Unit capacity planning method and system

Abstract

A unit capacity planning method and system based on a carbon emission reduction path optimization model. The method comprises: acquiring wind-power photovoltaic power data and electric heating load data (S1); constructing a carbon emission reduction path optimization model on the basis of the wind-power photovoltaic power data and the electric heating load data (S2); and performing hierarchical solution on the carbon emission reduction path optimization model to obtain an optimal energy unit capacity planning scheme (S3).

Description

Unit capacity planning method and system

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on December 1, 2023, with application number 202311642524.6. The entire contents of the above application are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of power system planning, for example, to a unit capacity planning method and system. Background Art

[0003] As energy challenges intensify, the development and utilization of renewable energy are gaining attention in many countries. Accelerating the low-carbon transformation of power systems is key to achieving low-carbon development. Power systems should minimize the investment and construction of coal-fired power units and replace traditional power generation with clean energy units. However, this will exacerbate the predicament of unstable power supply. Large-scale grid integration of a high proportion of renewable energy will further exacerbate the problem of renewable energy absorption. Furthermore, with the continuous development of the economy and society, the variety of loads and energy demand are increasing, further exacerbating load fluctuations. To ensure a sustainable power supply, thermal energy resources can be integrated and dispatched to unlock the regulation potential of the thermal energy side, thereby enhancing the regulation capacity of the power system. Furthermore, utilizing electric-thermal coupling can achieve waste heat recovery and utilization, reducing system carbon emissions through efficient energy utilization. Carbon is a core driver of the low-carbon transformation of power systems, and research on emission reduction pathways has become a key issue for achieving clean, low-carbon goals in new power systems.

[0004] Summary of the Invention

[0005] The present application provides a unit capacity planning method and system, which optimizes the capacity of energy units and reduces carbon emissions of power systems.

[0006] This application provides the following solutions:

[0007] A unit capacity planning method, the method comprising:

[0008] Obtain wind power and photovoltaic power data and electric and thermal load data;

[0009] Constructing a carbon emission reduction path optimization model based on wind power and photovoltaic power data and electric and thermal load data; the carbon emission reduction path optimization model includes an objective function and constraints, the objective function is constructed with the goal of minimizing the total cost of the power system and the carbon emission penalty cost, and the constraints include: unit constraints and carbon emission limit constraints;

[0010] The carbon emission reduction path optimization model is solved hierarchically to obtain the optimal energy unit capacity planning scheme.

[0011] Optionally, obtaining wind and photovoltaic power data includes:

[0012] The actual operation historical data of wind power and photovoltaic output are divided into four parts according to the regional weather characteristics through the K-means clustering algorithm, and the data of each part are clustered into a cluster to obtain the wind power and photovoltaic power data.

[0013] Optionally, obtaining electric and heating load data includes:

[0014] The load scenario is generated using the Latin Hypercube sampling (LHS) method based on the historical data of actual operation of electrical load and thermal load;

[0015] The K-means clustering algorithm is used to obtain the electric and thermal load data in the load scenario.

[0016] Optionally, a carbon emission reduction path optimization model is constructed based on wind power and photovoltaic power data and electric and thermal load data, including:

[0017] Range thresholds are set based on wind power and photovoltaic power data and electric and thermal load data, and objective functions and constraints are constructed according to the range thresholds to obtain a carbon emission reduction path optimization model.

[0018] Optionally, the objective function is: min F co =F+C carbon ; F co =C inv +C main +C op +C carbon +C deload ; F=C inv +C main +C op +C deload ; R n =(1+i) -n ;

[0019] Among them, F co To add carbon emission penalty cost C carbon The total cost of the power system, F represents the total cost of the power system, which mainly includes the investment and construction costs of various types of units C inv , unit maintenance cost C main , unit operating cost C op and load shedding penalty cost C deload ; The main components of investment and construction costs include wind turbines Photovoltaic units coal-fired power units Combined heat and power units heat source boiler and electrochemical energy storage systems a n,s,x represents the unit capacity investment cost of each type of unit, Represents the installed capacity of each type of unit, R n is the present value coefficient of the equivalent year, Ω n is the planning year set, Ω s is the scene collection within the year, Ω x It is a collection of various types of units; the main components of maintenance costs include wind turbines Photovoltaic units coal-fired power units Combined heat and power units heat source boiler and electrochemical energy storage systems b n,s,x Represents the unit capacity maintenance cost of each type of unit; the main components of operating costs include wind turbines Photovoltaic units coal-fired power units Combined heat and power units heat source boiler and electrochemical energy storage systems is the load shedding amount of the power system at time t in scenario s in year n, is the load shedding cost per unit of power corresponding to the load shedding amount of the power system, Ω t is the daily operating time set of various types of units, Δt is the time interval; i is the discount rate; and are the carbon emission penalty costs caused by the operation of coal-fired power units, cogeneration units and heat source boilers; The penalty cost per unit of carbon emissions for coal-fired power generation, combined heat and power generation, combined heat and power generation, and heat generation by heat source boilers in the sth scenario of the nth year.

