103 results about "Bilevel optimization" patented technology

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

The invention discloses a power distribution network adaptive topological structure optimization method, system and device and a medium, and the method comprises the steps: obtaining related parameters of a power distribution network system, dividing the power distribution network system into different layers and different regions based on the related parameters of the power distribution network system, and constructing a layering and partitioning model; coordinating and solving the layering and partitioning model through an intelligent algorithm, and obtaining control variables of different regions; constructing an objective function based on the control variable, and solving the objective function by adopting an improved particle swarm optimization method to obtain a topological structure; and optimizing the topological structure by adopting a double-layer optimization strategy to obtain an optimization result, evaluating the optimization result, and feeding back the evaluation result to the target function. According to the method, the randomness and volatility challenges caused by multi-source resource access can be effectively dealt with, and the limitation of the traditional separation design is broken through the collaborative optimization of topology and control, so that the operation efficiency, the stability and the anti-interference capability of the power distribution network are improved, and the collaborative evolution of a topological structure and a control strategy is realized.

The invention discloses a park energy double-layer optimization scheduling method considering electric vehicles and demand response, and the method comprises the following steps: constructing a park integrated energy system which comprises an energy supply side, an energy conversion side and a demand side; a double-layer optimization model is established, the upper layer is a multi-target optimization model with the target of minimizing the total cost of the system, minimizing the carbon emission and maximizing the renewable energy consumption rate, and the lower layer is a single-target optimization model with the target of minimizing the charging cost of the electric vehicle user and the load fluctuation of the power grid; based on the Pareto theory, designing a multi-objective litsea coreana optimization algorithm to solve an upper-layer multi-objective model; introducing chaos initialization, adaptive step length adjustment and a Gaussian disturbance strategy improved litsea coreana optimization algorithm to solve a lower-layer single-target model; and through a vehicle network interaction and demand response cooperation mechanism, real-time scheduling is executed, a day-ahead plan is fed back and corrected, and finally an optimal park energy optimization scheduling strategy is solved.

The invention provides an electric vehicle charging station planning method and system, and the method comprises the steps: obtaining road network data and power distribution network topology information of a planning region, and constructing a time and space distribution model of an electric vehicle distributed load; based on historical travel behaviors and path probability distribution of the electric vehicle, constructing an electric vehicle behavior prediction model in combination with a graph neural network, and determining a candidate charging station service radius and a clustering region; according to the distributed photovoltaic access, the dynamic carbon emission factor and the vehicle charging and discharging behavior, constructing a dynamic optimization model of the coupling energy flow and the carbon flow; constructing an upper-layer charging station site selection model in the candidate charging station nodes, and determining an optimal site selection result by adopting a multi-objective optimization algorithm of an attention-based self-evolution Transform structure; establishing a lower-layer dynamic mixed integer programming configuration model based on the site selection result; and outputting an optimal deployment scheme considering energy efficiency, green low carbon and supply and demand balance by jointly solving the double-layer optimization model.

The invention relates to the technical field of alternating-current and direct-current power distribution networks, in particular to an alternating-current and direct-current power distribution network SOP partition interconnection locating and sizing method and a related device, and the method comprises the steps: obtaining GIS multi-source data and power distribution network data of a target region; dividing a power supply area according to the GIS multi-source data and the power distribution network data to obtain a to-be-interconnected partition pair set; generating a high-quality interconnected partition pair set by using the to-be-interconnected partition pair set; constructing a target optimization model by taking the minimum total operation cost as an optimization target and taking a power balance constraint, a node voltage constraint, a branch capacity constraint, an SOP operation constraint, an SOP reactive power constraint and an SOP capacity constraint as constraint conditions; and inputting the high-quality interconnection partition pair set into the target optimization model, cooperatively solving the target optimization model by adopting a double-layer optimization algorithm to obtain the optimal position and the optimal capacity of the SOP, and solving the technical problem that the existing SOP locating and sizing method is difficult to meet the targets of engineering feasibility and optimal operation benefit at the same time.

