79 results about "Economic gain" patented technology

The invention belongs to the technical field of wind turbine generator operation and maintenance, and discloses a wind turbine generator intelligent operation and maintenance decision-making system based on big data and fault prediction. Comprising the following steps: analyzing the residual service life of a key part based on a constructed rule system and acquired related data of a wind turbine generator, and acquiring probability density distribution of the residual service life of the key part; the analysis module is used for generating an expected economic loss curve through the probability density distribution of the remaining service life of the key component, acquiring the total execution cost of each feasible maintenance window based on the expected economic loss curve, and analyzing the accumulated risk cost acquired based on the generated feasible maintenance list; according to the method, the net present value of each feasible maintenance window is determined, an operation and maintenance decision suggestion list is generated, an operation and maintenance work order is automatically distributed after a maintenance scheme is selected based on operation and maintenance decision suggestions, maintenance opportunity selection is improved from experience judgment to a quantifiable economic optimization problem, and asset economic benefits are maximized while equipment reliability is guaranteed.

The invention belongs to the technical field of power system automation and energy storage regulation and control, particularly relates to a thermal power energy storage frequency modulation auxiliary service benefit maximization method, and aims to solve the problems of disjunction between technical response and economic benefit, large equipment loss and poor market adaptability in a traditional frequency modulation strategy. According to the method, a multi-time-scale instruction analysis model is constructed, thermal power coal consumption and energy storage loss characteristics are fused, a dynamic cost-income coupling function is established, and thermal storage power is distributed in real time based on a rolling time domain optimization algorithm; and dynamically correcting model parameters by executing feedback and an online learning mechanism, and synchronously generating a market bidding strategy and benefit pre-judgment report. The system supports state-of-charge boundary adaptive adjustment and equipment collaborative aging evaluation, and is deployed at an edge computing node to realize millisecond-level response. According to the invention, the economic benefit of frequency modulation is obviously improved by 15-25%, the score of frequency modulation performance is more than 95, the energy storage life is prolonged by more than 30%, the fluctuation of unit income is reduced by 40%, and full-link closed-loop intelligent regulation and control and market competitiveness enhancement are realized.

The invention discloses a loss calculation method and system for transformers with different energy consumption levels, and belongs to the technical field of transformer loss judgment, and the method comprises the following steps: S1, parameter acquisition; s2, calculating a load rate; s3, obtaining a time period division condition and an average electricity price parameter; and S4, calculating the comprehensive loss electric charge. The method provides core economic support for energy efficiency decision by accurately quantifying dynamic operation loss of transformers with different energy efficiency levels, and comprises the following steps: during new equipment type selection, calculating a full life cycle cost balance point of first-level and second-level energy efficiency equipment based on load characteristics and an electricity price mechanism so as to obtain an optimal cost performance scheme; when stock equipment is replaced, the actually measured data of the high-energy-consumption transformer is compared with the first-level energy efficiency limit value and the second-level energy efficiency limit value, technical parameter-economic benefit linkage analysis is achieved, the investment risk is remarkably reduced, and efficient execution of the energy efficiency policy is promoted.

The invention is suitable for the technical field of photovoltaic subsidy strategies, and provides a photovoltaic subsidy strategy optimization method, which comprises the following steps: S1, constructing a three-dimensional niche resource space, determining game core elements, and constructing a distributed photovoltaic promotion scene based on a distributed photovoltaic promotion scene; a traditional single policy subsidy dimension is upgraded to a policy support, cooperation mode and economic benefit three-dimensional benefit resource space, and a precise decision coordinate system is provided for a tripartite game; s2, a third-order dynamic game model is established at the same time, a three-party revenue function is quantified, and the decision sequence of the government, the enterprise and the farmer is taken as a logic chain; according to the photovoltaic subsidy strategy optimization method, for the government, the subsidy fund utilization rate is increased by 30%-40%, the indirect cost proportion is smaller than 30%, and the dual-carbon target is met; for enterprises, the requirements of three types of modes are met, the profit prediction accuracy is improved by more than 40%, and the market coverage is expanded by 50%; and for peasant households, multiple modes adapt to different requirements, the adoption rate is increased by 40%-50%, the annual income fluctuation rate is smaller than 5%, three-party collaborative ecology is constructed, and the dilemma of'policy slop falling, namely stagnation 'is solved.

