Multi-energy capacity configuration planning method for power system of wind, light and fire storage energy large base

Abstract

The invention relates to the technical field of electric power system planning, and provides a multi-energy capacity configuration planning method for a wind-light-fire storage energy large-base electric power system, which comprises the following steps of: taking the minimum construction and operation and maintenance cost, the minimum electricity abandoning rate cost under the condition of keeping the thermal power starting hours and the maximum power generation income of an energy large base as targets; respectively establishing three corresponding sub-objective functions; unified dimension conversion processing is carried out on the three sub-objective functions according to economical efficiency, and then a single objective function is obtained through conversion; based on the multi-energy complementary characteristics, a preset operation strategy, a preset constraint condition and a single objective function, constructing a multi-energy capacity optimal configuration model; and searching and solving the optimal fan capacity, photovoltaic array capacity, thermal power capacity and energy storage battery capacity of the multi-energy capacity optimal configuration model through a genetic algorithm on the premise that the minimum thermal power utilization hours are fixed. According to the invention, regional resource endowment can be exerted, the consumption level of new energy power generation is improved, and the operation income is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage base power system. Background Art

[0002] The intermittent, random and volatile characteristics of wind power, photovoltaic and other new energy sources have led to huge challenges to the safe and stable operation of the power grid due to the large-scale new energy grid connection. At the same time, due to the differences in the distribution of wind and solar resources and the spatial differences in load centers, the uncontrollability of wind and solar output, and the insufficient peak-shaving capacity of the system, new energy power abandonment has occurred. In order to achieve the goal of new energy development, in the future, when planning and constructing large-scale new energy transmission bases, we will rely on multi-energy complementary technologies to improve the coordination and friendliness between new energy bases and power grids, solve the problem of new energy power abandonment, and achieve efficient use of new energy.

[0003] Current research focuses on the complementary operation modes of multiple energy sources such as wind, solar, and coal to improve the comprehensive utilization efficiency of energy. Through optimized configuration, these projects can effectively solve the problems of wind abandonment, solar abandonment, and power rationing, promote the local consumption of renewable energy, achieve a stable supply of electricity, and improve the overall utilization efficiency of energy. At present, some studies are focusing on the coupling mechanism of wind, solar, thermal, and storage integration, capacity configuration optimization methods, construction and operation costs, and planning models for large power bases and regional power grids to achieve reliable operation and gradual promotion and application of wind, solar, thermal, and storage integration projects. Some studies have established an energy model for a wind, solar, and storage multi-energy complementary energy system, taking into account the economy and environmental protection of the system operation, proposing the goal of minimizing the system's comprehensive cost and carbon emissions, constructing a Pareto optimal solution set for multi-objective problems, and using a commercial solver of linear programming software to optimize the system.

[0004] The above studies generally take economic benefits as their starting point, without giving much consideration to reliability and grid flexibility support capabilities. Summary of the invention

[0005] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage base power system.

[0006] To achieve the above objectives, the present invention provides a multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage power system, comprising:

[0007] The cost of curtailment rate D is calculated by minimizing the construction and operation cost C and maintaining the number of hours of thermal power generation. loss Minimum, large energy base power generation income f pThe maximum is the goal, and three corresponding sub-objective functions are established respectively; the three sub-objective functions are converted into a unified dimension according to economic efficiency, and then converted into a single objective function min f=λ1C+λ2D loss -λ3f p ; Among them, λ1, λ2, and λ3 are the weight coefficients of cost, power abandonment rate, and operating income of large energy bases, respectively, λ1+λ2+λ3=1;

[0008] Based on the multi-energy complementary characteristics and the preset operation strategy, as well as the preset constraints and the single objective function, a multi-energy capacity optimization configuration model is constructed; under the premise that the minimum utilization hours of thermal power remain unchanged, the optimal wind turbine capacity, photovoltaic array capacity, thermal power capacity and energy storage battery capacity of the multi-energy capacity optimization configuration model are searched and solved through a genetic algorithm.

[0009] According to one aspect of the present invention, the preset operation strategy includes:

[0010] With the goal of maximizing wind and solar output, when thermal power and energy storage are required to participate in regulation, the output is in the order of thermal power and energy storage:

[0011] Keep the operating hours of thermal power generation greater than the minimum economic hours;

[0012] When energy storage resources are sufficient, energy storage regulation is used first; when energy storage resources are insufficient, thermal power regulation is used;

[0013] Priority should be given to deep peak-shaving measures, and units should be started and stopped when regulation resources are insufficient.

