75 results about "Energy planning" patented technology

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention discloses a power load curve clustering method, system and equipment based on an improved density peak value clustering algorithm and a storage medium, and belongs to the field of intelligent power distribution networks, and the method comprises the steps: carrying out the sampling segmentation of power load data, constructing a daily load curve set, carrying out the standardization processing of each daily load curve, and generating a standardized sample set; based on the standardized sample set, calculating a sample distance relation of the daily load curve set, and constructing a corresponding distance matrix; establishing a local density estimation model according to the distance matrix, and calculating a density estimation value of each sample curve; according to the local density estimation value of each sample curve and the relative distance between each sample curve and other samples in the distance matrix, constructing a clustering decision value set, and selecting a plurality of sample curves with clustering decision values higher than a threshold value as clustering centers; and by taking the clustering center as a reference, sequentially distributing the residual sample curves to the class cluster to which the corresponding clustering center belongs according to the density estimation value and the distance relationship between the residual sample curves and each clustering center, and completing sample clustering division. According to the method, the problem of misclassification when the density difference of the power load curve set is too large is solved, the sample density difference when the data density difference is too large is accurately represented, the clustering precision of the load curve is effectively improved, reliable power consumer social attribute identification is provided for a power supply side, and an energy planning strategy is better implemented.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

The invention provides a dual-time-scale optimization scheduling system and method considering diesel storage insurance supply and new energy consumption, and the method comprises the steps: collecting a plurality of data in real time, and outputting a historical data set and real-time monitoring data to a scheduling module; based on the wind and light output predicted values and load predicted values of the day before dispatching and the dispatching day, a multi-target optimization model is constructed, an energy storage charging and discharging plan, a diesel generator start-stop plan and an electric vehicle dispatching instruction are output, and cross-day energy transfer is achieved; on the basis of a model prediction control principle, 15 minutes are taken as a scheduling step length, a day-ahead scheduling plan is corrected in a rolling manner in combination with ultra-short-term source load prediction data, and prediction deviation is stabilized; controlling charging and discharging switching of the energy storage system, dynamic adjustment of the charging power of the electric vehicle and the operation state of the charging pile; through cooperative operation of day-ahead pre-scheduling and intra-day rolling optimization, source-load balance and low-carbon operation are realized. According to the scheduling system, long-period energy planning and short-period dynamic correction are both considered, and the self-consistency and low-carbon property of the off-grid charging station are improved.

The invention provides a polar region clean energy system staged capacity optimization method and device and a readable storage medium, and belongs to the field of new energy planning. The problems of energy storage capacity redundancy and low hydrogen energy conversion efficiency existing in a traditional capacity planning method are solved; comprising the following steps that a staged optimization model is constructed based on seasonal characteristics, the staged optimization model comprises a first-stage model and a second-stage model, the first-stage model considers day-to-day balance of one year, the second-stage model only considers intra-day balance, and total cost minimization is taken as a target; inputting related parameters, and calling the first-stage model to solve annual capacities of wind power, photovoltaic and hydrogen energy storage and a standby threshold value of a diesel generator; calling the second-stage model day by day to obtain power and capacity of electrochemical energy storage and hydrogen energy storage; performing global capacity decision and outputting final capacity configuration of each device; the method is applied to clean energy system capacity planning.

The invention belongs to the technical field of energy planning, and discloses a coal mining area green electricity direct connection planning method and system, and the method comprises the steps: integrating geographic space information, obtaining a three-dimensional geographic space information database, carrying out the processing through a land recycling evaluation model, obtaining a thermodynamic diagram, and carrying out the quantitative analysis of the thermodynamic diagram, and obtaining the power generation amount. Processing and fusing by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic combined sample; processing by using the load prediction model to obtain a load prediction curve set; a source-load time sequence mismatch quantitative model is input for analysis, and an energy storage collaborative configuration scheme is obtained; inputting a multi-target line optimization model for analysis to obtain a direct connection line topological structure diagram; and performing data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and performing coal mining area green electricity direct connection planning through the source storage load collaborative optimization model. The green electricity is obtained through direct connection, and the cost is reduced.