[0020] Optionally, the unit constraints include: installed capacity constraints, preliminary screening constraints for power or thermal unit combinations, cogeneration unit operation constraints, power balance constraints, line transmission power constraints, unit output upper and lower limit constraints, and unit ramp constraints;

[0021] The installed capacity constraint is:

[0022] in, and Indicates the lower and upper limits of the installed capacity of each type of unit in year n; Indicates the installed capacity of each type of unit;

[0023] The preliminary screening constraints for the power or thermal unit combination are:

[0024] Among them, σ wind , σ pv , σ g and σ chp are the confidence factors of wind turbines, photovoltaic turbines, coal-fired power plants, and combined heat and power plants, respectively; and They are the installed capacity of wind turbines, photovoltaic units, coal-fired power units and combined heat and power units on the power side; are the installed capacities of the thermal side of the cogeneration unit and the heat source boiler respectively; is the maximum power load of the current power system in the current year, R d,e is the power capacity reserve factor; σ boil is the confidence factor of the heating unit, is the maximum heat load of the current power system in the planned year, R d,h is the thermal capacity reserve factor;

[0025] The operation constraints of the cogeneration unit are: H t =ρP t +β;

[0026] Among them, P t and H t are the electrical output and thermal output of the cogeneration unit, ρ is the heat-to-electricity ratio of the back-pressure cogeneration unit, and β is a constant related to the operating characteristics of the cogeneration unit; σ v1 and σ v2 It represents the reduction in electric power caused by increasing unit thermal power at high and low power levels while keeping the steam inlet of the cogeneration unit unchanged, σ m It is expressed as the thermal power coefficient of the cogeneration unit under back pressure conditions, P min and P max is the minimum and maximum power output of the cogeneration unit, H min is the minimum heat output of the cogeneration unit, H med is the heat median value of the extraction-condensing cogeneration unit;

[0027] The power balance constraint is:

[0028] in, and is the power output of wind turbines, photovoltaic units, coal-fired power units, cogeneration units and energy storage systems at the tth moment in the nth year and the sth scenario, and is the heat output of the cogeneration unit and the heat source boiler at the tth moment in the nth year and the sth scenario, is the load shedding demand of the power system at time t in scenario s in year n, and are the electric load demand and thermal load demand of the power system at time t in scenario s in year n, respectively;

[0029] The line transmission power constraint is:

[0030] Among them, p n,s,t,k is the actual value of the active power of the kth branch, and are the maximum allowable power value and the minimum allowable power value of the kth branch respectively;

[0031] The upper and lower limits of the unit output are:

[0032] in, and They are the minimum technical output and maximum technical output of the power supply unit respectively; and is the minimum and maximum technical output of the heating unit; p n,s,t 、h n,s,t They are the actual output of the power supply unit and the actual output of the heating unit respectively;

[0033] The unit ramp constraints are:

[0034] in, and The power supply unit's allowable power for upward climbing and downward descending; and It is the allowable power for upward climbing and downward descending of the heating unit.

[0035] Optionally, the carbon emission limit constraint includes:

[0036] in, and is the actual power output of the coal-fired power units and cogeneration units in the fuel power supply units at the tth moment in the sth scenario in the nth year, and The actual heat output of the combined heat and power unit and the heat source boiler in the fuel heating unit at the tth moment in the sth scenario in the nth year; and They are the carbon dioxide emission coefficients under unit output of coal-fired power units, power supply part of cogeneration units, heating part of cogeneration units and heating boilers, The planned carbon dioxide gas emission cap for the power system in year n.

[0037] A unit capacity planning system, comprising:

[0038] A data acquisition module configured to acquire wind power and photovoltaic power data and electric and thermal load data;

[0039] a model building module configured to construct a carbon emission reduction path optimization model based on wind power and photovoltaic power data and electric and thermal load data; the carbon emission reduction path optimization model includes an objective function and constraints, the objective function is constructed with the goal of minimizing the total cost of the power system and the carbon emission penalty cost, and the constraints include: unit constraints and carbon emission limit constraints;

[0040] The solution planning module is set to perform hierarchical solution on the carbon emission reduction path optimization model to obtain the optimal energy unit capacity planning scheme.

[0041] Optionally, the system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method described above when executing the computer program.

[0042] Optionally, a computer program is stored thereon, and when the computer program is executed, the method described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The following is a brief introduction to the drawings required for use in the embodiments.

