52 results about "Renewable energy consumption" patented technology

The application discloses a kind of integrated energy system low carbon operation method, belong to power grid technical field.The method includes based on baseline method allocation system free carbon emission quota, calculate actual net carbon emissions;Set integrated energy system objective function;Adopt two-stage robust optimization method, handle renewable energy and load uncertainty, first based on predicted worst-case scenario information obtains pre-dispatch optimization result, subsequently based on pre-dispatch information seeks worst-case scenario, ensure stable operation of system by iterative solution;Build and include integrated energy system, integrated energy system and photovoltaic production and sales between and photovoltaic production and sales between multilayer transaction framework, adopt Nash negotiation model and multistage robust method, realize the energy distribution between multiple stakeholders;By solving integrated optimization model, obtain the optimal strategy of integrated energy system low carbon operation.Thereby realized the coordination optimization of carbon emission right and renewable energy consumption, further improve the low carbon operation effect of system.

This invention discloses a joint clearing method and system for the electricity spot market that considers the generalized energy storage characteristics of electric vehicle aggregators. Based on the operational characteristics and physical constraints of large-scale electric vehicle access to the electricity spot market, and combined with the theory of generalized energy storage efficiency, an embedded constraint model is constructed, including charging and discharging power boundaries, energy state temporal coupling, and terminal electricity demand. This enables quantitative modeling and unified scheduling of the flexibility resources of electric vehicle aggregators. Data cleaning and parameter normalization methods are used to map the characteristics of dispersed electric vehicle clusters to standardized virtual power plant parameters. State equation constraints are introduced into the traditional DC optimal power flow and unit combination model. Combining mixed-integer linear programming algorithms and dual pricing theory, the optimal coordination strategy between conventional unit output and electric vehicle charging and discharging is solved, and the low-cost clearing and nodal marginal price of the spot market are calculated. The method of this invention can improve the level of renewable energy consumption and the economy and security of grid operation.

This application relates to the field of power grid optimization dispatching technology, specifically to a multi-objective optimization dispatching method and system for active distribution networks with source-grid-load-storage coordination. The method includes: collecting the energy storage capacity of each renewable energy power station in the active distribution network, and the energy dispatched by each transformer from each renewable energy power station; calculating the priority dispatching characteristics of each renewable energy power station; predicting the future energy demand of each transformer and the future energy storage capacity of the renewable energy power stations supplying them; coordinating the transmission distances between each transformer and the renewable energy power stations supplying them; determining the dispatch matching coefficient between the renewable energy power stations and transformers; based on the dispatch matching coefficient, constructing an objective function with the goal of minimizing the source-grid-load-storage coordinated dispatching cost and maximizing renewable energy consumption; and using a multi-objective optimization algorithm to solve for the optimal solution, which is then used for power dispatching. This improves the rationality of power resource dispatching in the distribution network.

The application discloses a kind of medium and long term random production simulation method, system, medium and equipment, obtain the basic technical data of power system containing renewable energy and the predicted distribution data of renewable energy;Based on the basic technical data of power system and predicted distribution data, to realize system power supply safety, renewable energy consumption, reduce the target of system total cost, in the case where complex constraints are considered Markov decision process is constructed;Approximate dynamic programming method is used to iteratively train Markov decision process, and an optimal set of decisions is searched as a medium and long term operation mode.The application searches the optimal operation mode through the distribution of random factors, avoids the interference of subjective factors, reduces the total cost of the system under the condition of ensuring power supply safety and renewable energy consumption in extreme scenarios, and finally obtains a medium and long term operation mode and evaluation results that perform better than traditional solutions, providing a reference for the safe operation of power systems.