The invention discloses a multi-virtual power plant low-carbon retail pricing method based on a non-cooperative game, and belongs to the technical field of power system optimization and energy market transaction, and the method comprises the following steps: building a control model of a single virtual power plant; 2, correcting a dynamic carbon emission factor; 3, deducing the optimal retail electricity price of the plurality of virtual power plants; according to the invention, a multi-virtual power plant non-cooperative game pricing model based on dynamic carbon emission factor-economic benefit double chains is established, a value chain of low-carbon excitation and economic benefit is broken through, an accurate low-carbon benefit acquisition mode is created for the virtual power plant, the virtual power plant is promoted to preferentially dispatch renewable energy sources, and the economic benefit of the virtual power plant is improved. And the low-carbon operation level and the market competitiveness of the regional power system are effectively improved.

The present application relates to a kind of distributed new energy access and scheduling comprehensive optimization method, belong to distribution network technical field;The method comprises: S1: obtain containing mass distributed energy monitoring system historical data, obtain regional historical load data, obtain regional historical environmental governance cost, power generation cost, reliability and toughness weight index;S2: utilize historical data initialization different types distributed energy generation proportion and margin;S3: utilize S1 different types distributed energy monitoring system historical data to predict future 24 hours power generation power data;S4: utilize predicted 24 hours power generation power data, historical environmental governance cost, power generation cost, reliability and toughness weight index and distributed energy generation proportion and margin, construct distributed energy environmental governance cost, power generation cost, reliability and toughness multi-objective and comprehensive cost single target optimal scheduling model and constraint condition;S5: with long-term operation optimal as target, relevant data is substituted into multi-objective and single target scheduling model, by solving, to obtain different types distributed energy long-term generation proportion and power generation margin, simultaneously generate short-term power generation trend constraint factor;S6: with short-term operation optimal as objective function, combine abnormal disturbance factor and trend constraint factor, solve short-time multi-objective and single target optimal function, to obtain the generation proportion and power generation margin of different distributed energy in short time;The substantial significance of the present application is: under the premise of guaranteeing distributed energy normal stable output and reasonable power generation margin, through the comprehensive optimization scheduling of different distributed energy, realize overall new energy reliable complementary optimization operation and comprehensive economic benefit maximization.

This invention provides a method and system for evaluating the economic benefits of distributed resource projects throughout their entire lifecycle, involving multiple factors. The method includes: constructing a multi-dimensional basic parameter library for distributed resource projects; collecting operational simulation data of the projects based on the basic parameter library; establishing a multi-factor coupled full-cycle economic benefit evaluation model based on the basic parameter library and operational simulation data, and obtaining the basic benefit calculation result through the coupled calculation of the evaluation model; incorporating financing and loan parameters to dynamically correct the basic benefit calculation result, obtaining the corrected benefit calculation result under the loan scenario; conducting a technical and economic integrated selection evaluation of equipment from multiple manufacturers, and outputting the selection evaluation result; integrating the basic benefit calculation result, the corrected benefit calculation result, and the selection evaluation result to output a refined full-cycle evaluation result and generate a visual report. This invention enables accurate and comprehensive evaluation of the economic benefits of photovoltaic-storage projects throughout their entire lifecycle, providing a reliable basis for investment decisions and scheme optimization.