[0044] FIG1 is a flow chart of a method for unit capacity planning based on a carbon emission reduction path optimization model provided in an embodiment of the present application;

[0045] FIG2 is a schematic diagram of a unit capacity planning method based on a carbon emission reduction path optimization model provided in an embodiment of the present application;

[0046] FIG3 is a schematic diagram of an emission reduction evolution path of a unit capacity planning method based on a carbon emission reduction path optimization model provided in an embodiment of the present application;

[0047] FIG4 is a schematic diagram of the structure of a unit capacity planning system based on a carbon emission reduction path optimization model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings. The described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0049] The present application provides a unit capacity planning method and system based on a carbon emission reduction path optimization model. The present application constructs an objective function with the goal of minimizing the total cost of the power system and the carbon emission penalty cost, takes unit constraints and carbon emission limit constraints as constraints, constructs a carbon emission reduction path optimization model, and then solves the model in layers to optimize the capacity of energy units and reduce the carbon emissions of the power system.

[0050] Example 1

[0051] As shown in Figures 1 and 2, the present application provides a unit capacity planning method based on a carbon emission reduction path optimization model, the method comprising:

[0052] Step S1: Obtain wind power and photovoltaic power data and electric heating load data.

[0053] Step S2: construct a carbon emission reduction path optimization model based on wind power and photovoltaic power data and electric heat load data; the carbon emission reduction path optimization model includes an objective function and constraints, the objective function is constructed with the goal of minimizing the total cost of the power system and the carbon emission penalty cost, and the constraints include: unit constraints and carbon emission limit constraints.

[0054] Step S3: performing hierarchical solution on the carbon emission reduction path optimization model to obtain the optimal energy unit capacity planning scheme.

[0055] As an optional implementation, the process of obtaining wind power and photovoltaic power data is as follows:

[0056] The actual operation historical data of wind power and photovoltaic output are divided into four parts according to the regional weather characteristics through the K-means clustering algorithm, and the data of each part are clustered into a cluster to obtain the wind power and photovoltaic power data.

[0057] For example, the historical data is divided into four parts: spring, summer, autumn and winter according to the weather characteristics of the region, and the K-means clustering algorithm is used to cluster the data in each quarter into a cluster to obtain wind power and photovoltaic power data.

[0058] As an optional implementation, the process of obtaining electric and heating load data is as follows:

[0059] The load scenario is generated using the Latin Hypercube sampling (LHS) method based on the historical data of actual operation of electrical load and thermal load;

[0060] The K-means clustering algorithm is used to obtain the electric and thermal load data in the load scenario.

[0061] For example, the LHS method is used to generate a large number of load scenarios based on historical data from actual electrical and thermal load operations. The LHS method divides a large area into fixed cells, sampling each cell only once. Assuming the x-dimensional vector space is sampled y times, with each dimension uniformly sampled, the intermediate sampling steps can be stored in matrix A, and matrix B can be used to store the coordinates of the sampling points.

[0062] Where P represents the sampling point. Y values ​​are randomly extracted from the first dimension of the x-dimensional vector space to form a column vector as the first column of matrix A. Similarly, y values ​​are randomly extracted from the second dimension of the x-dimensional vector space to form the second column of matrix A, and so on. The order of the dimensions of matrix A is maintained and each row of matrix A is randomly shuffled to obtain matrix B, which stores the coordinates of the sampling points. Each column vector of matrix B represents one sampling point, and matrix B has a total of y columns, meaning that y sampling points are obtained through LHS sampling.

[0063] Based on the historical data of actual operation of electrical load and thermal load, a series of load scenarios C can be generated:

[0064] The K-means clustering algorithm is used to randomly select any number of initial mean vectors S = {S1, S2, ..., S k}, analyze the distance between load scenario Si and the initial mean vector S, and divide load scenario C into different clusters. After all initial samples in the sample are completely divided, select the initial mean vector from the new cluster to re-partition the new cluster. Repeatedly, until the segmentation result remains unchanged, the final scenario cluster segmentation result is obtained, which is used to obtain electric and thermal load data. In addition, load forecast data for future years can be obtained, providing load-side data support for unit capacity planning in the carbon reduction path optimization model.

[0065] As an optional implementation method, a carbon emission reduction path optimization model is constructed based on wind power and photovoltaic power data and electric and thermal load data, including:

[0066] Range thresholds are set based on wind power and photovoltaic power data and electric and thermal load data, and objective functions and constraints are constructed according to the range thresholds to build a carbon emission reduction path optimization model.