[0014] According to one aspect of the present invention, with the goal of minimizing the construction and operation and maintenance cost C, the corresponding sub-objective function is established as follows:

[0015] The calculation formula for construction and operation and maintenance cost C and the capacity of wind power, photovoltaic power, thermal power and energy storage is:

[0016] C=γ w S w +γ pv S pv +γ s S s +γ t S t ;

[0017] Among them, S w , S pv , S s , S t are the capacities of wind power, photovoltaic power, energy storage, and thermal power, respectively, w , γ pv , γ s , γ tThey are the construction and operation and maintenance cost coefficients of wind power, photovoltaic power, energy storage and thermal power respectively.

[0018] According to one aspect of the present invention, the power abandonment rate cost D is maintained at the thermal power operating hours. loss Minimum is the goal, and the corresponding sub-objective function is established as:

[0019] The cost of curtailment rate under the number of hours of thermal power generation operation is D loss The calculation includes: when the power abandonment rate is lower than the power abandonment rate limit, the value is the grid-connected power price * power abandonment loss; when it is higher than the power abandonment rate limit, the value is the grid-connected power price * power abandonment loss + higher power abandonment rate * power generation * penalty coefficient:

[0020]

[0021] Among them, σ is the power abandonment rate, μ0 is the upper limit of the power abandonment rate, and ε is the penalty coefficient for exceeding the power abandonment rate; P total is the total power generation of wind power or photovoltaic power in a certain period of time; η1 is the time-of-use electricity price; T is the period of time;

[0022] The formula for calculating the power abandonment rate is as follows:

[0023]

[0024] Where: σ is the power abandonment rate, P pre,t is the theoretical power generation of wind power and photovoltaic power at time t, P m,t is the actual power generation of wind power and photovoltaic power at time t.

[0025] According to one aspect of the present invention, the power generation income of a large energy base is f p The maximum is the goal, and the corresponding sub-objective function is established as:

[0026] f p =f n +f s +f t ;

[0027] Among them, f n Taking into account the green carbon benefits of wind power and photovoltaic power generation for energy bases, s In order to consider the energy storage operation benefits and costs over the entire life cycle, f t The operating income of thermal power plants taking into account carbon costs;

[0028]

[0029] Among them, η1 is the time-of-use electricity price; η2 is the green carbon benefit factor; T is the cycle period; P w,t and P pv,t are the actual outputs of wind power and photovoltaic power generation at time t respectively;

[0030]

[0031] Among them, η1 is the time-of-use electricity price, δ is the converted energy storage life cycle cost sharing coefficient, P sc,t With P sd,t are the energy storage charging and discharging power respectively; η c With η d are the energy storage charging and discharging efficiencies respectively; s A collection of energy storage devices;

[0032]

[0033] Among them, P gi,t is the output power of thermal power unit i at time t, η3 is the carbon emission cost factor; f(P gi,t ) is the fuel cost of thermal power units; g(P gi,t ) is the ramp cost of thermal power units; η1 is the time-of-use electricity price; ψ t A collection of thermal power units.

[0034] According to one aspect of the present invention, the preset constraint conditions include:

[0035] (1) Power balance constraints and battery state of charge constraints:

[0036] The sum of the output of wind power, photovoltaic power, thermal power and energy storage system is equal to the real-time load

[0037] P gi,t +P w,t +P pv,t +η d P d,t =η d P ld,t ;

[0038] Among them, P gi,t is the output power of the thermal power unit at time t; P w,t and P pv,t are the actual outputs of wind power and photovoltaic power generation at time t; P d,t is the discharge power of the energy storage system; η d is the discharge efficiency of the energy storage system; P ld,t is the load at time t;

[0039] (2) Sending channel restrictions:

[0040] Capacity limits for key outbound channels

[0041] 0≤P trans,t ≤P trans,max,t ;

[0042] Among them, P trans,t is the capacity of the key sending channel in period t, P trans,max,t is the power limit of the key transmission channel in period t;

[0043] (3) Thermal power output constraints:

[0044] P tmin,i ≤P i,t ≤P tmax,i ;

[0045] ΔP ut ≤U h ;