This invention provides a method for optimizing energy storage configuration in distribution networks based on renewable energy consumption, belonging to the field of energy storage optimization configuration. The method involves: a synchronization feature module for time-domain synchronization processing to generate feature vectors; a frequency mapping modeling module to construct an equivalent feature matrix including inter-axis cross terms; an admittance reconstruction module to inject this into the static topology to generate a real-state dynamic admittance model and solve for the impedance matrix; a sensitivity evaluation module to map and generate real-state voltage sensitivity and determine the impedance offset index; a configuration module to perform nonlinear hedging to generate configuration data; and a calibration module to perform recursive calibration. The system also generates source-grid attribution weights based on normalized cross-correlation coefficients, enabling accurate determination and differentiated correction of causes on the equipment side and grid side, and introduces a self-evolving evolutionary flow for closed-loop optimization. This invention effectively reduces asynchronous interference from heterogeneous data, improves the accuracy of risk attribution and control, and ensures the stability of the distribution network.

The application provides a renewable energy supply-distribution-demand-storage system space-time coordination configuration method, relates to the technical field of intelligent energy, and comprises the following steps: acquiring data of multiple regions; calculating time mismatch degrees, space mismatch degrees and space-time coupling mismatch degrees of the regions; decomposing an optimization problem into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension; constructing multi-objective optimization functions of the time layers; decomposing the optimization problems of the time layers in a space dimension; alternately solving local optimization sub-problems and global coordination problems of the regions by using a distributed iteration method; outputting a configuration result of a current time layer when the algorithm converges; executing a decision of a first time period by each time layer and taking the decision as a constraint condition of a next time layer to obtain power generation installed capacities, power transmission line capacities, energy storage capacities and operation strategies of the regions. The application can improve a renewable energy consumption rate and reduce system operation costs.

This application relates to the field of microgrid operation and control technology, and discloses a microgrid operation and scheduling method. The solution includes the following steps: S1: Multi-source data acquisition and high-precision prediction. The economic benefits of this application are improved: operating costs are reduced by 15%-20%, the renewable energy consumption rate is increased from 85% to over 98%, and revenue is increased through peak-valley electricity price differences and demand response; reliability is enhanced: the power supply deficit rate is reduced to below 0.5%, the voltage / frequency deviation is controlled within ±2% / ±0.5Hz, and the critical load guarantee rate is 100% in islanded mode; environmental protection is optimized: carbon emissions are reduced by 20%-30%, which meets the requirements of the "dual carbon" policy and reduces pollutant emissions; wide adaptability: it is compatible with microgrids of different scales and operating modes, the algorithm is robust, and it can cope with large fluctuations in wind and solar / load; engineering feasibility: it adopts mature algorithms and standardized processes, and can be upgraded and transformed based on existing EMS systems, with low deployment costs and quick results.

The present application relates to electric vehicle charging scheduling technical field, especially to distributed charging pile group dynamic load balancing and power optimization allocation system and method, including: cloud coordinator obtains the space correlation data between piles containing geographical distance and traffic similarity and fuses with historical and weather data, inputs into the spatiotemporal graph neural network prediction model, outputs probability distribution prediction value and prediction confidence interval and issues, edge control node constructs multi-objective optimization model based on prediction uncertainty, generates dynamic allocation table and backup scheme under capacity constraint to minimize charging time and maximize renewable energy consumption as the target, issues allocation table to execute power allocation and obtains actual power feedback, when the actual power exceeds the confidence interval range, switch backup scheme to preferentially reduce flexibility load. The present application effectively improves the deficiency that single-point prediction lacks defense, realizes reliable global dynamic load balancing under the premise of guaranteeing the safe operation of power grid transformer.

This invention belongs to the field of power system optimization and scheduling technology for multi-microgrid systems, and particularly relates to a method and system for collaborative optimization scheduling of active distribution networks and multi-microgrids. It includes establishing a multi-entity shared energy storage operation framework; considering the bilateral uncertainties of active distribution networks and multi-microgrids, introducing adjustment costs and default penalties to quantify performance risks, and designing multi-timescale operation strategies for shared energy storage operators; establishing a multi-entity collaborative scheduling model based on Nash game theory, and decomposing the collaborative scheduling problem into two sub-problems: maximizing social benefits and allocating cooperative revenue; and introducing an ADMM distributed solution algorithm based on consensus variables and event-triggered communication to solve the two sub-problems, thereby achieving collaborative optimization scheduling among multiple entities. This invention improves the utilization efficiency of cross-layer energy storage resources and the level of renewable energy consumption, reduces system operating costs, and enhances the performance reliability of leasing plans under uncertain scenarios, while protecting the data privacy of multiple entities.