The invention discloses a polar region environment-oriented two-stage micro-siting optimization method for a wind generating set, and relates to the technical field of polar region renewable energy planning and wind power generation, the optimization method comprises the following steps: polar region environment parameter initialization and scene modeling; the first stage is grid primary selection based on mixed integer linear programming; the second stage is coordinate refinement based on a multi-population genetic algorithm; and scheme output and efficiency evaluation. According to the method, a sector reweighting mechanism of extremely cold correction parameters is realized through reweighting of biochemical parameters and scenes in an environment special for a polar region, the extremely cold correction parameters are directly embedded into a wind regime scene set and a power model, and the problem that a conventional land model can obviously underestimate or overestimate single-machine power and wake flow propagation distance under extremely low temperature and low roughness conditions, so that the reliability of the model is improved is solved. Therefore, the power prediction and wake flow loss evaluation are more practical under the polar region conditions such as the south pole, so that the engineering reliability of site selection decision and the accuracy of generating capacity prediction are improved.

The invention belongs to the technical field of wind and light load probability prediction, and particularly discloses a wind and light load probabilistic balance method considering hydrogen energy two-stage planning, comprising the following steps: constructing a joint probability distribution model of wind power, photovoltaic output and load demand, and defining a feasible region; based on a joint probability distribution model of wind power, photovoltaic output and load demand and a feasible region, first-stage hydrogen energy monthly planning is carried out with the goal of minimizing wind-abandoning, light-abandoning and electricity-abandoning quantity; and with minimization of the total cost of the system as a target and with the monthly planning total amount of the first-stage hydrogen energy as a constraint condition, second-stage hydrogen energy hourly scheduling is carried out, and wind and light load probabilistic balance is realized. The problems that the randomness and volatility of wind and light output are not fully considered in an existing method, the wind and light abandoning rate is high, energy waste is serious, system planning and real-time scheduling are usually separated in an existing planning and scheduling method, and dynamic association cannot be established between monthly energy storage configuration and hourly output optimization are solved.

The invention relates to the technical field of energy planning, and discloses an uncertain scene generation and planning method based on source load matching and time sequence characteristics, which effectively reduces the operation performance deviation and improves the engineering implementability of a stochastic planning scheme. According to the method, a representative meteorological year data set, a reference capacity optimization variable set and a whole-year wind-light load initial scene time set are determined through historical data of a target area in previous years; dividing the sample points in each hour into a residual power state region set and a power shortage state region set based on positive and negative source charge difference values; respectively clustering the two types of sets hour by hour by adopting a distance clustering algorithm to obtain an annual typical scene set and a corresponding first probability set; and further generating a year-round disordered boundary sequence, and performing optimization by taking the representative meteorological year data set time sequence as a benchmark to form a year-round scene boundary with time sequence characteristics. According to the method, the source load difference and the time sequence characteristics are effectively reserved, the system reliability evaluation accuracy and the planning implementability are improved, and the operation deviation is reduced.

The invention discloses an energy structure planning method and system based on an entropy flow dynamic model, and relates to the field of comprehensive energy planning, and the method comprises a first model building module which is used for building a decision matrix and a state matrix, and building an evolution model based on a system dynamics principle; the second model construction module is used for constructing a target function which takes system entropy generation as an optimization target and takes policy implementation cost and system orderliness as constraint conditions according to the entropy balance equation and the thermodynamic force-flow relationship, and constructing a maximum entropy generation model based on the target function and an evolution model; and the path output module is used for performing iterative calculation by utilizing an outer layer nested optimization algorithm based on the maximum entropy generation model to obtain an optimal policy combination of each time node in a preset planning period, and outputting an energy planning maximum entropy path. According to the method, system dynamics and an entropy theory are fused, source-network-load-storage full-link interaction is described, and a scientific and reliable benchmark reference is provided for low-carbon transformation of an energy system.