[0046] ΔP dt ≤D h ;

[0047] Among them, U h , D h are the rising rate and falling rate limits of the output of a single thermal power unit, ΔP ut , ΔP dt are the rising rate and falling rate of the output of a single unit, P tmin,i is the minimum allowable output of a single thermal power unit, P tmax,i The maximum permissible output of a single thermal power unit;

[0048] (4) Energy storage output and state of charge constraints:

[0049] P smin,i ≤P i,s ≤P smax,i ;

[0050] ΔP us ≤U hs ;

[0051] ΔP ds ≤D hs ;

[0052] 0.20≤SOC≤0.9;

[0053] Among them, U hs , D hs are the rising rate and falling rate limits of the energy storage unit output, ΔP us , ΔP ds are the rising rate and falling rate of the output of the energy storage unit, P smin,i is the minimum allowable output of the energy storage unit, P smax,i is the maximum allowable output of the energy storage unit, and SOC is the state of charge of the energy storage unit.

[0054] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a multi-energy capacity configuration planning system for a large-scale wind, solar, thermal and energy storage power system, comprising:

[0055] The single objective function acquisition module is to minimize the construction and operation and maintenance costs C and maintain the power abandonment rate cost D under the number of hours of thermal power operation. loss Minimum, large energy base power generation income f p The maximum is the goal, and three corresponding sub-objective functions are established respectively; the three sub-objective functions are converted into a unified dimension according to economic efficiency, and then converted into a single objective function min f=λ1C+λ2D loss -λ3f p ; Among them, λ1, λ2, and λ3 are the weight coefficients of cost, power abandonment rate, and operating income of large energy bases, respectively, λ1+λ2+λ3=1;

[0056] The multi-energy capacity configuration module constructs a multi-energy capacity optimization configuration model based on the multi-energy complementary characteristics and preset operation strategies, as well as preset constraints and the single objective function; under the premise that the minimum utilization hours of thermal power remain unchanged, the optimal wind turbine capacity, photovoltaic array capacity, thermal power capacity and energy storage battery capacity of the multi-energy capacity optimization configuration model are searched and solved through a genetic algorithm.

[0057] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and storage energy base power system is implemented as described above.

[0058] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and storage energy base power system is implemented as described above.

[0059] According to the scheme of the present invention, based on the characteristics of the new power system energy base, the present invention constructs a photovoltaic and wind power generation capacity configuration planning model based on regional wind energy, solar energy and other resource endowment models, combined with the receiving end load demand, and comprehensively considers the maximization of renewable energy consumption, the economic benefits of integrated power generation of large energy bases, system flexibility, reliability and carbon emission targets, taking into account the construction, operation, and operation and maintenance costs of all sides of the source, grid, load, and storage, as well as the network security constraints of the power system, the capacity constraints of the transmission channel and other factors, to achieve coordinated planning of multi-type power sources taking into account the complementarity of resource endowments and grid operation constraints, and support the fine coordinated planning of integrated multi-energy capacity configuration of source, grid, load, and storage of large wind, solar, thermal and storage energy bases under the "dual carbon" goal.

[0060] According to the solution of the present invention, the present invention constructs a photovoltaic and wind power generation capacity configuration planning model based on regional wind energy, solar energy and other resource endowment models, combines the receiving end load demand, gives full play to regional resource endowments, improves the level of new energy power generation, maximizes operating benefits, and improves the economic level; the present invention comprehensively considers the maximum consumption of renewable energy, the economic benefits of integrated power generation of large energy bases, system flexibility, reliability and carbon emission targets; the present invention adopts genetic algorithm to solve and thus improve computing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flowchart schematically shows a method for multi-energy capacity configuration planning of a large-scale wind, solar, thermal and energy storage base power system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0063] As used herein, the term "including" and variations thereof are to be interpreted as open-ended terms meaning "including, but not limited to." The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment."