[0067] Optionally, the wind power and photovoltaic power data include: the predicted output of the wind turbine and photovoltaic units at the tth moment in the nth year and the sth scenario and

[0068] The electric and thermal load data include: the maximum electric load in the current power system planning year The maximum heat load of the current power system in the planned year The power system load demand at time t under scenario s in year n and heat load requirements and

[0069] As an optional implementation, the objective function is: min F co =F+C carbon ; F co =C inv +C main +C op +C carbon +C deload ; F=C inv +C main +C op +C deload ; R n =(1+i) -n .

[0070] Among them, F co To add carbon emission penalty cost C carbon The total cost of the power system, F represents the total cost of the power system, which mainly includes the investment and construction costs of various types of units C inv , unit maintenance cost C main , unit operating cost C op and load shedding penalty cost C deload ; The main components of investment and construction costs include wind turbines Photovoltaic units coal-fired power units Combined heat and power units heat source boiler and electrochemical energy storage systems a n,s,x represents the unit capacity investment cost of each type of unit, Represents the installed capacity of each type of unit, R n is the present value coefficient of the equivalent year, Ω nis the planning year set, Ω s is the scene collection within the year, Ω x It is a collection of various types of units; the main components of maintenance costs include wind turbines Photovoltaic units coal-fired power units Combined heat and power units heat source boiler and electrochemical energy storage systems b n,s,x Represents the unit capacity maintenance cost of each type of unit; the main components of operating costs include wind turbines Photovoltaic units coal-fired power units Combined heat and power units heat source boiler and electrochemical energy storage systems is the load shedding amount of the power system at time t in scenario s in year n, is the load shedding cost per unit of power corresponding to the load shedding amount of the power system, Ω t is the daily operating time set of various types of units, Δt is the time interval; i is the discount rate; and are the carbon emission penalty costs caused by the operation of coal-fired power units, cogeneration units and heat source boilers; and The penalty cost per unit of carbon emissions for coal-fired power generation, combined heat and power generation, combined heat and power generation, and heat generation by heat source boilers in the sth scenario of the nth year. and The penalty cost per unit of electricity curtailment for wind power and photovoltaic units, is the unit electricity generation cost of coal-fired power units, and The unit electricity generation cost and unit heat supply cost of the cogeneration unit are is the unit heat supply cost of the heat source boiler, is the unit operating cost of the energy storage system; and The predicted output of wind turbines and photovoltaic units at time t in scenario s in year n; and is the power output of wind turbines, photovoltaic units, coal-fired power units, cogeneration units and energy storage systems at the tth moment in the nth year and the sth scenario, and is the thermal output of the cogeneration unit and heat source boiler at time t in the sth scenario in the nth year.

[0071] Among them, the power system refers to the regional power system that takes thermal constraints into consideration. The electric / heat source units mainly include wind turbines, photovoltaic units, coal-fired power units, cogeneration units and heat source boilers. The energy storage system considers electrochemical energy storage systems.

[0072] As an optional implementation method, the unit constraints include: installed capacity constraints, preliminary screening constraints for power / thermal unit combinations, cogeneration unit operation constraints, power balance constraints, line transmission power constraints, unit output upper and lower limit constraints, and unit ramp constraints.

[0073] The installed capacity constraint is:

[0074] in, and Indicates the lower and upper limits of the installed capacity of each type of unit in year n; Indicates the installed capacity of each type of unit; It means that the installed capacity of the power system is only considered to be expanded, and the installed capacity of the previous year is used as the initial capacity of the installed capacity of the next year.

[0075] The preliminary screening constraints for the power / thermal unit combination are:

[0076] Among them, σ wind , σ pv , σg and σ chp are the confidence factors of wind turbines, photovoltaic turbines, coal-fired power plants, and combined heat and power plants, respectively; and They are the installed capacity of wind turbines, photovoltaic units, coal-fired power units and combined heat and power units on the power side; are the installed capacities of the thermal side of the cogeneration unit and the heat source boiler respectively; is the maximum power load of the current power system in the current year, R d,e is the power capacity reserve factor; σ boil is the confidence factor of the heating unit, is the maximum heat load of the current power system in the planned year, R d,h is the thermal capacity reserve coefficient.

[0077] For the preliminary screening constraints of electric / thermal unit combinations, the total installed capacity of the power system's power and heat sources must meet the maximum electric and thermal loads, with a certain margin. At the same time, for new energy units, since it is difficult for them to reach full output, a confidence factor is introduced to further narrow the constraint range. This constraint is not complete and is only used to pre-screen unit installed capacity configurations that obviously do not meet the requirements.