The invention discloses a multi-energy complementary system site selection method based on dynamic weight and geographic information system (GIS) cooperation, and belongs to the technical field of renewable energy planning. According to the method, a technical framework of'multi-dimensional data acquisition-dynamic weight optimization-collaborative decision verification 'is constructed, so that the core problems of weight staticizing, space-time splitting, insufficient robustness and the like in a traditional site selection method are solved. According to the technical scheme, an analytic hierarchy process and a space-time dynamic correction mechanism are innovatively combined, and self-adaptive weight optimization of multi-dimensional criteria such as climate resources, power grid access and environmental constraints is achieved; through fusion of spatial clustering analysis and Monte Carlo simulation, a site selection verification system considering geographical proximity and system collaboration is established. According to the method, the dynamic response capability of site selection decision to policy change and technical evolution is remarkably improved, the scientificity and implementation reliability of a multi-energy complementary system planning scheme are ensured, and an intelligent decision support tool is provided for efficient deployment of renewable energy sources in a novel power system.

The present application belongs to the field of comprehensive energy planning, and provides a kind of comprehensive energy system planning robust optimization configuration method and system.The method comprises, obtaining the resource condition and load demand parameter of comprehensive energy system, establish the IES optimization configuration model with the maximum internal rate of return as optimization target;Establish the uncertain set model of wind and light output and cold and heat load demand parameter, based on IGDT combined with IES optimization configuration model, construct IES robust optimization model facing investment income;Simplify IES robust optimization model, solve the maximum uncertainty fluctuation radius and the uncertain scene parameter of the corresponding worst scenario;Under the condition that the uncertain scene parameter is determined, the optimization configuration scheme of maximizing investment income is solved;According to different expected income deviation calculation forms IES optimization configuration scheme set, adopt fuzzy decision, select optimal scheme.Maximize the investment rate of return as optimization configuration target, and based on information gap decision, under the premise of reaching expected income, solve configuration scheme.

The embodiment of the invention provides an energy regulation and control system, a regulation and control method and electronic equipment, and belongs to the technical field of data processing. The energy regulation and control system comprises an Internet of Things platform module used for collecting various equipment data of an oil field station; the data platform module is used for carrying out data preprocessing on the various equipment data; the algorithm platform module is used for performing energy regulation and control based on the various equipment data after data preprocessing by using an operational research optimization algorithm or a machine learning method; and the business platform module is used for performing correlation analysis on the various equipment data based on a correlation analysis method and determining an operation strategy of the oil field station based on a correlation analysis result. The embodiment of the invention is used for solving the defects that an existing scheme lacks a comprehensive and multi-dimensional energy planning system, so that the system cost is reduced, and the load efficiency of an oil field station is improved.

The invention relates to the technical field of energy planning and utilization, in particular to an integrated energy system capacity planning optimization method for a zero-carbon park, and the method comprises the steps: obtaining an output power curve and a consumption power curve, and dividing a plurality of dominant time periods on the output power curve and the consumption power curve respectively; a dominant characteristic coefficient of each dominant time period is calculated according to the performance parameter of each dominant time period, and a first profit and loss fluctuation time period and a second profit and loss fluctuation time period are screened according to analysis results of the output power curve and the consumption power curve under the first division comparison mechanism and the second division comparison mechanism; according to the method and the device, the energy storage end equipment is selected to determine the profit and loss fluctuation feature aggregation time periods, and the optimal calling mode of the energy storage end equipment is determined in each profit and loss fluctuation feature aggregation time period, so that the complex energy supply and demand relationship is captured according to a multi-matching mechanism, and energy storage optimal calling is performed in combination with physical constraints such as equipment capacity and geographical distribution. And the overall performance of the energy system is improved.

The invention belongs to the technical field of energy optimization scheduling, and provides a comprehensive energy planning method and system considering the regulation capacity of a regulation and storage pool of a sewage plant, and the method comprises the steps: designing an economic cost function of a comprehensive energy system; the economic cost of the comprehensive energy system comprises investment cost, cost of purchasing electricity from a power distribution network, cost of purchasing natural gas and operation and maintenance cost of the comprehensive energy system; based on an integrated energy system economic cost function, establishing an integrated energy planning model; and solving the integrated energy planning model to obtain a design scheme of the integrated energy system. Under the condition of considering capacity limitation of the regulation and storage pool and natural gas and electric power price, the starting time and power of the water pump of the regulation and storage pool are optimized, and the water pump is started to pump the to-be-treated sewage into the regulation and storage pool as far as possible in the power margin period of the power distribution network, so that optimal configuration of energy of a sewage plant and reduction of energy consumption cost are realized.