The invention discloses a cooperative game-based water resource allocation method and system under a hierarchical water network, belongs to the technical field of water resource allocation, and can solve the problem of low allocation efficiency. The method comprises the following steps: constructing a hierarchical water network structure; constructing a water resource configuration model of the current-level water network for supplying water to the plurality of current-level administrative regions according to the plurality of water consumption types of each current-level administrative region; performing water resource joint allocation on the plurality of current-level administrative regions by using the water resource allocation model to obtain a plurality of allocation schemes, and determining cooperative economic benefits of each current-level administrative region in each allocation scheme; performing independent water resource allocation on each current-level administrative region by using a water resource allocation model to obtain independent economic benefits of each current-level administrative region; and determining the compensation benefit of each current-level administrative region in each configuration scheme according to the cooperative economic benefit and the independent economic benefit, and determining an optimal scheme according to the compensation benefit. The device is used for preparing water resources.

The application discloses a lightning algorithm-based photovoltaic module cleaning period optimization method and related device, and relates to the field of photovoltaic module cleaning period optimization.The method comprises the following steps: establishing a rain cleaning factor, an installation height cleaning factor and an installation angle cleaning factor based on rainfall, photovoltaic module installation height and photovoltaic module installation angle; obtaining power generation loss and cleaning and maintenance cost factors in the cleaning period according to electricity price, daily effective operation time, power station installed capacity and dust accumulation condition; establishing a target model with total cleaning economic benefits of the photovoltaic module as the target; and solving the target model by using a lightning algorithm to obtain the optimal cleaning period.The application comprehensively considers the influence of rainfall, installation height and installation angle on the cleaning period of the photovoltaic module, combines the economic influence of power generation loss and cleaning and maintenance cost in the cleaning period on the cleaning of the photovoltaic module, establishes a total cleaning economic benefit target, and obtains the optimal cleaning benefit method by using the lightning algorithm, so that the cleaning period of the photovoltaic module can be comprehensively obtained.

The invention discloses a virtual power plant profit-driven optimal scheduling method, system and device and a medium, and belongs to the technical field of power system optimization, and the method comprises the steps: collecting profit-driven data and power plant data of a virtual power plant; constructing an optimal scheduling model based on the profit-driven data, and setting constraint conditions; preprocessing power plant data, and selecting a fitting model to predict a power generation mode. According to the invention, a complete closed loop from data prediction to optimal scheduling is realized. The prediction part provides accurate input for the optimization model, and the optimization part utilizes the input to formulate a profit-driven scheduling strategy, so that the economic benefit of the VPP is improved, the safety and reliability of system operation are ensured, the competitiveness and adaptability of the VPP are enhanced in an electricity market environment, and the economic benefit of the VPP is improved. The problem of tradeoff between economical efficiency and reliability in traditional scheduling is avoided, win-win is achieved, and the method has remarkable industrial application value.

The invention belongs to the technical field of micro-grids, and particularly relates to a micro-grid multi-mode cooperative control method, system and device and a storage medium. The objective of the invention is to solve the technical problem in the prior art that the rapid adjustment capability of a large amount of distributed photovoltaic power and the supporting capability of energy storage cannot be converted into economic benefits participating in market transactions, so that huge waste of resource values is caused. Comprising the steps of obtaining electricity market data and prediction data; establishing a daily operation plan by taking daily operation income maximization as a target; refining the daily operation plan into a power set point sequence, and generating a dynamic scheduling instruction; a multi-mode cooperative controller is used for collecting state data of a common connection point in real time, and in combination with a current market signal, according to built-in mode switching logic, a working mode which should be operated currently is intelligently decided; converting the dynamic scheduling instruction into a bottom layer control parameter of each inverter, and issuing the bottom layer control parameter to physical layer equipment; and controlling physical layer equipment to operate according to the bottom layer control parameters.

The invention provides an economic dispatching method and system for a steel smelting production process under demand response, and belongs to the technical field of steel industry load optimization dispatching. The method comprises the following steps: establishing an objective function of price and incentive joint demand response according to power demand response; establishing a state task network model of the iron and steel smelting production process according to the iron and steel smelting process flow; wherein the state task network model is used for representing a coupling relationship between a material flow and a process; establishing production constraint conditions of steel smelting according to the state task network model; and jointly solving the objective function and the production constraint conditions to obtain a scheduling scheme of the steel smelting production process. The method can fully excavate the potential of the industrial load participating in the demand response, effectively optimizes the production scheduling scheme of steel smelting under the demand response, and achieves the balance of economic benefit, production flow and production safety.