[0078] The operation constraints of the cogeneration unit are: H t =ρP t +β

[0079] Among them, P t and H t are the electrical output and thermal output of the cogeneration unit, ρ is the heat-to-electricity ratio of the back-pressure cogeneration unit, and β is a constant related to the operating characteristics of the cogeneration unit; σ v1 and σ v2 It represents the reduction in electric power caused by increasing unit thermal power at high and low power levels while keeping the steam inlet of the cogeneration unit unchanged, σ m It is expressed as the thermal power coefficient of the cogeneration unit under back pressure conditions, P min and P max is the minimum and maximum power output of the cogeneration unit, H min is the minimum heat output of the cogeneration unit, H med It is the thermal median value of the extraction-condensing cogeneration unit.

[0080] Based on their operating conditions, CHP units can be primarily categorized as backpressure and extraction-condensing. Backpressure CHP units satisfy a specific thermal-electric coupling relationship, accounting for heat leakage losses during the electric-to-heat conversion process. The relationship between the unit's electrical and thermal outputs is approximately linear. Extraction-condensing units, on the other hand, extract steam at a certain pressure from the turbine cylinder to supply heat. Their power generation and heating capacity can be adjusted freely within a certain range.

[0081] The power balance constraint is:

[0082] in, and is the power output of wind turbines, photovoltaic units, coal-fired power units, cogeneration units and energy storage systems at the tth moment in the nth year and the sth scenario, and is the heat output of the cogeneration unit and the heat source boiler at the tth moment in the nth year and the sth scenario, is the load shedding demand of the power system at time t in scenario s in year n, and are the electric load demand and thermal load demand of the power system at the tth moment in the sth scenario in the nth year, respectively.

[0083] For power balance constraints, in any scenario and at any time period, the sum of the power output should be equal to the total power load, and the sum of the heat source output should be equal to the total heat load.

[0084] The line transmission power constraint is:

[0085] Among them, p n,s,t,k is the actual value of the active power of the kth branch, and are the maximum allowable power value and the minimum allowable power value of the kth branch respectively.

[0086] Regarding line transmission power constraints, in large power grid planning problems, whether the voltage exceeds the limit during power system operation is generally not considered, and only static safety analysis is performed on whether the line flow exceeds the transmission line capacity limit.

[0087] The upper and lower limits of the unit output are:

[0088] in, and They are the minimum technical output and maximum technical output of the power supply unit respectively; and is the minimum and maximum technical output of the heating unit; p n,s,t 、h n,s,t They are the actual output of the power supply unit and the actual output of the heating unit respectively.

[0089] For the upper and lower limit constraints of unit output, the output of any unit in the power generation state or heating state should be controlled within its allowable range. When conducting system planning, the upper and lower limit constraints of unit output and the ramp constraints are generally considered.

[0090] The unit ramp constraints are:

[0091] in, and The power supply unit's allowable power for upward climbing and downward descending; and It is the allowable power for upward climbing and downward descending of the heating unit.

[0092] As shown in FIG3 , as an optional implementation, the carbon emission limit constraint includes:

[0093] in, and is the actual power output of the coal-fired power units and cogeneration units in the fuel power supply units at the tth moment in the sth scenario in the nth year, and The actual heat output of the combined heat and power unit and the heat source boiler in the fuel heating unit at the tth moment in the sth scenario in the nth year; and They are the carbon dioxide emission coefficients under unit output of coal-fired power units, power supply part of cogeneration units, heating part of cogeneration units and heating boilers, The planned carbon dioxide gas emission cap for the power system in year n.

[0094] For carbon emission limit constraints, the emission reduction evolution path is set according to the carbon emission target, and the front acceleration and back acceleration theory is used to generate multiple types of emission reduction paths. The carbon emission results generated by multi-scenario planning without considering carbon emission limit constraints and carbon tax are used as the basic carbon emissions. In multiple planning stages, carbon emissions are limited using uniform speed, front acceleration and back acceleration evolution paths.

[0095] The constructed carbon emission reduction path optimization model is as follows: F co =C inv +C main +C op +C carbon +C deload

[0096] Optionally, the carbon emission reduction path optimization model is solved hierarchically to obtain the optimal energy unit capacity planning solution, including:

[0097] Set up energy unit capacity planning scheme based on the initial total installed capacity;

[0098] Based on the energy unit capacity planning scheme, the quantum particle swarm algorithm is used to solve the production simulation layer of the carbon emission reduction path optimization model to obtain the total cost of the power system with the carbon emission penalty cost added;

[0099] Based on the total cost of the power system with the carbon emission penalty cost added, a search algorithm is used to solve the investment decision-making layer of the carbon emission reduction path optimization model to obtain a new energy unit capacity planning scheme, and then "based on the energy unit capacity planning scheme, the quantum particle swarm algorithm is used to solve the production simulation layer of the carbon emission reduction path optimization model to obtain the total cost of the power system with the carbon emission penalty cost added" is re-executed, and a loop iteration is performed until the preset number of iterations is reached or the preset convergence conditions are met. The energy unit capacity planning scheme with the lowest total cost of the power system with the carbon emission penalty cost added is taken as the optimal energy unit capacity planning scheme.

