: The step S2 comprises: Among them, TR is the total annual return, p i,t is the electricity price of the ith power station in the tth period, A i is the output coefficient of the i-th power station, Q i,t is the power generation flow of the i-th power station in the t-th period, H i,t is the average net water head of power generation of the ith power station in the tth period, T is the total number of calculation periods in a year, and n is the total number of cascade power stations.

4. According to claim 2, a multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm is characterized in that: The constraints of the medium- and long-term scheduling model in step S2 include: water balance constraints, reservoir water level constraints, reservoir discharge flow constraints, power station output constraints, output channel constraints, and non-negative constraints.

5. The mid- and long-term scheduling method for multi-energy complementarity based on the improved cuckoo optimization algorithm according to claim 1 is characterized in that: The improved cuckoo optimization algorithm includes: improving the traditional cuckoo search algorithm, introducing elite strategy, Gaussian distribution, local search and adaptive adjustment mechanism.

6. The mid- and long-term scheduling method for multi-energy complementarity based on the improved cuckoo optimization algorithm according to claim 5 is characterized in that: The step S5 comprises: S51: Randomly generate the initial positions of cuckoo individuals in the population, involving n power plants in the medium- and long-term scheduling optimization, and 12 calculation cycles; S52: Minimize the objective function and calculate the fitness value of each cuckoo individual; S53: Generate a new cuckoo individual position through Levy flight and evaluate the fitness value of the new solution; if the fitness of the new solution is better than the original position, update the cuckoo position; S54: Replace low-quality solutions with new random solutions; S55: For the optimal solution of a single iteration, randomly replace a part of the bird's nests with the optimal bird's nests with probability pa / 4, take a smaller step size, and perform local search; S56: For the optimal solution of a single iteration, randomly replace a part of the bird's nests with the optimal bird's nests with probability pa / 4, take a smaller step size, and perform local search; S57: Keep the best bird nests at present; S58: Repeat steps S51 to S57 until the maximum number of iterations is reached or the convergence condition is met.

7. The method for medium- and long-term scheduling of multi-energy complementarity based on improved cuckoo optimization algorithm according to claim 5 is characterized in that: In step S53, Levi's flight is: x i (t+1)=x i (t)+αLevy(λ) Among them, x i (t) is the position of the ith cuckoo in the tth generation; α is the step size scaling factor; Levy (λ) is the Levy distribution.

8. A multi-energy complementary medium- and long-term scheduling system based on an improved cuckoo optimization algorithm, characterized in that: The system comprises: The data acquisition unit is used to collect characteristic data of each cascade power station, photovoltaic power station data, wind farm power generation characteristic data, and historical / predicted values ​​of flow rate, irradiation resources, and wind resources in the basin of the cascade power station within the calculation period, and pre-process the data; The dispatch model building unit is used to build a medium- and long-term dispatch model with the goal of maximizing minimum output and maximizing overall benefits; The solving unit is used to solve the medium- and long-term scheduling model according to the improved cuckoo optimization algorithm to obtain the optimal scheduling strategy.

9. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of a multi-energy complementary medium- and long-term scheduling method based on an improved cuckoo optimization algorithm as described in any one of claims 1 to 7.

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