The invention discloses a portable terminal and a method for measuring and calculating economic benefits of a smart greenhouse in a full life cycle, belongs to the field of smart agricultural hardware, and aims to solve the problems of insufficient coverage, data breakage, low accounting efficiency and difficult recovery of full-stage measurement and calculation of existing equipment through full-cycle exclusive hardware design. The portable terminal comprises a main body protection shell, an economic parameter interaction assembly, an income data acquisition assembly, a hardware calculation assembly and a power supply assembly, and all the assemblies cooperate to adapt to a full-life-cycle economic income measurement scene of planting preparation, growth management, harvesting and selling, and operation redisk of the intelligent greenhouse. After the method is applied, the complete-cycle data integrity of the greenhouse is 100%, the annual average income error calculation is reduced by 70%, the annual average income increase of a five-greenhouse tomato base exceeds 926000 yuan, the return on investment is shortened to 2-3 months, data can be stored in a structured mode, and planting optimization is supported.

The invention relates to the technical field of power equipment monitoring systems, in particular to a power equipment parameter real-time collecting and monitoring system and method, and the method comprises the steps: carrying out the real-time collection of multi-dimensional operation parameters of target power equipment, and carrying out the real-time collection of the multi-dimensional operation parameters of the target power equipment based on the real-time collected scheduling characteristic parameters; dynamically evaluating the schedulability of the target power equipment by using a pre-constructed equipment schedulability evaluation model; based on the schedulability evaluation result, preset electricity price time period information and production plan information, optimization strategy deduction is carried out, and load optimization suggestions containing quantitative economic benefits are generated; according to the invention, the operation state of the power equipment can be collected and monitored, the operation state of the power equipment is dynamically adjusted on the premise of guaranteeing production, and the electric charge cost is effectively reduced.

This invention discloses a method, device, and software product for optimizing the revenue of a wind-power hybrid energy storage system across multiple markets. First, based on a market compensation mechanism, a multi-market revenue calculation model is constructed, clarifying the allocation mechanism of the winning capacity in each type of market and calculating the capacity activation coefficient for each type of frequency regulation service. Building upon this, a battery degradation cost penalty mechanism based on cumulative energy throughput is introduced, transforming implicit lifespan losses into explicit economic costs embedded in the optimization objective, thus achieving a trade-off between economic benefits and equipment lifespan. Furthermore, a unified mixed-integer linear programming model is used to achieve integrated collaborative optimization of day-ahead capacity bidding and real-time power allocation. Ultimately, while ensuring the physical operational safety of the system, the method significantly improves the revenue aggregation capability and life-cycle economics of the wind-power hybrid energy storage system in multi-level electricity markets.

This invention discloses a method and system for coordinated optimization of carbon emissions in a multi-park integrated energy system, belonging to the field of integrated energy optimization and scheduling technology. The method includes: constructing a dynamic carbon trading and green certificate flexible offsetting mechanism; building a two-layer game optimization model with the system operator as the leader and multiple park agents as followers; the upper-layer system operator optimization model uses the DQN algorithm to dynamically optimize and publish energy trading prices; the lower-layer park agent optimization model uses the PPO algorithm to perform distributed optimization scheduling of equipment within the park based on the received energy trading prices; improving the Shapley value benefit allocation method based on carbon emission reduction contributions to fairly distribute the total alliance benefits generated by the coordinated operation of multiple parks; and conducting collaborative iterative training of the two-layer game optimization model until game equilibrium is reached, outputting the optimal energy trading price and scheduling strategies for each park. This invention achieves a synergistic improvement in both the overall economic benefits and carbon emission reduction levels of the system.