This invention discloses a method for constructing a longitudinal comparative analysis model of heating unit efficiency, belonging to the field of data analysis technology. The method specifically includes the following steps: S1, Multi-source operation data acquisition and preprocessing: At least three consecutive acquisition cycles are determined, multi-source operation data is collected, and preprocessed to form a structured dataset; S2, Deep feature extraction from multi-source data: The Pearson correlation coefficient between parameters and historical efficiency is calculated, key parameters are screened, and then time windows are set according to parameter frequency to mine training features. Finally, a matrix containing key parameters and time-series features is constructed, and a deep feature set is obtained after normalization and cross-feature generation. This constructs a longitudinal comparative system for the same heating unit across at least three acquisition cycles. Through benchmark value setting and multi-cycle comprehensive efficiency calculation, the long-term energy efficiency change pattern of the unit is clearly presented, providing core data support in the time dimension for energy planning and operation and maintenance strategy formulation, and helping to deeply understand the performance evolution of the unit from a time-series perspective.

The invention relates to the technical field of artificial intelligence, in particular to a distributed energy emergency scheduling method and system based on a smart grid, and the method comprises the steps: carrying out the operation and maintenance state recovery degree visualization operation of a pre-constructed regional basic network diagram according to an edge power diagnosis information set and an energy planning information set, and obtaining a basic recovery view; performing color feature extraction operation on the basic recovery view by using an emergency energy scheduling platform to obtain a regional color feature set; performing an energy scheduling operation based on a to-be-started facility set on the regional color feature set to obtain a first energy scheduling scheme; executing an emergency energy cluster according to the first energy scheduling scheme, and obtaining an updated basic recovery view; and performing an energy scheduling operation based on power sharing on the updated basic recovery view to obtain a second energy scheduling scheme, and performing power scheduling on the emergency energy cluster according to the second energy scheduling scheme. The power grid emergency repair efficiency can be improved, and the economic loss caused by power failure is reduced.

The invention relates to an LLM-based integrated energy model establishing and solving system. The system comprises a user input module, an integrated energy knowledge base module, an LLM module and a plurality of postposition modules used for automatically establishing an integrated energy model according to an analysis result of the LLM module and solving the integrated energy model. The user input module is used for receiving a comprehensive energy planning demand described by a user in a natural language; the comprehensive energy knowledge base module is composed of a scene template base, an equipment model base, a constraint rule base and a solver strategy base. And the LLM module is used for receiving the integrated energy planning demand described by a user in a natural language and a combined text of all contents in the integrated energy knowledge base module, constructing a prompt project based on the combined text, and calling an LLM interface to generate an analysis result. According to the system, the natural language requirement of the user can be converted into a structured modeling process, the establishment of the comprehensive energy system is automatically completed, and the modeling efficiency of the comprehensive energy system is effectively improved.

The application discloses a method for joint configuration of multiple service area substations considering charging demand of expressways, relates to the technical field of traffic energy planning, and comprises the following steps: S1, establishing a traffic flow OD matrix based on the traffic flow distribution of electric vehicles at expressway entrances and exits; S2, establishing a travel behavior and charging demand model of electric vehicles based on the traffic flow OD matrix; S3, establishing a charging station queuing model and dynamically correcting the same; S4, constructing an electric vehicle charging decision probability model; S5, establishing a light-storage-charging multi-node coordination model based on the charging waiting cost of electric vehicle owners, system construction and operation and maintenance cost; and S6, optimizing the light-storage-charging multi-node coordination model to obtain an optimal solution of the joint configuration scheme of the light-storage-charging multiple service area substations considering the charging demand of expressways. The application fully considers the influence of queuing time, charging price and residual electric quantity on the charging behavior of electric vehicles, and also considers the influence of traffic flow on capacity configuration, so that efficient utilization of global resources is realized.