[0100] An improved search algorithm is used to solve the investment decision-making layer of the carbon emission reduction path optimization model. When accuracy requirements are low, the heuristic search method can select a larger iteration step size to obtain a better energy unit capacity configuration result with fewer iterations. At the same time, the heuristic search method can search along multiple directions, which can avoid the problem of being trapped in the local optimal solution during the search process. The search direction of the pattern search algorithm is affected by the order of priority. When the accuracy is low, the search process is prone to being trapped in the local optimal solution. However, when the accuracy is high, the optimal solution in the local area can be found. The heuristic search method is combined with the pattern search method. The heuristic search method is first used to search along the descending trend in each direction with a large step size to determine the area where the optimal solution is located. Then, the pattern search method is used to improve the accuracy and determine the optimal solution within the area, thereby improving the efficiency and accuracy of the search.

[0101] A quantum particle swarm algorithm (QPSO) is used to solve the production simulation layer of the carbon reduction path optimization model. This algorithm builds on the traditional particle swarm algorithm by extending particles into quantum space, imbuing them with quantum behavior. This algorithm offers advantages such as simple evolutionary equations, fewer control parameters, fast convergence, and minimal computational effort. Particles in quantum space only update their positions, not their velocities. This eliminates the velocity constraints of particles in traditional particle swarm algorithms, improving the algorithm's global search capabilities and convergence speed.

[0102] The evolution equation is:

[0103] x i (t) is the position of the i-th particle at the t-th iteration; Mbest(t) is the average optimal position of the particles at the t-th iteration; Pbest i (t) is the individual optimal position of the i-th particle at the t-th iteration; Gbest(t) is the global optimal position at the t-th iteration; p i (t) is the attractor of the i-th particle at the t-th iteration, which indicates the ability of the particle to attract other particles; m is the number of particles; and u i(t) is a random number between (0,1); α is the shrinkage-expansion coefficient. During the operation simulation, the initial parameters are set according to the unit capacity configuration transmitted from the investment decision part, and the population is initialized. The first generation of particle positions are randomly generated. Based on the particle fitness function, the fitness of each particle's initial position and the parameter p of the ability to attract other particles are calculated. i (t), calculate Pbest i (t) and Gbest(t), and then calculate the current average optimal position of the particle Mbest(t); after the position is updated, calculate the fitness function again, and based on the new fitness function, calculate the fitness of each particle's updated position and the parameters of its ability to attract other particles, and calculate Pbest i (t) and Gbest(t), and with Pbest i (t-1) and Gbest(t-1), if the particle position fitness is better after the position update, use Pbest i (t) Replace Pbest i (t-1), and replace Gbest(t-1) with Gbest(t). Continuously update the position and fitness until the maximum number of iterations is reached or the set conditions are met, and output Gbest(t), which means the optimal result of the simulation part can be obtained.

[0104] Example 2

[0105] As shown in FIG4 , the present application further provides a unit capacity planning system based on a carbon emission reduction path optimization model, the system comprising:

[0106] The data acquisition module 101 is configured to acquire wind power and photovoltaic power data and electric and thermal load data.

[0107] The model building module 102 is configured to construct a carbon emission reduction path optimization model based on wind power and photovoltaic power data and electric heat load data; the carbon emission reduction path optimization model includes an objective function and constraints, the objective function is constructed with the goal of minimizing the total cost of the power system and the carbon emission penalty cost, and the constraints include: unit constraints and carbon emission limit constraints.

[0108] The solution planning module 103 is configured to perform hierarchical solution on the carbon emission reduction path optimization model to obtain the optimal energy unit capacity planning solution.

[0109] Example 3

[0110] The present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described above when executing the computer program.

[0111] Example 4

[0112] The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the method described above when the computer program is executed.

[0113] This application uses the K-means clustering algorithm to calculate the normalized curve of wind power and photovoltaic power based on the historical data of electric load, thermal load and wind power and photovoltaic output at a regional level, and uses the Latin hypercube sampling method and the K-means clustering algorithm to obtain the electric and thermal load curve. An overall cost model of the power system is established based on the investment and operation status of the power units and heat source units, including the investment cost of the system energy units, maintenance cost, operation cost and load shedding cost, and the overall cost model of the system is used as the objective function of the subsequent multi-scenario planning model. Based on the operating characteristics of the extraction condensing and back-pressure cogeneration units, a refined cogeneration unit operation model is established. On the basis of the first two models, planning level constraints and operation level constraints are incorporated. The planning level includes installed capacity constraints and preliminary screening constraints for the combination of electric power / thermal units; the operation level includes power balance constraints, line transmission power constraints, upper and lower limit constraints of unit output, and ramp constraints, forming a multi-scenario planning model. Based on a multi-scenario planning model, low-carbon factors are incorporated. Carbon emission penalty costs are added to the objective function, and carbon emission limits are added to the constraints. A carbon emission evolution path is set based on the carbon emission target. The pre-acceleration and post-acceleration theory is used to generate multiple emission reduction paths, forming a carbon emission reduction path optimization model. An improved search algorithm and quantum particle swarm optimization algorithm are used to quickly solve the carbon emission reduction path optimization model and output the optimal energy unit capacity planning solution.