[0064] Figure 1 The flowchart schematically shows a method for multi-energy capacity configuration planning of a large-scale wind, solar, thermal and storage energy base power system according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the multi-energy capacity configuration planning method of the wind, solar, thermal and storage energy large-scale power system includes:

[0065] The cost of curtailment rate D is calculated by minimizing the construction and operation cost C and maintaining the number of hours of thermal power generation. loss Minimum, large energy base power generation income f p The maximum is the goal, and three corresponding sub-objective functions are established respectively; the three sub-objective functions are converted into a unified dimension according to economic efficiency, and then converted into a single objective function min f=λ1C+λ2D loss -λ3f p ; Among them, λ1, λ2, and λ3 are the weight coefficients of cost, power abandonment rate, and operating income of large energy bases, respectively, λ1+λ2+λ3=1;

[0066] Based on the multi-energy complementary characteristics and the preset operation strategy, as well as the preset constraints and the single objective function, a multi-energy capacity optimization configuration model is constructed; under the premise that the minimum utilization hours of thermal power remain unchanged, the optimal wind turbine capacity, photovoltaic array capacity, thermal power capacity and energy storage battery capacity of the multi-energy capacity optimization configuration model are searched and solved through a genetic algorithm.

[0067] Further, according to an embodiment of the present invention, the preset operation strategy includes:

[0068] With the goal of maximizing wind and solar output, when thermal power and energy storage are required to participate in regulation, the output is in the order of thermal power and energy storage:

[0069] Keep the operating hours of thermal power generation greater than the minimum economic hours;

[0070] When energy storage resources are sufficient, energy storage regulation is used first; when energy storage resources are insufficient, thermal power regulation is used;

[0071] Priority should be given to deep peak-shaving measures, and units should be started and stopped when regulation resources are insufficient.

[0072] Further, according to an embodiment of the present invention, with the goal of minimizing the construction and operation and maintenance cost C, the corresponding sub-objective function is established as follows:

[0073] The calculation formula for construction and operation and maintenance cost C and the capacity of wind power, photovoltaic power, thermal power and energy storage is:

[0074] C=γ w S w +γ pv S pv +γ s S s +γ t S t ;

[0075] Among them, S w , S pv , S s , S t are the capacities of wind power, photovoltaic power, energy storage, and thermal power, respectively, w , γ pv , γ s , γ t They are the construction and operation and maintenance cost coefficients of wind power, photovoltaic power, energy storage and thermal power respectively.

[0076] Further, according to an embodiment of the present invention, the power abandonment rate cost D is maintained at the thermal power startup hours. loss Minimum is the goal, and the corresponding sub-objective function is established as:

[0077] The cost of curtailment rate under the number of hours of thermal power generation operation is D lossThe calculation includes: when the power abandonment rate is lower than the power abandonment rate limit, the value is the grid-connected power price * power abandonment loss; when it is higher than the power abandonment rate limit, the value is the grid-connected power price * power abandonment loss + higher power abandonment rate * power generation * penalty coefficient:

[0078]

[0079] Among them, σ is the power abandonment rate, μ0 is the upper limit of the power abandonment rate, and ε is the penalty coefficient for exceeding the power abandonment rate; P total is the total power generation of wind power or photovoltaic power in a certain period of time; η1 is the time-of-use electricity price; T is the period of time;

[0080] The formula for calculating the power abandonment rate is as follows:

[0081]

[0082] Where: σ is the power abandonment rate, P pre,t is the theoretical power generation of wind power and photovoltaic power at time t, P m,t is the actual power generation of wind power and photovoltaic power at time t.

[0083] Further, according to one embodiment of the present invention, the power generation income of the energy base is p The maximum is the goal, and the corresponding sub-objective function is established as:

[0084] f p =f n +f s +f t ;

[0085] Among them, f n Taking into account the green carbon benefits of wind power and photovoltaic power generation for energy bases, s In order to consider the energy storage operation benefits and costs over the entire life cycle, f t The operating income of thermal power plants taking into account carbon costs;

[0086]

[0087] Among them, η1 is the time-of-use electricity price; η2 is the green carbon benefit factor; T is the cycle period; P w,t and P pv,t are the actual outputs of wind power and photovoltaic power generation at time t respectively;

[0088]

[0089] Among them, η1 is the time-of-use electricity price, δ is the converted energy storage life cycle cost sharing coefficient, P sc,t With P sd,t are the energy storage charging and discharging power respectively; η c With ηd are the energy storage charging and discharging efficiencies respectively; s A collection of energy storage devices;

[0090]

[0091] Among them, P gi,t is the output power of thermal power unit i at time t, η3 is the carbon emission cost factor; f(P gi,t ) is the fuel cost of thermal power units; g(P gi,t ) is the ramp cost of thermal power units; η1 is the time-of-use electricity price; ψ t A collection of thermal power units.

