19 results about "Bilevel optimization" patented technology

The application discloses a kind of multi-element load linkage regulation method, system, equipment and medium, comprising: constructing the two-stage operation transaction model of multi-element load aggregation main body user, forms lower user response model;Establish two-stage robust regulation model dominated by distribution network operator;Introduce power grid topology adjustable constraint;Coupling user response model and enhanced operator regulation model, build double-layer interactive optimization framework, form two-stage robust double-layer optimization problem containing integer variable;Optimization problem is solved using nested column constraint generation algorithm, output optimal electricity price guide strategy, direct load control amount and topology adjustment scheme, as the total scheme of multi-element load linkage regulation for weak point uncertainty.

This disclosure relates to a multi-objective, two-layer optimization method and apparatus for a multi-regional integrated energy system. The method includes: establishing an upper-layer multi-objective programming model and a lower-layer optimization scheduling model; using the unit capacity of various types of energy supply units as initial values, generating velocity and position expressions for upper-layer particles based on a particle swarm optimization algorithm; solving the expression using a Gurobi solver to generate the daily power output state of various types of energy supply units in the multi-regional integrated energy system; updating the velocity and position expressions of the upper-layer particles based on the upper-layer multi-objective programming model and iteratively calculating; stopping the iteration when preset conditions are met, and calculating and generating a capacity configuration scheme for the multi-regional integrated energy system based on the velocity and position expressions of the upper-layer particles. This disclosure's two-layer optimization strategy incorporates the system's operational characteristics into the planning process, improving the feasibility of the planning scheme; regional energy mutual assistance can optimize the system's operation mode and improve overall efficiency.

This invention relates to an erasure coding method and system based on two-layer optimization. The method includes: obtaining storage parameters of the data to be stored; constructing an initial generator matrix structure and a reference table for characterizing the bit-level characteristics of elements; constructing a two-layer optimization model and using the two-layer optimization model to collaboratively optimize the initial generator matrix structure; the two-layer optimization model includes: an upper-layer optimization model configured to adjust the structural distribution of the generator matrix; and a lower-layer optimization model configured to schedule the bit-level operation paths after the generator matrix expansion based on the reference table and calculate the actual bit-level computational overhead; feeding back the bit-level computational overhead of the lower-layer optimization model to the upper-layer optimization model; and encoding the data to be stored based on the comprehensive evaluation result of the algebraic characteristics and the bit-level computational overhead. This invention achieves the collaborative unification of the generator matrix structure and XOR scheduling optimization through the two-layer optimization model, improving the coding rationality, practicality, and throughput.

This invention discloses a two-tier decision-making method and system for collaborative electricity-carbon trading among building producers and consumers. The method constructs a two-tier optimization framework for collaborative electricity-carbon trading based on the functional responsibilities of upper-level aggregators and the carbon trading positioning of lower-level building producers and consumers. It categorizes individual buildings according to differentiated carbon emission compliance requirements and calculates CCER mutual recognition amounts based on the principle of emission reduction equivalence and the amount of green electricity consumed in each scheduling period. Through dynamic application and allocation by aggregators, a dynamic carbon emission reduction mutual recognition mechanism is constructed. Combining the framework and mutual recognition mechanism, a two-tier objective function and constraints including building thermal balance are constructed, forming a two-tier nonlinear optimization model. This model is then transformed into a single-tier mixed-integer linear programming model through Boolean variable constraint relaxation, Lagrangian function construction, KKT condition transformation, and strong duality principle and Big M method linearization to obtain the decision results. This invention solves the problems of missing differentiated carbon compliance paths for building producers and consumers and poor adaptability of the electricity-carbon collaborative model in existing technologies.

This invention belongs to the field of power system automation technology, specifically relating to a microgrid cluster energy regulation method considering multi-level entity interactions. It includes: establishing a Nash bargaining game model between upper-level microgrids, considering individual microgrid operation constraints, with the objectives of minimizing the total operating cost of the microgrid cluster and maximizing the Nash product; establishing a lower-level distribution network DC power flow model, considering power generation and consumption balance constraints, thermal power unit output constraints, and network power flow constraints; and transforming the two-level optimization model into a single-level mathematical programming model with equilibrium constraints using KKT conditions and the strong duality theorem, solving for the operating strategies of each microgrid, the inter-microgrid trading strategies, and the optimal power flow and nodal prices of the distribution network. This invention can consider the impact of three levels of interaction on energy management strategies: individual microgrid operation, energy interaction between microgrids, and interaction between microgrids and the distribution network, resulting in a more reasonable, safer, and more economical microgrid cluster energy management scheme.