A kind of operation and maintenance adjusting method according to photovoltaic power station light abandonment quantity, comprising the following steps: obtaining the historical operation and maintenance data of photovoltaic power station, determine the time category of photovoltaic power station power limiting;Power data of photovoltaic power station is obtained, wherein power data includes AGC value, inverter actual time sequence power, inverter theoretical time sequence power, inverter historical actual time sequence power, inverter historical theoretical time sequence power;Power data is input into prediction model, and the light abandonment quantity is obtained by prediction model;According to light abandonment quantity and the time category of power limiting, the equipment of photovoltaic power station is adjusted.The present application has important role in power supply and demand balance, energy planning, energy saving and emission reduction, emergency response and evaluation effect etc. through the analysis of light abandonment quantity and power limiting, it is helpful to realize the stability of sustainable energy development and power supply in photovoltaic power station.

The invention provides an electric power planning method for renewable green new energy development, and belongs to the technical field of energy planning, and the method comprises the steps: calibrating a plurality of supply points in a region according to the position of each electric device in the current region and the type of an energy demand in combination with a change plan; performing preliminary modeling on each building in the area, and inferring an energy transformation scheme by combining the energy demand quantity and the energy demand type designed by each supply point; transmitting energy generated by the new energy system to a plurality of corresponding electric devices for consumption; and the new energy system is integrated into the current actual construction scheme and the building construction scheme to be carried out in the future, and finally energy supply transformation of the buildings in the area is completed. According to the electric power planning method for renewable green new energy development, the proportion of new energy supply of the whole area can be increased, efficient nearby consumption of energy is achieved, dependence on traditional energy is reduced, and a limited new energy system structure is optimized.

This invention belongs to the field of deep learning and renewable energy assessment technology, specifically a method and system for assessing urban photovoltaic potential based on multimodal deep learning. The method includes: acquiring and preprocessing multimodal data of the study area; constructing and training a multimodal deep learning model, which includes a roof recognition module based on a semantic segmentation network and a multimodal fusion module consisting of image branches, table branches, and a fusion output layer to output roof masks and potential correction parameters; based on the model output, sequentially calculating the roof's radiation potential, installation potential, technical power generation potential, carbon emission reduction potential, and economic potential, forming a multi-level potential assessment system. This invention, by fusing multi-source heterogeneous data, achieves high-precision, high-efficiency, and systematic assessment of rooftop photovoltaic potential at the urban scale, providing strong support for urban energy planning and decision-making.

This invention provides an energy management system for a modular robot, comprising a local management unit within each modular unit and a global scheduling unit located on the main control side of the modular robot. The local management unit, centered on a microcontroller, includes an energy bus dynamic reconfiguration module, a lithium-ion battery pack management and monitoring module, a lithium-ion battery pack current capacity estimation module, and a modular unit energy consumption prediction module. It estimates and outputs to the main control controller the current capacity of each modular unit and the future energy consumption requirements for each trajectory in the task sequence. The global scheduling unit, centered on the main control controller, includes a modular robot energy scheduling module. Based on data provided by each microcontroller, it performs global energy planning for the operation of the modular robot and sends energy scheduling commands to each microcontroller. This invention is applicable to solving the problem of localized failures caused by uneven energy consumption in independent power supply systems, and improving the remaining power balance rate.

The invention discloses an energy management system of multi-energy complementary smart energy based on source network load storage, which relates to the technical field of energy scheduling management and comprises an intelligent sensing unit, an intelligent control unit and a data management unit. According to the invention, energy supply data of an energy source system and various energy control systems in a park and an enterprise are obtained through an intelligent sensing unit, and energy medium production, consumption and cost data are collected in real time, so that an energy plan scheduling scheme is obtained; the system is used for realizing digitization, informatization, visualization, predication and intelligentization of the whole process of enterprise energy supply, production, conveying, conversion and consumption, realizing energy management of whole-plant energy data acquisition full coverage and enterprise main energy consumption system analysis, optimization and auxiliary decision making, improving enterprise energy utilization efficiency and improving enterprise energy utilization efficiency. The enterprise energy efficiency maximization, the energy efficiency online optimization and the important information online pushing can be realized, and the goals of energy conservation, emission reduction, cost reduction, efficiency improvement and management and control level improvement are realized.