[0114] This application constructs an objective function with the goal of minimizing the total cost of the power system and the carbon emission penalty cost, takes the unit constraints and carbon emission limit constraints as constraints, constructs a carbon emission reduction path optimization model, and then solves the model in layers, thereby optimizing the capacity of energy units and reducing the carbon emissions of the power system. This application uses the Latin hypercube sampling method to deal with load forecasting problems under uncertain scenarios, which has faster computing speed and higher reliability. It combines the K-means clustering algorithm to simplify the load scenario to obtain a more accurate description of the load scenario. This application establishes a refined cogeneration operation model based on the actual operating conditions of cogeneration units, and proposes a new power system planning method that fully calls on the regulation potential of the heat source side, reduces peak-shaving costs, and makes the planning scheme more in line with reality. This application combines the carbon emission reduction target requirements with carbon restriction measures to construct a carbon emission reduction path optimization model, reduces the carbon emissions of the power system, and achieves comprehensive optimization of the economic operation and carbon emission reduction of the power system. This application also proposes a method for generating multiple emission reduction pathways using the pre-acceleration and post-acceleration theory. This method accurately describes the evolution of carbon emissions, provides guidance for achieving emission reduction targets, and enables rapid acquisition of emission reduction pathway solutions. The improved search algorithm and quantum particle swarm optimization algorithm are used to jointly solve the carbon emission reduction pathway optimization model, effectively improving the algorithm's global search capabilities and convergence speed, resulting in high search efficiency, high accuracy, and fast solution speed.

Claims

1. A unit capacity planning method, comprising: Obtain wind power and photovoltaic power data and electric heat load data; Constructing a carbon emission reduction path optimization model based on the wind power and photovoltaic power data and the electric heat load data; The carbon emission reduction path optimization model includes an objective function and constraints. The objective function is constructed with the goal of minimizing the total cost of the power system and the carbon emission penalty cost. The constraints include: unit constraints and carbon emission limit constraints; The carbon emission reduction path optimization model is solved hierarchically to obtain the optimal energy unit capacity planning solution.

2. The method according to claim 1, wherein: The obtaining of wind power and photovoltaic power data includes: The actual operation historical data of wind power and photovoltaic output are divided into four parts according to the weather characteristics of the region through the K-means clustering algorithm, and the data of each part are clustered into a cluster to obtain the wind power and photovoltaic power data.

3. The method according to claim 1, wherein: The obtaining of electric heat load data comprises: The load scenario is generated using the Latin Hypercube Sampling (LHS) method based on the historical data of actual operation of electrical and thermal loads; In the load scenario, the K-means clustering algorithm is used to obtain the electric and thermal load data.

4. The method according to claim 1, wherein: The carbon emission reduction path optimization model is constructed based on wind power and photovoltaic power data and electric heat load data, including: A range threshold is set based on the wind power and photovoltaic power data and the electric and thermal load data, and an objective function and constraint conditions are constructed according to the range threshold to obtain a carbon emission reduction path optimization model.

5. The method according to claim 1, wherein: The objective function is: min F co =F+C carbon ; F co =C inv +C main +C op +C carbon +C deload ; F=C inv +C main +C op +C deload ; R n =(1+i) -n ; Among them, F co To add carbon emission penalty cost C carbon The total cost of the power system is F, which mainly includes the investment and construction costs of various types of units C inv , Unit maintenance cost C main 、Unit operating cost C op and load shedding penalty cost C deload ; The main components of investment and construction costs include wind turbines Photovoltaic unit Coal-fired power units Combined heat and power unit Heat source boiler and electrochemical energy storage systems a n,s,x represents the unit capacity investment cost of each type of unit, Represents the installed capacity of each type of unit, R n is the present value coefficient of the equivalent year, Ω n is the planning year set, Ω s is the scene collection within the year, Ω x It is a collection of various types of units; the main components of maintenance costs include wind turbines Photovoltaic unit Coal-fired power units Combined heat and power unit Heat source boiler and electrochemical energy storage systems b n,s,x Represents the unit capacity maintenance cost of each type of unit; the main components of operating costs include wind turbines Photovoltaic unit Coal-fired power units Combined heat and power unit Heat source boiler and electrochemical energy storage systems is the load shedding amount of the power system at the tth moment in the sth scenario in the nth year, is the load shedding cost per unit corresponding to the load shedding amount of the power system, Ω t is the daily operation time set of various types of units, Δt is the time interval; i is the discount rate; and The carbon emission penalty costs caused by the operation of coal-fired power units, cogeneration units and heat source boilers; and It is the penalty cost per unit of carbon emission of coal-fired power generation, combined heat and power generation, combined heat and power generation and heat source boiler heating in the sth scenario in the nth year.