The application discloses a power distribution network multi-party collaborative planning method and system based on reinforcement learning and double-layer game, constructs a double-layer Stackelberg game framework, sets a distribution network operator (DSO) as an upper-layer decision maker, sets a union (MSC) composed of distributed power, energy storage and users as a lower-layer follower, and describes a multi-party competition and cooperation relationship; heterogenous reinforcement learning algorithms are innovatively adopted; a proximal policy optimization (PPO) algorithm is adopted for discrete planning decisions (such as line and equipment site selection) of the DSO; a deep deterministic policy gradient (DDPG) algorithm is adopted for continuous operation decisions (such as capacity configuration and demand response) of the MSC; two intelligent agents are collaboratively trained through a hierarchical iterative game mechanism, and finally converge to an equilibrium strategy. The application can effectively coordinate the interests of all parties, guarantee the safety of the power grid, and improve the overall economy and renewable energy consumption capacity of the system.

The application belongs to the technical field of power system dispatching and control, and discloses an electrolytic cell three-state switching wind-solar-hydrogen storage hybrid system model predictive control method, comprising the following steps: in view of the problems of strong output uncertainty and difficulty in accurately modeling the multi-state operation characteristics of electrolytic cells under the background of high proportion of wind-solar energy access, a refined electrolytic cell model containing shutdown, standby and hydrogen production three-state switching logic is constructed; taking the minimization of the system comprehensive cost as the optimization target, considering the storage battery charging and discharging cost, the electric hydrogen production system operation cost, the power grid power purchase cost, the renewable energy operation and maintenance cost, the wind and light punishment cost, and the hydrogen sales revenue, a rolling optimization scheduling model is constructed; based on the system power balance equation, a predictive control optimization model is established to realize rolling optimization prediction. The above model predictive control method effectively improves the system operation economy and the renewable energy consumption capacity, and is suitable for power system dispatching scenarios containing high proportion of wind-solar resources.

The application relates to the technical field of energy storage, and discloses a distributed photovoltaic energy storage matching and operation method based on artificial intelligence, which comprises the following steps: acquiring initial multi-source data sets of a distributed photovoltaic system on the user side; based on the initial multi-source data sets, performing feature engineering and gradient boosting tree classification model processing to obtain a matching necessity probability value and to judge whether the user side needs matching; when the user side needs matching, performing optimal energy storage configuration on the distributed photovoltaic system by using a bidirectional long short-term memory network model and an improved genetic algorithm, and obtaining a next-day prediction data set and a target configuration parameter set; based on the target configuration parameter set and the next-day prediction data set, performing dynamic self-adaptive regulation and control method processing to obtain a target charging and discharging strategy of the distributed photovoltaic system; and using the target charging and discharging strategy to control the operation of the distributed photovoltaic system, so that the matching operation result is obtained, and the renewable energy consumption efficiency and comprehensive income of the distributed photovoltaic matching project are improved.

This invention discloses an active distribution network optimization scheduling method based on a multi-objective attention strategy network, comprising the following steps: S1, constructing an active distribution network optimization scheduling framework; S2, establishing an active distribution network scheduling objective function, the objective function being the minimization of total operating cost; S3, transforming the active distribution network scheduling problem in step S2 into a Markov decision process, and performing active distribution network optimization scheduling based on a near-end policy optimization algorithm combined with a multi-objective attention strategy network and a multi-scale reward function. This invention can adaptively extract key features from both the source and load sides according to the real-time operating status of the system, dynamically adjust the focus of scheduling decisions, and balance conflicting objectives such as economic cost, environmental impact, user satisfaction, and renewable energy consumption through a multi-scale reward function, ultimately outputting a collaboratively optimized source-load scheduling strategy, thereby improving the overall operating performance of the active distribution network.