The application discloses a database training method and device for regional energy consumption prediction and path optimization, an optimization method and system. The STIRPAT model is trained through the standardized multivariate time series data set; the standardized multivariate time series data set is divided to obtain the batched LSTM training data, the batched early stop verification data and the hyperparameter verification set; the LSTM time series prediction model is trained through the batched LSTM training data, the batched early stop verification data and the hyperparameter verification set to obtain the trained LSTM time series prediction model; the optimal fusion weight is solved according to the trained STIRPAT model and the trained LSTM time series prediction model in combination with the hyperparameter verification set, and the STIRPAT-LSTM fusion prediction scheme is constructed. The Pareto optimal solution set finally generated by the application has both the causal logic support and the accurate time series prediction basis, and the optimality and the feasibility are taken into account, thereby providing reliable technical support for regional energy planning.

This invention provides a method and system for optimizing the configuration of equipment capacity in offshore energy island systems, relating to the field of integrated offshore energy planning technology. Specifically, it includes: collecting meteorological data, load demand, and equipment parameters within an assessment period; calculating the cumulative corrosion factor corresponding to each sampling time based on the meteorological data and equipment parameters; obtaining the initial performance parameters of wind power and photovoltaic equipment; using the cumulative corrosion factor in conjunction with rainfall intensity and effective scour volume to correct the initial wind energy utilization coefficient and photovoltaic conversion efficiency, generating wind power and photovoltaic power generation; obtaining an initial configuration scheme; analyzing the power generation, energy storage charging and discharging characteristics, and load demand; and iteratively optimizing the equipment capacity configuration scheme with the goal of minimizing economic costs. This invention constructs a capacity configuration system that integrates a physical dynamic degradation mechanism, achieving accurate and reliable planning of the equipment capacity of offshore energy islands.

The invention discloses a heat supply unit efficiency longitudinal comparative analysis model construction method, and belongs to the technical field of data analysis. The method specifically comprises the following steps: S1, multi-source operation data acquisition and preprocessing: determining at least three continuous acquisition periods, acquiring multi-source operation data, and forming a structured data set after preprocessing; s2, multi-source data depth feature extraction: calculating a Pearson's correlation coefficient of parameters and historical efficiency, screening key parameters, setting a time window according to parameter frequency to mine practical training features, finally constructing a matrix containing the key parameters and time sequence features, and performing normalization and cross feature generation to obtain a depth feature set. A longitudinal comparison system of the same heat supply unit across at least three collection periods is constructed, the long-term energy efficiency change rule of the unit is clearly presented through reference value setting and multi-period comprehensive efficiency calculation, time-dimension core data support is provided for energy planning and operation and maintenance strategy making, and deep insight into unit performance evolution from the time sequence angle is assisted.

The embodiment of the invention provides a comprehensive energy planning method and related equipment, and belongs to the technical field of energy planning. The method comprises the following steps: establishing a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model and a cogeneration unit power generation model; establishing a target planning function according to the minimum cost constraint through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model; the target planning function comprises carbon emission limiting conditions for gas turbine power generation and cogeneration unit power generation; and splitting the target planning function into an inner circulation problem and an outer circulation problem through a planning optimization algorithm, solving the inner circulation problem, calculating to obtain initial scene data, and calculating to obtain a target planning scheme according to the initial scene data through the outer circulation problem. According to the embodiment of the invention, the feasibility and calculation efficiency of model solving can be effectively improved, and the adaptability of a planning scheme to multiple operation scenes is enhanced.

The invention discloses a coal-electricity transformation path comprehensive evaluation method and system based on AHP-TOPSIS, and relates to the field of power system planning, and the method comprises the steps: constructing a multi-dimensional index system for evaluating a coal-electricity transformation path based on an environment dimension, a system dimension and an economic dimension, and giving an index weight through an analytic hierarchy process AHP; and on the basis of a multi-dimensional index system with index weights, alternative scheme sorting is carried out through an approximate ideal solution sorting method TOPSIS, and comprehensive evaluation is carried out on a coal-electricity transformation path. Through organic combination of AHP and TOPSIS, subjective judgment and objective data information are effectively fused, scientificity, systematicness and operability of comprehensive evaluation of the coal-electricity transformation path are improved, and more reliable decision support is provided for energy planning and policy making.