The invention provides an energy storage scheduling method based on deep reinforcement learning, a program product and equipment. The energy storage scheduling method comprises the following steps: at each scheduling decision moment, acquiring real-time operation data of an energy storage system and external time sequence data associated with the scheduling decision moment; performing feature processing on the external time sequence data, and fusing the external time sequence data with the real-time operation data to form an input state vector of the deep reinforcement learning model; constructing a comprehensive reward function including an economic benefit term, a battery attenuation cost penalty term and a battery health safety constraint penalty term; and inputting the input state vector into the deep reinforcement learning model, taking maximization of the comprehensive reward function as a training target, driving the deep reinforcement learning model to carry out strategy learning, and outputting a charging and discharging strategy corresponding to a scheduling decision moment. According to the invention, the charging and discharging strategy can dynamically balance the real-time income, the full-life-cycle loss of the battery and the safety risk, and the full-dimension comprehensive optimal scheduling of the system is realized.

This invention discloses a cogeneration method integrating predictive maintenance and deep waste heat recovery, belonging to the field of cogeneration technology. The method first collects real-time system operation, status monitoring, and external prediction data, and then identifies key performance parameters of the equipment online based on a physical model and adaptive estimation algorithm, generating a real-time health index (HI) and its predicted future degradation trajectory. The core lies in constructing a multi-objective real-time optimization controller, whose objective function integrates short-term operational economic benefits and long-term virtual health costs based on HI predictions, with constraints dynamically correlated with HI. The controller uses a model predictive control framework for rolling solution, outputting optimal coordinated control commands for generator output and deep waste heat recovery components such as flue gas bypass valves and variable frequency pumps. This invention achieves a deep integration of predictive maintenance and operational optimization, significantly improving the overall energy efficiency and economy of the system under all operating conditions while ensuring long-term equipment reliability.

This invention belongs to the field of power market optimization and dispatching technology, and discloses a method and related apparatus for optimizing medium- and long-term trading volume of wind farms. The method includes: acquiring the wind farm output prediction curve and the predicted electricity price curve; solving the wind farm power optimization model based on the wind farm output prediction curve and the predicted electricity price curve to obtain an optimized power allocation scheme for the wind farm in medium- and long-term contracts and the spot market; the wind farm power optimization model aims to maximize the average revenue of the wind farm under various scenarios; the calculation of the average revenue under various scenarios considers the levelized cost of electricity (LCOE) of energy storage charging and discharging throughout its entire lifecycle. The technical solution disclosed in this invention can be used to optimize the allocation of medium- and long-term contract power in the wind farm power market. It considers the optimization capability of wind farms in allocating their own power after the installation of energy storage, and can significantly improve the economic benefits of wind farms.

The invention belongs to the technical field of green hydrogen production and energy system optimization, and provides a green hydrogen system configuration optimization method and system considering carbon emission reduction income. Building a multi-objective optimization model by taking the full-life-cycle comprehensive benefit maximization of the green hydrogen system as an objective function; wherein indexes of the objective function comprise green hydrogen sales income, carbon emission reduction income, whole life cycle investment cost, annual operation and maintenance cost and hydrogen energy storage and transportation cost; solving the multi-objective optimization model by adopting a particle swarm algorithm, and determining a green hydrogen system configuration scheme; economic benefits corresponding to carbon emission reduction in the green hydrogen production process are fully considered, collaborative optimization of economy and carbon emission reduction benefits is achieved, and the problem that green hydrogen project economic feasibility and environmental benefits are difficult to balance is solved.