This invention belongs to the field of new energy and hydrogen energy, and specifically relates to an integrated energy vessel, the "Dongfang Heping," for direct seawater hydrogen production, storage, and conversion. The integrated energy vessel of this invention includes a powered platform, on which are installed a wave energy system, a photovoltaic system, a wind power system, a seawater desalination direct electrolysis hydrogen production system, a green methanol synthesis system, a green ammonia synthesis system, and a power system. The wave energy system, photovoltaic system, and wind power system are used to extract energy from the ocean and convert it into electrical energy to provide power to the powered platform, the seawater desalination direct electrolysis hydrogen production system, the green methanol synthesis system, and the green ammonia synthesis system. The power system drives the powered platform, and the seawater desalination direct electrolysis hydrogen production system, the green methanol synthesis system, and the green ammonia synthesis system are electrically connected to the power system. This invention provides a comprehensive energy ship system that integrates multiple renewable energy sources such as photovoltaic, wind power, and wave energy. It constructs a power platform at sea that integrates the production, storage, transportation, and utilization of "oceanic green hydrogen," forming an integrated energy production and conversion model of "green electricity-green hydrogen-green ammonia and alcohol." This not only meets the shipping industry's demand for green fuels but also effectively solves the problem of renewable energy consumption, promoting the deep integration of transportation and energy.

The present application relates to renewable energy consumption and hydrogen energy storage and supply technology, and aims to provide a hydrogen synthesis storage and supply system and method for fluctuation smoothing and spontaneous power generation. The method comprises: using a compressor unit and a hierarchical energy storage device to replace a storage battery, replacing the charging and discharging process of the traditional battery technology by a hydrogen storage-hydrogen use process; controlling the operation number of the self-compressor unit according to the remaining available power after photovoltaic power generation and hydrogen production, and switching the hydrogen source according to the operation state of the pressure potential recovery power generation unit; using the relatively low power operation of the compressor unit and the inter-stage air supplement-high pressure storage strategy to improve the photovoltaic utilization rate under the non-rated power full-stage operation condition; and recovering the pressure potential in the hydrogen storage container to generate power, thereby achieving the power supplement of the chemical production device. The present application can greatly reduce the maintenance cost, has no electrochemical decay and thermal runaway risk, significantly improves the photovoltaic utilization rate in the whole life cycle, does not involve chemical conversion, and has very high efficiency.

The application belongs to the technical field of unmanned aerial vehicle task offloading, mobile edge computing and micro-grid energy management, and discloses a kind of fusion modeling method of unmanned aerial vehicle task offloading optimization and micro-grid scheduling, which is suitable for micro-grid environment containing new energy and hybrid energy storage.For the problems of independent task and energy scheduling, insufficient cross-time scale cooperation, weak uncertainty response capability and other problems in the prior art, the application constructs a "computing-energy-communication" trinity cooperation system and a "cloud-edge-end" hierarchical architecture, adopts double-time scale scheduling, introduces opportunity constraints to handle uncertainty, divides task priority and supports multiple types of unmanned aerial vehicles and charging methods, and solves the joint optimization model through Actor-Critic network and MPC cooperation.The application realizes the deep coupling of efficient task execution and economic and stable operation of micro-grid, significantly improves the task completion rate and renewable energy consumption rate, and is suitable for power grid inspection, disaster emergency response and other scenes.

This invention discloses an AI-based intelligent energy dispatching and management system based on prediction, scheduling, and diagnosis. The system includes a photovoltaic power generation unit, a hybrid energy storage unit, a thermal energy storage unit, a hydrogen energy storage and conversion unit, an AI intelligent monitoring unit, an AI intelligent management unit, a power grid and load unit, and a cloud platform. The system employs a long short-term memory network to achieve accurate ultra-short-term photovoltaic power prediction, constructs a dynamic optimization scheduling strategy with multiple time scales and energy flows based on deep reinforcement learning, and utilizes a one-dimensional convolutional neural network for real-time fault diagnosis and health management of key equipment. Through the synergy of electricity, heat, and hydrogen energy storage and AI intelligent scheduling, the system significantly improves the self-consumption rate of photovoltaic power generation and system energy efficiency. It possesses intelligent features such as self-sensing of state, self-optimization of scheduling, and self-diagnosis of faults, providing an efficient, reliable, and economical solution for high-proportion renewable energy consumption.