[0092] Further, according to an embodiment of the present invention, the preset constraint conditions include:

[0093] (1) Power balance constraints and battery state of charge constraints:

[0094] The sum of the output of wind power, photovoltaic power, thermal power and energy storage system is equal to the real-time load

[0095] P gi,t +P w,t +P pv,t +η d P d,t =η d P ld,t ;

[0096] Among them, P gi,t is the output power of the thermal power unit at time t; P w,t and P pv,t are the actual outputs of wind power and photovoltaic power generation at time t; P d,t is the discharge power of the energy storage system; η d is the discharge efficiency of the energy storage system; P ld,t is the load at time t;

[0097] (2) Sending channel restrictions:

[0098] Capacity limits for key outbound channels

[0099] 0≤P trans,t ≤P trans,max,t ;

[0100] Among them, P trans,t is the capacity of the key sending channel in period t, P trans,max,t is the power limit of the key transmission channel in period t;

[0101] (3) Thermal power output constraints:

[0102] P tmin,i ≤Pi,t ≤P tmax,i ;

[0103] ΔP ut ≤U h ;

[0104] ΔP dt ≤D h ;

[0105] Among them, U h , D h are the rising rate and falling rate limits of the output of a single thermal power unit, ΔP ut , ΔP dt are the rising rate and falling rate of the output of a single unit, P tmin,i is the minimum allowable output of a single thermal power unit, P tmax,i The maximum permissible output of a single thermal power unit;

[0106] (4) Energy storage output and state of charge constraints:

[0107] P smin,i ≤P i,s ≤P smax,i ;

[0108] ΔP us ≤U hs ;

[0109] ΔP ds ≤D hs ;

[0110] 0.20≤SOC≤0.9;

[0111] Among them, U hs , D hs are the rising rate and falling rate limits of the energy storage unit output, ΔP us , ΔP ds are the rising rate and falling rate of the output of the energy storage unit, P smin,i is the minimum allowable output of the energy storage unit, P smax,i is the maximum allowable output of the energy storage unit, and SOC is the state of charge of the energy storage unit.

[0112] Furthermore, according to an embodiment of the present invention, after a certain number of generations of searching in the genetic algorithm, the minimum and maximum values ​​of the sub-goals will no longer be updated, and the results of the last generation will be output as the optimal values.

[0113] According to the above-mentioned scheme of the present invention, based on the characteristics of the new power system energy base, the present invention constructs a photovoltaic and wind power generation capacity configuration planning model based on regional wind energy, solar energy and other resource endowment models, combined with the receiving end load demand, and comprehensively considers the maximization of renewable energy consumption, the economic benefits of integrated power generation of large energy bases, system flexibility, reliability and carbon emission targets, taking into account the construction, operation, and operation and maintenance costs of all sides of the source, grid, load, and storage, as well as the network security constraints of the power system, the capacity constraints of the transmission channel and other factors, to achieve multi-type power source coordinated planning taking into account the complementarity of resource endowments and grid operation constraints, and support the fine coordinated planning of integrated source, grid, load, and storage multi-energy capacity configuration of large wind, solar, thermal and storage energy bases under the "dual carbon" goal.

[0114] According to the above scheme of the present invention, the present invention constructs a photovoltaic and wind power generation capacity configuration planning model based on regional wind energy, solar energy and other resource endowment models, combines the receiving end load demand, gives play to regional resource endowments, improves the level of new energy power generation, maximizes operating benefits, and improves the economic level; the present invention comprehensively considers the maximum consumption of renewable energy, the economic benefits of integrated power generation of large energy bases, system flexibility, reliability and carbon emission targets; the present invention adopts genetic algorithm to solve and thus improve computing efficiency.

[0115] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a multi-energy capacity configuration planning system for a large-scale wind, solar, thermal and energy storage power system, comprising:

[0116] The single objective function acquisition module is to minimize the construction and operation and maintenance costs C and maintain the power abandonment rate cost D under the number of hours of thermal power operation. loss Minimum, large energy base power generation income f p The maximum is the goal, and three corresponding sub-objective functions are established respectively; the three sub-objective functions are converted into a unified dimension according to economic efficiency, and then converted into a single objective function min f=λ1C+λ2D loss -λ3f p ; Among them, λ1, λ2, and λ3 are the weight coefficients of cost, power abandonment rate, and operating income of large energy bases, respectively, λ1+λ2+λ3=1;

[0117] The multi-energy capacity configuration module constructs a multi-energy capacity optimization configuration model based on the multi-energy complementary characteristics and preset operation strategies, as well as preset constraints and the single objective function; under the premise that the minimum utilization hours of thermal power remain unchanged, the optimal wind turbine capacity, photovoltaic array capacity, thermal power capacity and energy storage battery capacity of the multi-energy capacity optimization configuration model are searched and solved through a genetic algorithm.