This invention discloses a two-layer optimization method for warehouse picking operations considering dynamic order constraints, belonging to the field of logistics and warehousing. The optimization method includes the following steps: Step 1: Digitalization of warehouse layout; Step 2: Establishing an order batch optimization model with the minimum number of picking aisles and the highest level of service counter fairness as the upper-level optimization objectives; Step 3: Establishing an employee picking operation optimization model with the shortest total picking time as the lower-level optimization objective; The next step is to optimize the models in Steps 2 and 3. Addressing the operational requirements of a logistics company, this invention studies the order allocation and picking path problems, constructing a two-layer optimization model with the minimum total number of shelves and the maximum service counter fairness as the upper-level optimization objectives, and the shortest total picking time as the lower-level optimization objective. It comprehensively considers practical operational constraints, including warehouse physical layout, personnel and auxiliary tool capabilities, etc. Compared with traditional models, the model constructed in this paper has stronger practicality.

This invention discloses a data-driven adaptive parametric modeling method for automotive rooflines, comprising the following steps: acquiring multiple sets of side-view roofline coordinate data of real vehicles, dividing the side-view roofline into a front guide segment and a rear main body segment; establishing a normalized coordinate system for each segment, and then discretizing it into several sampling points to form a bottom-level sample database; defining control variables, including the spatial position of control points in the normalized coordinate system, the number of control points, and the type of reconstructed model; employing a two-layer optimization strategy to perform global iterative optimization of the control variables for each segment to obtain the optimal control variables; constructing a comprehensive evaluation index, and selecting the control variable with the optimal comprehensive evaluation index from all parametric reconstructed models that have undergone inner-layer optimization as the optimal control variable, thus obtaining the optimal parametric reconstructed model. This invention aims to solve the problems of existing designs relying on manual experience and the difficulty in achieving an optimal balance between fitting accuracy and model complexity.

The application discloses a kind of biological natural gas system optimization design method and system, method includes: obtaining renewable energy power generation data, initial biogas data and power grid operation data;Based on the data obtained, a two-stage hydrogenation conversion of biogas-green hydrogen collaborative system optimization configuration model is constructed, the model maximizes the net present value of the whole life cycle as the target, the key component capacity is the decision variable, and contains the coupling constraint representing the competitive allocation relationship between hydrogen in the first biosynthesis and the second chemical synthesis;The model is decomposed into upper investment decision layer and lower operation decision layer, and a two-level optimization solving framework is constructed;A hybrid algorithm combining genetic algorithm and linear programming is used to solve the two-level optimization model, and the optimal capacity configuration scheme of the key components is obtained. The two-stage hydrogenation conversion process is collaboratively optimized and the capacity of the key components of the whole system is matched, which significantly improves the economy and operation flexibility of the biogas-green hydrogen coupled system.

This invention discloses a method, apparatus, terminal, and medium for distributed bi-level optimization on time-varying directed graphs, comprising: acquiring a time-varying directed graph sequence; solving a bi-level optimization problem for a multi-agent system based on the time-varying directed graph sequence; updating the decision variables of each agent using a row random matrix and calculating the local gradient of the decision variables of each agent using a penalty function; updating the gradient tracking variables of each agent using a column random matrix to eliminate gradient estimation bias in the time-varying directed graph sequence; and outputting the bi-level optimization results for the multi-agent system. This invention solves the problem of bi-level optimization failure under dynamic topology, eliminates the risk of numerical instability, overcomes the second-order computational bottleneck, and reduces resource overhead.

This invention relates to the field of collaborative transportation using public transport and drones, and discloses an optimized method for express delivery allocation in urban public transport and drone collaborative transportation. The steps are as follows: collecting urban traffic flow information data; extracting clustered bus trajectory data features, constructing a multiple linear regression equation to predict road segment travel time, and continuously optimizing the equation based on actual results to reduce prediction errors; obtaining the operating time of public transport in each city, the Euclidean distance from each POI point to the integrated transfer point, and constructing a two-layer optimization framework using a three-way optimization algorithm on the outer layer and a genetic algorithm on the inner layer to realize the express delivery allocation scheme; loading express deliveries according to the allocation scheme for each batch, and determining whether to perform transfer based on real-time traffic data. This further improves the overall efficiency and intelligence level of urban last-mile logistics.

This application relates to multi-microgrid systems, and more particularly to a two-layer collaborative planning method for multi-microgrid systems based on dynamic path optimization. The method includes: acquiring the topology, line parameters, energy source and storage equipment parameters of each microgrid, load data, and typical operating scenarios of the multi-microgrid system; constructing a two-layer optimization architecture, with an upper planning layer and a lower operation layer; feeding back the annualized operating cost calculated by the lower operation layer to the upper planning layer, iteratively optimizing until convergence, and outputting the optimal equipment configuration scheme and the corresponding dynamic scheduling strategy for energy transmission paths. This application treats energy transmission paths as optimizable decision variables in the planning layer, reduces equivalent transmission costs through dynamic path selection at the lower level, and feeds back more realistic operating cost information to the planning layer. This avoids deviations in upper-level investment configuration from reality due to lower-level network congestion or loss deviations, thereby improving the overall economic efficiency of the multi-microgrid interconnection system.