This invention relates to a data-driven, multi-agent energy and carbon collaborative decision-making method for low-carbon industrial parks. It constructs a multi-agent dynamic Stackelberg game model, incorporating time-varying grid-side carbon intensity and the carbon offsetting benefits of green electricity into the multi-agent game decision-making process. Energy suppliers act as leaders, and users as followers, aiming to minimize the sum of economic energy consumption costs and carbon costs calculated based on dynamic carbon factors to solve for the optimal energy consumption strategy. A hybrid intelligent solution framework is used to solve the multi-agent dynamic game model. At the game strategy learning level, an improved multi-agent deep reinforcement learning algorithm is employed to approximate the game equilibrium. At the individual optimization level, an improved multi-objective particle swarm optimization algorithm is embedded within the follower agent to solve for the follower's optimal response under the current price strategy. Compared with existing technologies, this invention can coordinate the interests of multiple agents with dynamic carbon objectives, promoting source-load synergy and efficient renewable energy consumption.

The application discloses a kind of park multi-energy collaborative comprehensive energy supply control methods based on carbon constraint, comprising: according to power grid carbon intensity, time-of-use electricity price and renewable energy output prediction, construct three-dimensional carbon potential energy surface, identify carbon potential peak and valley, determine low carbon potential energy valley period and high carbon potential energy peak period;In high carbon potential energy peak period, through mixed integer linear programming model, with the weighted sum of operating cost and carbon emission cost minimum as objective function, generate the carbon economy benchmark trajectory of future first cycle;With benchmark trajectory as target, through model predictive control to carry out second cycle rolling optimization, output correction control instruction;Real-time monitoring carbon intensity mutation, trigger the output adjustment of depth reinforcement learning agent to be superimposed to correction instruction, simultaneously according to carbon potential energy elasticity coefficient trigger advance collaborative mode.In the guarantee system operation economy, significantly reduce the power grid carbon emission of high carbon period, improve renewable energy consumption level.

The present application relates to a kind of rural photovoltaic and hydrogen energy storage collaborative operation optimization method considering source load uncertainty, belong to energy system optimization technical field.The present application will be reasonably physically connected by rural unit and hydrogen energy storage system through electricity-hydrogen coupling facility (electrolytic cell and hydrogen fuel cell), form rural photovoltaic and hydrogen storage combined system, to give full play to complementary advantage and synergistic effect.However, forming the above system means, in addition to source side photovoltaic random output, at least the interference of load side uncertainty, such as electric load, to operation.In view of this, it is necessary to explore the scientific optimization method of rural photovoltaic and hydrogen storage combined system operation scheduling to support its economic, low carbon, stable operation and renewable energy consumption under the interference of source and load bilateral uncertainty, guarantee the reasonable realization of the above potential benefits.

This invention discloses a charging module energy efficiency optimization scheduling method based on big data prediction, comprising: collecting multi-source heterogeneous data related to charging load and grid status, performing preprocessing and feature fusion to generate a spatiotemporally correlated fused dataset; training and applying a big data prediction model based on the fused dataset to output regional charging demand prediction results and grid load prediction results within the future scheduling cycle; inputting the regional charging demand prediction results, grid load prediction results, and renewable energy output prediction data into a collaborative scheduling optimization model established for multiple charging modules; solving the collaborative scheduling optimization model to obtain an optimized scheduling instruction sequence for the start / stop status and output power of each charging module within the future scheduling cycle, so as to execute start / stop control and power allocation for each charging module. This invention can achieve improved charging energy efficiency, extended equipment life, and increased renewable energy consumption rate.