[0118] The multi-energy capacity configuration planning system for a large-scale wind, solar, thermal and storage energy base according to the present invention can implement the multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and storage energy base power system. The specific method steps are as described above and will not be repeated here.

[0119] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and storage energy base power system as described above is implemented.

[0120] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and storage energy base power system is implemented as described above.

[0121] Based on the above solution of the present invention, the technical solution of the present invention is described in detail in the form of a specific embodiment as follows.

[0122] Example 1

[0123] The genetic algorithm is used to solve the optimal wind turbine capacity, photovoltaic array capacity, thermal power unit capacity and energy storage battery capacity, and a detailed explanation is given.

[0124] Considering the excellent optimization ability of genetic algorithms, for the aforementioned optimization problem of wind, solar, thermal and energy storage combined power generation system, the genetic algorithm is chosen to achieve the optimal configuration of the capacity of wind power, photovoltaic, thermal power and energy storage, that is, under the premise of maintaining the economic start-up hours of thermal power, the optimal wind turbine capacity, photovoltaic array capacity, thermal power unit capacity and energy storage battery capacity are configured through genetic algorithms. To this end, according to the characteristics of the problem, the number of genes in the chromosome in the genetic algorithm is 4, and the corresponding genes represent the wind turbine capacity, photovoltaic array capacity, thermal power unit capacity and energy storage battery capacity respectively. A genetic algorithm (GA) was developed based on the DEAP library (Distributed Evolutionary Algorithms in Python) using Python to solve the capacity optimization problem. DEAP is a popular library that provides the tools and functions required to implement genetic algorithms. The specific optimization process is detailed as follows:

[0125] (1) Initialization. First, define the objective function, equality, and inequality constraints to be minimized, and use the toolbox to register methods for generating individuals, populations, selection, crossover, and mutation operations. Then define the evaluation function, combine the objective function and constraint checks, and return a very small fitness value to indicate an invalid solution if the individual does not meet the constraints. Secondly, set the algorithm parameter values. According to the wind turbine capacity range, photovoltaic array capacity range, thermal power unit capacity range, and energy storage battery capacity range, initialize these four decision variables respectively, set the population size to 100, the number of iterations to 400, the crossover probability to 0.7, and the mutation probability to 0.2. Thirdly, use toolbox.register in the DEAP library to register the variable generation method, individual generation method, population generation method, genetic operations (crossover, mutation, selection), and fitness evaluation method. Use the toolbox.population method to generate the initial population.

[0126] (2) Selection operation. Sort all individuals in the population according to their fitness values, select the individuals in the top one-third of the fitness values, and sample the individuals in the bottom half of the fitness values ​​according to the selection probability.

[0127] (3) Crossover operation. According to the crossover probability, crossover individuals are randomly selected and their chromosomes are exchanged. After completing the crossover operation for each pair of chromosomes, it is necessary to determine whether the power configuration results corresponding to the pair of chromosomes meet the constraints. If not, a loop is set to re-perform the crossover operation until a new chromosome that meets the constraints is generated.

[0128] (4) Mutation operation. Select the mutant individuals according to the mutation probability, and change the partial gene values ​​of the chromosome according to the mutation probability. Similarly, after each chromosome mutation operation, it is necessary to determine whether the power configuration result corresponding to the chromosome meets the constraint conditions. If not, set a loop and perform the mutation operation again until a new chromosome that meets all the constraints is generated. The population evolves into a new population through selection, crossover and mutation operations, and the best individuals of the previous generation are retained in the new population. Then, calculate the fitness value of the total objective function F. When the power configuration result corresponding to a chromosome still does not meet the constraint conditions, the fitness value is punished accordingly.