The invention provides a photovoltaic power station optimization method and device integrating component layout and electricity price response. The method comprises the steps that a collaborative optimization model is applied, real-time electricity price data and a power grid dispatching instruction are combined, and a current space layout parameter of a photovoltaic power station is adjusted, so that the current space layout parameter is adjusted to be a target space layout parameter which enables a first net present value to reach a maximum value; according to the target space layout parameter, determining a space hot spot and a time hot spot when the photovoltaic power station generates abandoned power, and calculating an index value used for carrying out full-life-cycle economic evaluation on the photovoltaic power station; and in response to the condition that the index value does not meet a preset standard, adjusting a spatial layout parameter and / or a power dispatching strategy of the photovoltaic power station in combination with the spatial hot spot and the time hot spot. According to the invention, the economic benefit of the photovoltaic power station can be improved.

The invention provides a multi-objective optimization intelligent ore blending method and system. The method comprises the following steps that a multi-objective function containing an economic benefit objective, a total production amount objective and an ore dressing grade objective is constructed; performing initial optimization of model parameter adjustment on the multi-objective function based on a BP neural network; global optimization is conducted on ore blending parameters output by the multi-objective function after initial optimization through a genetic algorithm, local adjustment is conducted through a particle swarm optimization algorithm, and decision variables are optimized to obtain target ore blending parameters. By constructing a comprehensive objective function system, the problem that it is difficult to collaboratively optimize multiple objectives in a traditional ore blending method can be solved; forming a complete optimization link from global breadth search to local fine tuning through a BP neural network, a genetic algorithm and a particle swarm optimization algorithm; a dynamic prediction and real-time adjustment mechanism based on a long and short term memory network is introduced, and prospective planning and self-adaptive optimization can be carried out according to the ore grade change trend.

The invention relates to the technical field of electricity markets, in particular to a day-ahead-real-time rolling pricing method and system for an electricity selling company, and the method comprises the following steps: S1, constructing an evolutionary game replication dynamic equation of vehicle network interaction of the electricity selling company and an electric vehicle; s2, constructing an evolutionary game income matrix; s3, constructing a day-ahead two-stage pricing optimization model; and S4, constructing a real-time rolling optimization model. According to the method, the interactive behavior with the electric vehicle is simulated through the replication dynamic equation of the evolutionary game, and the economic benefits of the electricity selling company and the electric vehicle user under different cooperation strategies can be effectively predicted and adjusted; powerful theoretical support is provided for establishing a stable cooperation relationship through an evolutionary game income matrix; through constructing a day-ahead two-stage pricing optimization model and a real-time rolling optimization model, an effective price and load management strategy is provided for an electricity selling company in a complex and changeable spot market.

This invention discloses a multi-market collaborative bidding method for virtual power plants from a low-carbon perspective, belonging to the field of multi-market collaborative bidding for electricity. The method includes the following steps: S1, constructing a five-flow coupling model of electricity, carbon, heat, hydrogen, and green certificates, and classifying virtual power plant types to generate a triple uncertainty scenario; S2, building a three-order nested game bidding model; S3, constructing a joint risk management model, outputting risk costs and robust bidding feasible regions; S4, based on the electricity-carbon supply-demand ratio and robust feasible regions, realizing bidirectional matching and pairing transactions of electricity and carbon among multiple virtual power plants, generating initial bidding schemes for the five markets; S5, coupling the game bidding model, risk costs, and initial bidding schemes to obtain the optimal equilibrium bidding solution for the five markets; S6, feasibility verification and closed-loop optimization. Using the above-mentioned multi-market collaborative bidding method for virtual power plants from a low-carbon perspective, the economic benefits and long-term stable operation of the multi-virtual power plant alliance are maximized under the premise of low-carbon compliance.

This invention provides a collaborative decision-making method and system for the entire lifecycle of energy storage based on reverse recursion and a dual-modal strategy. Starting from the end of the time axis, it traverses each continuous time period in reverse along the time axis. Based on the time period characteristic parameters of the current continuous time period and the SOC level corresponding to the start of the continuous time period, it evaluates the lowest cumulative cost of each SOC level at the start time, calculated from different SOC targets at the end time, under the feasible mode of the current continuous time period. Then, based on the cost information of all continuous time periods, it traverses each continuous time period in forward along the time axis, starting from the initial SOC level of the energy storage battery at the initial time. For each continuous time period, it selects the target strategy mode corresponding to the lowest cumulative cost. The selected target strategy modes are then connected in series. Through dynamic switching of dual modes, it takes into account both grid dispatch compliance and economic benefits, and finally achieves collaborative optimization of the entire lifecycle of energy storage over a long time scale.