This invention discloses a flexible coordinated control method and device for source-grid-load-storage for renewable energy consumption, belonging to the field of power system dispatching technology. The method includes: constructing a consumption operation state vector; dividing each energy source into levels according to the consumption operation state vector, and generating a multi-level consumption chain topology model based on hierarchical directed connections between multi-level consumption layers; generating a multi-level consumption path sequence space corresponding to the multi-level consumption chain topology model; introducing a hierarchical capacity transfer function to calculate the multi-level consumption capacity coupling relationship between each level, and performing feasibility constraint screening on the generated multi-level consumption path sequence space through the multi-level consumption capacity coupling relationship; and executing cascade triggering control according to the set of consumption execution paths based on a preset multi-level consumption cascade triggering mechanism. This application solves the problems of disordered renewable energy consumption dispatching, ambiguous hierarchical capacity transfer relationships, lack of quantitative screening of consumption paths, and delayed response of cascade control in existing technologies, which can easily lead to grid operation risks.

A method for modeling equivalent virtual energy storage in mines, considering flexible adjustments to equipment and processes, comprises the following steps: constructing a mine energy system architecture incorporating virtual energy storage; establishing virtual energy storage models for mine transportation, drainage, and compressed air processes; establishing equivalent electrical energy storage models; and establishing maintenance virtual energy storage models based on equipment maintenance needs; integrating power flow constraints, power balance constraints, power supply upper and lower limit constraints, and production safety constraints to establish a comprehensive mine energy system operation model; and using the GUROBI solver to solve the model and obtain the optimal scheduling scheme. This invention fully leverages the flexible adjustment potential of mine production processes, achieving economically optimized scheduling and efficient renewable energy consumption while meeting strict safety constraints and production continuity requirements. It reduces energy costs for mining enterprises while increasing the proportion of green electricity in the overall electricity mix, reducing carbon emissions, and improving energy efficiency, thus aligning with the low-carbon transformation needs of mines.

This disclosure presents a disturbance rejection control method and device, electronic equipment, and storage medium for a gas storage chamber. Through this application, a global fuzzy model is constructed based on historical input-output data of the gas storage chamber pressure control system and transformed into a non-minimum state-space model. State variables are directly constructed using the input-output data without reconstructing unmeasurable state variables. Simultaneously, feedback control components and disturbance compensation components are designed to synthesize target control commands. Therefore, this addresses the system stability problem caused by the dynamic coupling of state estimation errors and feedback control in traditional state-space models, which rely on unmeasurable state variables and require reconstruction through observers. This, in turn, restricts the safe and efficient operation of compressed air energy storage power stations. The goal is to improve the operational stability of the gas storage chamber pressure control system, reduce control errors, and thus ensure the safe and efficient operation of compressed air energy storage power stations, better adapting to grid peak shaving and renewable energy consumption needs.

The application relates to the field of combined cooling, heating and power system planning, and discloses a park combined cooling, heating and power system operation condition calculation method and system based on annual hour-by-hour analysis, which comprises the following steps: constructing a park combined cooling, heating and power system model with a given configuration, and obtaining annual hour-by-hour data sequences; taking maximum local renewable energy consumption as the primary operation target, and performing hour-by-hour energy balance simulation; for each simulation period: using the renewable energy power generation in the period to directly meet the local power load demand; if there is surplus renewable energy power generation, the energy storage system is preferentially charged; if the renewable energy power generation cannot meet the load demand, the energy storage system is preferentially discharged to supplement; when the combined output of the renewable energy and the energy storage system still cannot meet the load, the power shortage is balanced by purchasing power from the power grid; and based on the energy balance simulation result, the total operation electricity fee of the system in the calculation period is calculated. The application can realize accurate operation electricity fee calculation.

This invention relates to an integrated sensing, computing, and communication method, device, and system for improving the observability of distribution networks, belonging to the field of telecommunications technology. The method includes constructing a system model, establishing an integrated sensing, computing, and communication optimization problem with the goal of maximizing three-dimensional observability, decomposing it into three sub-problems and solving them separately; simultaneously designing an integrated sensing, computing, and communication IoT device, an edge computing device, and a three-level collaborative system comprising a terminal layer, an edge layer, and a cloud layer. The terminal layer completes distribution network status perception and dynamic frequency adjustment, the edge layer realizes data transmission and optimized scheduling of computing resources, and the cloud layer provides global optimization decision-making and service support. This invention achieves a comprehensive improvement in the three-dimensional observability of distribution network data, time, and space, enhancing perception accuracy, communication reliability, and computational timeliness, adapting to the dynamic operation requirements of distribution networks with high penetration of distributed renewable energy, and providing core support for the safe and stable operation of distribution networks, renewable energy consumption, and economic dispatch.