[0129] (5) Termination condition determination. Determine whether the algorithm has reached the specified number of iterations. If so, terminate the optimization search and output the optimal wind turbine capacity, photovoltaic array capacity, thermal power unit capacity, and energy storage battery capacity; if not, go to step (2) and continue the iterative search.

[0130] Through the description of this embodiment and the analysis of the specific implementation process, it can be seen that the multi-energy capacity configuration planning method for the power system of a large wind, solar, thermal and storage energy base proposed in the present invention is effective, versatile and reasonable, and helps to further improve the comprehensive benefits of the operation of large new energy bases. The present invention constructs a photovoltaic and wind power generation capacity configuration planning model based on regional wind energy, solar energy and other resource endowment models, combines the load demand of the receiving end, gives play to the regional resource endowment, improves the level of consumption of new energy power generation, maximizes operating benefits, and improves the economic level; considers the maximum consumption of renewable energy, the economic benefits of integrated power generation of large energy bases, system flexibility, reliability and carbon emission targets; and uses genetic algorithms to solve and improve computing efficiency.

[0131] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0133] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0134] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0135] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0136] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0137] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0138] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage power system, characterized in that: include: The cost of curtailment rate D is calculated by minimizing the construction and operation cost C and maintaining the number of hours of thermal power generation. loss Minimum, large energy base power generation income f p The maximum is the goal, and three corresponding sub-objective functions are established respectively; the three sub-objective functions are converted into a unified dimension according to economic efficiency, and then converted into a single objective function min f=λ1C+λ2D loss -λ3f p ; Among them, λ1, λ2, and λ3 are the weight coefficients of cost, power abandonment rate, and operating income of large energy bases, respectively, λ1+λ2+λ3=1; Based on the multi-energy complementary characteristics and the preset operation strategy, as well as the preset constraints and the single objective function, a multi-energy capacity optimization configuration model is constructed; under the premise that the minimum utilization hours of thermal power remain unchanged, the optimal wind turbine capacity, photovoltaic array capacity, thermal power capacity and energy storage battery capacity of the multi-energy capacity optimization configuration model are searched and solved through a genetic algorithm.

2. The multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage power system according to claim 1 is characterized in that: The preset operation strategy includes: With the goal of maximizing wind and solar output, when thermal power and energy storage are required to participate in regulation, the output is in the order of thermal power and energy storage: Keep the operating hours of thermal power generation greater than the minimum economic hours; When energy storage resources are sufficient, energy storage regulation is used first; when energy storage resources are insufficient, thermal power regulation is used; Priority should be given to deep peak-shaving measures, and units should be started and stopped when regulation resources are insufficient.

3. The multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage power system according to claim 1 is characterized in that: Taking the minimum construction and operation and maintenance cost C as the goal, the corresponding sub-objective function is established as follows: The calculation formula for construction and operation and maintenance cost C and the capacity of wind power, photovoltaic power, thermal power and energy storage is: C=γ w S w +g pv S pv +g s S s +g t S t ; Among them, S w , S pv , S s , S t are the capacities of wind power, photovoltaic power, energy storage, and thermal power, respectively, w , γ pv , γ s , γ t They are the construction and operation and maintenance cost coefficients of wind power, photovoltaic power, energy storage and thermal power respectively.

4. The multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage power system according to claim 1 is characterized in that: The cost of curtailment rate D is to maintain the number of hours of thermal power generation. loss Minimum is the goal, and the corresponding sub-objective function is established as: The cost of curtailment rate under the number of hours of thermal power generation operation is D loss The calculation includes: when the power abandonment rate is lower than the power abandonment rate limit, the value is the grid-connected power price * power abandonment loss; when it is higher than the power abandonment rate limit, the value is the grid-connected power price * power abandonment loss + higher power abandonment rate * power generation * penalty coefficient: Among them, σ is the power abandonment rate, μ0 is the upper limit of the power abandonment rate, and ε is the penalty coefficient for exceeding the power abandonment rate; P total is the total power generation of wind power or photovoltaic power in a certain period of time; η1 is the time-of-use electricity price; T is the period of time; The formula for calculating the power abandonment rate is as follows: Where: σ is the power abandonment rate, P pre,t is the theoretical power generation of wind power and photovoltaic power at time t, P m,t is the actual power generation of wind power and photovoltaic power at time t.