6. The method according to claim 1, wherein: The unit constraints include: installed capacity constraints, preliminary screening constraints for power or thermal unit combinations, cogeneration unit operation constraints, power balance constraints, line transmission power constraints, unit output upper and lower limit constraints, and unit ramp constraints; Wherein, the installed capacity constraint is: in, and Indicates the lower and upper limits of the installed capacity of each type of unit in year n; Indicates the installed capacity of each type of unit; The preliminary screening constraints for the combination of power or thermal units are: Among them, σ wind , σ pv , σ g and σ chp are the confidence factors of wind turbines, photovoltaic units, coal-fired units, and combined heat and power units, respectively; and They are the installed capacity of wind turbines, photovoltaic units, coal-fired units and combined heat and power units on the power side; They are the installed capacity of the thermal side of the cogeneration unit and the heat source boiler respectively; is the maximum power load of the current power system in the current year, R d,e is the power capacity reserve factor; σ boil is the confidence factor of the heating unit, is the maximum heat load of the current power system in the current year, R d,h is the thermal capacity reserve factor; The operation constraints of the cogeneration unit are: H t =ρP t +b; Among them, P t and H t are the electrical output and thermal output of the CHP unit, ρ is the thermal-electricity ratio of the back-pressure CHP unit, β is a constant related to the operating characteristics of the CHP unit; σ v1 and σ v2 It indicates the reduction of electric power caused by increasing unit thermal power at high and low power levels when the steam inlet of the cogeneration unit remains unchanged, σ m It is expressed as the thermal power coefficient of the cogeneration unit under back pressure conditions, P min and P max is the minimum and maximum power output of the cogeneration unit, H min is the minimum heat output of the cogeneration unit, H med is the thermal median value of the extraction-condensing cogeneration unit; The power balance constraint is: in, and is the power output of wind turbines, photovoltaic units, coal-fired power units, cogeneration units and energy storage systems at the tth moment in the nth year and the sth scenario, and is the heat output of the CHP unit and the heat source boiler at the tth moment in the nth year and the sth scenario, is the load shedding demand of the power system at the tth moment in the sth scenario in the nth year, and are the electric load demand and thermal load demand of the power system at the tth moment in the sth scenario in the nth year, respectively; The line transmission power constraint is: Among them, p n,s,t,k is the actual value of active power of the kth branch, and are the maximum allowable power value and the minimum allowable power value of the kth branch respectively; The upper and lower limits of the unit output are: in, and They are the minimum technical output and maximum technical output of the power supply unit respectively; and is the minimum and maximum technical output of the heating unit; p n,s,t 、h n,s,t They are the actual output of the power supply unit and the actual output of the heat supply unit respectively; The unit ramp constraints are: in, and The power supply unit's upward climbing allowable power and downward descending allowable power Rate; and It is the allowable power for upward climbing and downward descending of the heating unit.

7. The method according to claim 1, wherein: The carbon emission limit constraints include: in, and is the actual power output of the coal-fired power units and cogeneration units in the fuel power supply units at the tth moment in the sth scenario in the nth year, and The actual heat output of the combined heat and power unit and the heat source boiler in the fuel heating unit at the tth moment in the nth scenario of the nth year; and They are the carbon dioxide emission coefficients under unit output of coal-fired power units, power supply part of cogeneration units, heating part of cogeneration units and heating boilers. The planned carbon dioxide gas emission cap for the power system in year n.

8. A unit capacity planning system, comprising: A data acquisition module, configured to acquire wind power and photovoltaic power data and electric heat load data; A model building module is configured to build a carbon emission reduction path optimization model based on the wind power photovoltaic power data and the electric heat load data; the carbon emission reduction path optimization model includes an objective function and constraints, the objective function is built with the goal of minimizing the total cost of the power system and the carbon emission penalty cost, and the constraints include: unit constraints and carbon emission limit constraints; The solution planning module is configured to perform hierarchical solution on the carbon emission reduction path optimization model to obtain the optimal energy unit capacity planning solution.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method according to any one of claims 1 to 7 when executed.

Images