The application relates to a double-layer optimization method and system based on an energy concentrator, which firstly constructs a double-layer optimization model of a master-slave game architecture, wherein an upper-layer model of the double-layer optimization model takes minimization of total operation cost as an optimization target and comprises first-stage day-ahead scheduling decision and second-stage real-time adjustment decision; the double-layer optimization model is reconstructed into a single-layer mixed integer linear programming model through KKT conditions and strong duality theory; experience distribution is generated based on historical data and Huber fuzzy set is constructed to depict RG uncertainty, so as to form a min-max-min distribution robust optimization model under the first-stage day-ahead scheduling decision-worst distribution selection-second-stage real-time adjustment decision architecture; and finally, a customized C&CG algorithm is adopted for iterative solution. The method balances economy and robustness, improves multi-subject coordination and source-load interaction efficiency, and provides a stable and efficient scheduling scheme for the energy concentrator.

This invention relates to the field of intelligent agent construction and application technology, specifically disclosing a two-layer optimization method for constructing multi-agent systems. The method models the multi-agent system based on a large language model as a learnable graph, abstracts agents as nodes, and abstracts the interaction and communication between agents as edges. It introduces unbiased policy gradient estimation to transform the topology optimization problem into a continuous optimization problem, calculating gradients and updating connection probabilities. Based on Shapley values ​​from cooperative game theory, it quantifies the marginal contribution of each edge to system performance, optimizing the directed acyclic graph. Based on a black-box tuning mechanism, it introduces an optimization degree evaluator to ensure that prompts are iteratively updated towards the optimal fit, collaboratively optimizing the system's topology and prompts. This invention promotes the improvement of information interaction quality and overall decision-making ability among agents, providing support for the construction of multi-agent systems based on large language models and the optimization of their topology and prompts.

This invention provides a two-layer optimization method, system, and computer storage device for large-scale, expensive single-objective optimization problems. The method involves steps including initialization, subproblem construction, surrogate model construction, subproblem optimization and evaluation, a bottom-level population update stage, a top-level surrogate model selection stage, and a top-level optimization and global update stage. By constructing and evaluating multiple candidate subspaces at the lower layer, it automatically identifies key subspaces that significantly impact the global objective and focuses on optimizing these key subspaces at the upper layer. Under a finite expensive function evaluation budget, it achieves efficient solutions to large-scale single-objective optimization problems. By introducing a surrogate model to approximate the expensive objective function and combining it with a two-layer optimization structure, it organically combines subspace selection and fine-tuning, effectively improving search efficiency, accelerating convergence speed, and enhancing the quality of the final solution, thus meeting the application needs of complex engineering optimization scenarios.

The invention discloses a three-dimensional component layout double-layer optimization method based on an improved differential evolution algorithm, and relates to the technical field of satellite component layout, and the method comprises the steps: obtaining a to-be-installed satellite component and a component layout space, carrying out the upper-layer distribution optimization of the satellite component based on the component layout space based on the improved differential evolution algorithm, and obtaining a to-be-installed component layout space; obtaining an optimal distribution scheme; when the satellite components arranged according to the optimal distribution scheme meet the stability requirement, obtaining a component arrangement scheme; and performing lower-layer position optimization according to the component layout scheme based on the mixed integer linear programming model, and generating a satellite component layout position scheme. According to the invention, on the basis of the upper-layer distribution optimization of the improved differential evolution algorithm and the lower-layer position optimization of the mixed integer linear programming model, the rapid solution of the satellite component based on the component layout space is realized, the layout position scheme of the satellite component is obtained, and the overall optimization of the layout and the position of the satellite component is further realized.

This invention belongs to the field of electric vehicle load optimization technology, specifically disclosing a multi-objective bi-level optimization method for electric vehicle load considering peak shaving and valley filling, including the following steps: establishing a multi-objective bi-level optimization model for electric vehicle load; through... KKT Condition and maximum M The method transforms the bi-level optimization problem of the multi-objective bi-level optimization model into a single-level multi-objective linear problem; further, it transforms the single-level multi-objective linear problem into a single-level single-objective problem; and finally, it solves the single-level single-objective problem to obtain... Parteo Frontier; at Parteo The ideal solution is determined at the forefront and serves as the final result of the original two-level multi-objective optimization problem, achieving multi-objective two-level optimization of electric vehicle load. This invention solves the problem of existing two-level optimization operation models that lack consideration of comprehensive indicators such as peak-valley difference and load fluctuation on the distribution network side, resulting in the upper-level model in the two-level model being a single-objective optimization problem. This invention can reduce peak-valley difference and load fluctuation while ensuring the benefits of electric vehicle users.