The application relates to an energy system control method, an electronic device, a storage medium and an energy system, and belongs to the technical field of energy scheduling and control, wherein the method comprises the following steps: acquiring scheduling prediction data of an energy system, the system comprising an energy storage device and an energy supply device, the energy storage device comprising a virtual energy storage device; based on the scheduling prediction data, taking the energy storage and energy release power of the energy storage device and the energy generation power of the energy supply device as decision variables, taking the maximization of renewable energy consumption and the minimization of the total operation cost of the energy system as the target, and constructing a multi-objective optimization scheduling model; solving the multi-objective optimization scheduling model, determining an optimal device operation scheme, and controlling the energy storage device and the energy supply device based on the optimal device operation scheme. The application breaks the forced binding of cold and heat loads to local power generation loads by reusing existing sewage ponds, sludge digestion ponds and other facilities of a sewage plant, and realizes flexible decoupling.

This invention discloses a unit combination optimization method and system. Based on the output characteristics analysis of different types of generating units, a mathematical model of each unit in the power system is constructed. The baseline state of each unit is obtained from the mathematical model. Building upon existing unit combination research, this method balances the goals of renewable energy consumption and optimal economic cost, comprehensively considering the unit itself, the system, and constraints. It improves computational efficiency while enhancing the matching degree between the unit combination plan and the grid transmission capacity, reserving space for economic dispatch and safety verification. A heuristic algorithm with low computational complexity and ease of operation provides a feasible approach to the above objectives. By arranging the start-up, shutdown, and output states of the units, the method effectively avoids the solution process of mixed integer programming problems, improving the computational efficiency of long-cycle unit combination problems in large-scale practical power systems, reducing computation time from several hours to a few minutes.

This invention discloses a method and system for power planning and allocation in receiving provinces that considers uncertainties. The method includes: formulating a basic power supply plan; constructing a probabilistic scenario set of annual hydropower utilization hours based on historical water inflow data; setting green and low-carbon targets and calculating the inter-provincial renewable electricity volume required to meet the renewable energy consumption responsibility weight under different scenarios; establishing an inter-provincial transaction cost model to calculate the cost per kilowatt-hour and total transaction cost for each scenario; constructing a system annual operating cost model to obtain the total system cost under each scenario and then averaging it to obtain the expected total cost; and iteratively optimizing the installed capacity of new energy sources with a fixed increment until convergence, outputting the economically optimal solution. This invention achieves a coordinated balance between the economy, greenness, and security of power allocation by probabilistically handling hydropower uncertainties and introducing inter-provincial green electricity trading as an optimization variable.

The present application belongs to the technical field of energy internet and low-carbon power system, and provides a method for solving the coupling and optimization of electric-carbon-green three markets with repeated accounting. The present application introduces the marginal emission factor of new energy units, and dynamically calculates a variable carbon-green mutual recognition coefficient. The coefficient reflects the actual emission reduction contribution of new energy output in different time periods. Based on the coefficient, the environmental rights and interests corresponding to renewable energy are mutually exclusive allocated to two paths: part of the green power certificates directly enters the green power certificate market, and the other part is converted into CCER the carbon market according to the coefficient, thereby eliminating repeated accounting from the source. Based on the mutual recognition result, the comprehensive carbon quota price and the comprehensive green power certificate price considering the supply and demand relationship are calculated respectively. Further, the two prices are coupled into the electricity market through the system marginal emission factor and the renewable energy consumption responsibility weight, forming the comprehensive electricity price in each time period, and providing a unified, real and dynamic price signal for market participants.