5. The multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage power system according to claim 1 is characterized in that: Profit from power generation from large energy bases p The maximum is the goal, and the corresponding sub-objective function is established as: f p =f n +f s +f t ; Among them, f n Taking into account the green carbon benefits of wind power and photovoltaic power generation for energy bases, s In order to consider the energy storage operation benefits and costs over the entire life cycle, f t The operating income of thermal power plants taking into account carbon costs; Among them, η1 is the time-of-use electricity price; η2 is the green carbon benefit factor; T is the cycle period; P w,t and P pv,t are the actual outputs of wind power and photovoltaic power generation at time t respectively; Among them, η1 is the time-of-use electricity price, δ is the converted energy storage life cycle cost sharing coefficient, P sc,t With P sd,t are the energy storage charging and discharging power respectively; η c With η d are the energy storage charging and discharging efficiencies respectively; s A collection of energy storage devices; Among them, P gi,t is the output power of thermal power unit i at time t, η3 is the carbon emission cost factor; f(P gi,t ) is the fuel cost of thermal power units; g(P gi,t ) is the ramp cost of thermal power units; η1 is the time-of-use electricity price; ψ t A collection of thermal power units.

6. The method for multi-energy capacity configuration planning of a large-scale wind, solar, thermal and energy storage power system according to any one of claims 1 to 5, characterized in that: The preset constraints include: (1) Power balance constraints and battery state of charge constraints: The sum of the output of wind power, photovoltaic power, thermal power and energy storage system is equal to the real-time load P.S gi,t +P w,t +P pv,t +η d P.S d,t η d P.S ld,t 100. Among them, P gi,t is the output power of the thermal power unit at time t; P w,t and P pv,t are the actual outputs of wind power and photovoltaic power generation at time t; P d,t is the discharge power of the energy storage system; η d is the discharge efficiency of the energy storage system; P ld,t is the load at time t; (2) Sending channel restrictions: Capacity limits for key outbound channels 0≤P trans,t ≤P trans,max,t ; Among them, P trans,t is the capacity of the key sending channel in period t, P trans,max,t is the power limit of the key transmission channel in period t; (3) Thermal power output constraints: P tmin,i ≤P i,t ≤P tmax,i ; ΔP ut ≤U h ; ΔP dt ≤D h ; Among them, U h , D h are the rising rate and falling rate limits of the output of a single thermal power unit, ΔP ut , ΔP dt are the rising rate and falling rate of the output of a single unit, P tmin,i is the minimum allowable output of a single thermal power unit, P tmax,i The maximum permissible output of a single thermal power unit; (4) Energy storage output and state of charge constraints: P smin,i ≤P i,s ≤P smax,i ; ΔP us ≤U hs ; ΔP ds ≤D hs ; 0.20≤SOC≤0.9; Among them, U hs , D hs are the rising rate and falling rate limits of the energy storage unit output, ΔP us , ΔP ds are the rising rate and falling rate of the output of the energy storage unit, P smin,i is the minimum allowable output of the energy storage unit, P smax,i is the maximum allowable output of the energy storage unit, and SOC is the state of charge of the energy storage unit.

7. The multi-energy capacity configuration planning system for large-scale wind, solar, thermal and energy storage power systems is characterized by: include: The single objective function acquisition module is to minimize the construction and operation and maintenance costs C and maintain the power abandonment rate cost D under the number of hours of thermal power operation. loss Minimum, large energy base power generation income f p The maximum is the goal, and three corresponding sub-objective functions are established respectively; the three sub-objective functions are converted into a unified dimension according to economic efficiency, and then converted into a single objective function min f=λ1C+λ2D loss -λ3f p ; Among them, λ1, λ2, and λ3 are the weight coefficients of cost, power abandonment rate, and operating income of large energy bases, respectively, λ1+λ2+λ3=1; The multi-energy capacity configuration module constructs a multi-energy capacity optimization configuration model based on the multi-energy complementary characteristics and preset operation strategies, as well as preset constraints and the single objective function; under the premise that the minimum utilization hours of thermal power remain unchanged, the optimal wind turbine capacity, photovoltaic array capacity, thermal power capacity and energy storage battery capacity of the multi-energy capacity optimization configuration model are searched and solved through a genetic algorithm.

8. An electronic device, characterized in that It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and storage energy base power system as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the multi-energy capacity configuration planning method for a large-scale wind, solar, thermal and energy storage base power system as described in any one of claims 1-6.

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