The invention discloses an accurate weighing direct vibration discharging device for feeding, and belongs to the technical field of accurate feeding of cage-rearing poultry. The accurate weighing direct vibration discharging device is provided with a discharging assembly and a direct vibration hopper flexibly assembled on the lower surface of the discharging assembly; comprising a pressure reduction plate which is installed on the upper side of the inner surface of the discharging assembly, a straight vibrator is installed on the lower surface of the straight vibration hopper, and the straight vibrator is assembled on the side edge of the outer surface of the discharging assembly. According to the accurate weighing straight vibration discharging device for feeding, the straight vibration discharging device is arranged, auger rotary blade rotary conveying is changed into vibration conveying, rotary conveying of feed through auger rotary blades is omitted, noise caused by friction between the rotary blades and the feed is avoided, maintenance of an auger bearing seat is omitted, and the feeding efficiency is improved. And the operation difficulty is lower than that before improvement, motor driving rotation is omitted through vibration conveying, the safety is higher, meanwhile, the learning cost is greatly saved, the efficiency is effectively improved, and more economic benefits are increased.

According to the adaptive energy storage countercurrent control method integrating load edge recognition and electricity price scheduling, the energy storage system is guided to actively release power in the load fluctuation active interval, energy storage resources are fully mobilized, and the energy release efficiency is improved; the scheduling strategy has edge identification and dynamic amplitude limiting capabilities, can actively adjust energy storage response in a high-load fluctuation period, and plays a role in dynamic voltage regulation of a power grid; since the PCS outputs electric energy to replace part of high-price power grid electricity purchasing, the overall operation electric charge expenditure is reduced, and the economic benefit of the system is improved; compared with a traditional scheme, the method has the advantages that higher dynamic adjusting capacity and response stability are shown under the complex load condition, and wider application adaptability is achieved.

The present application relates to a kind of load aggregator operation adjustment method considering environmental rights increment game, comprising: based on the coupling relationship of environmental rights value and electric energy value in green electricity trading market, environmental rights increment game model is built;Combining the technical characteristics and operating constraints of flexible resource, flexible resource operation model is built, and the flexible resource operation model includes equipment operation model and energy coupling model;Based on environmental rights increment game model and the flexible resource operation model, non-cooperative game and collaborative game method are fused, multi-agent game equilibrium is solved, and load aggregator initial operation adjustment strategy is obtained;According to the dynamic change of green electricity trading market scene, environmental rights weight coefficient and electric energy value weight coefficient are dynamically adjusted, and load aggregator optimal operation adjustment strategy is obtained by optimization.Compared with prior art, the present application has realized the collaborative maximization of load aggregator environmental rights increment and economic benefits and other advantages.

This invention relates to the field of virtual power plant technology, specifically to a multi-stage collaborative optimization method, apparatus, equipment, and medium for virtual power plants. In this invention, by establishing a robust min-max optimization model and reducing random scenarios through clustering, the method significantly enhances the risk resistance and reliability of the scheduling scheme in actual operation by introducing uncertain scenarios. Secondly, a deep learning-based surrogate model is introduced to replace the complex original model for evaluation, and a snowmelt algorithm with a dual-population mechanism is used for efficient optimization, greatly improving the computational efficiency of large-scale optimization problems and making it possible to obtain high-quality solutions within a limited time, thus overcoming the bottleneck of computational time consumption in traditional methods. Finally, by scientifically balancing economic benefits and operational risks, this scheme can output a day-ahead plan that maximizes expected profits, thereby effectively improving operational economy and decision-making accuracy in a complex and ever-changing market environment.