The invention discloses an industrial park virtual power plant low-carbon optimization scheduling method based on augmented Lagrange safety reinforcement learning, and belongs to the technical field of virtual power plant low-carbon optimization scheduling related to carbon emission transaction. The problem that in the prior art, a traditional virtual power plant low-carbon optimization scheduling method is poor in scheduling performance due to the fact that the optimization result deviates from reality is solved. According to the method, a constrained Markov decision process solving framework based on an augmented Lagrange soft actor-commentator algorithm is proposed, a double-layer optimization problem is converted into a constrained Markov decision process by performing multi-dimensional modeling and establishing a double-layer optimization framework, and an augmented Lagrange function is introduced, so that the constraint Markov decision process is obtained. And completing the design of an augmented Lagrange reinforcement learning algorithm, and obtaining a trained low-carbon optimization scheduling neural network model. According to the method, the low-carbon optimal scheduling efficiency of the virtual power plant is effectively improved, and the method can be applied to optimization of the EV aggregator charging and discharging strategy in the virtual power plant in the industrial park.

The invention discloses an energy storage type intelligent soft switch double-layer optimization site selection method based on improved whale algorithm and second-order cone programming cooperation, and belongs to the technical field of power distribution networking and optimization operation, and the method comprises the steps: collecting and preprocessing the basic data of a power distribution network; a double-layer optimization model with the minimum daily operation cost of the system as the target is established, the upper layer takes the installation position of the energy storage type intelligent soft switch as a decision variable, and the lower layer takes the three-port power transmission value as a decision variable and constructs constraints based on the power flow equation after second-order cone relaxation and the energy storage operation limitation; an upper layer site selection problem is solved by adopting an improved whale optimization algorithm introducing a self-adaptive inertia weight, a lower layer operation problem is solved by adopting a second-order cone programming method, and collaborative optimization is realized through double-layer interaction. According to the method, global collaborative optimization of planning and operation is realized, the solving precision and efficiency of the site selection problem are improved, and the system operation cost and the network loss are effectively reduced.

The invention discloses a double-layer optimization method and system for real-time flood control scheduling of a series reservoir group. The method comprises the steps that the maximum feasible impounding boundary of each time period is calculated based on the discharge capacity and storage capacity constraint of each reservoir; constructing a space-time risk response kernel matrix, encoding lag response and attenuation intensity of flood routing, constructing a dynamic risk potential energy index in combination with real-time reservoir capacity, and solving space distribution water quantity of each reservoir by taking minimization of the index as a target under physical boundary constraint; the space distribution water quantity is used as a guide target, an evolutionary algorithm embedded with a physical consistent projection operator is adopted, and iterative optimization is carried out on hourly output flow of each reservoir. According to the method, through matrix coding of the space-time risk and introduction of manifold projection solution, the problem that upstream and downstream hydraulic space-time coupling and strong physical constraint are difficult to solve is effectively solved, and the physical consistency and peak clipping efficiency of a scheduling scheme are improved.

The invention relates to the technical field of three-dimensional point cloud measurement and intelligent path planning, solves the technical problems that a traditional method is strong in shielding, large in normal change, limited in reachability and difficult in path execution in point cloud measurement, and particularly relates to a double-layer optimization measurement planning method for a complex model measurement task. Calculating and caching a visible point set of each viewpoint under the conditions of angle constraint and shielding elimination based on the adjacency graph of the candidate viewpoints and a visibility evaluation mechanism; in the inner layer optimization, an initial measurement route and a viewpoint set are generated from an initial viewpoint, and a continuous executable measurement route is reconstructed after full coverage constraint is met; in the outer layer optimization, a genetic algorithm is adopted to carry out global search on a starting viewpoint, and the coverage rate and viewpoint scale cost are used as fitness to output an optimal measurement viewpoint set and a measurement path. According to the method, high-coverage-rate measurement planning can be realized under complex shielding and strict view angle constraint conditions, the number of viewpoints and path redundancy are reduced, and the planning efficiency and robustness are improved.

The invention provides a man-machine collaborative dynamic scheduling method fused with a double-layer optimization mechanism, and relates to the field of man-machine collaborative dynamic scheduling. According to a double-layer optimization mechanism provided by the invention, an offline optimization layer and an online real-time scheduling layer are organically combined: firstly, the offline optimization layer is based on a workpiece set, stations and robot resources, and is combined with a simulated annealing algorithm for adaptive optimization to obtain a global optimal solution; and then, taking a global optimal solution output by the offline optimization layer as an initial scheduling scheme of the online real-time scheduling layer, and performing real-time adjustment by adopting a multi-agent adaptive near-end strategy optimization algorithm. According to the method, the low efficiency of online scheduling from zero exploration is avoided, and the high efficiency of decision making is ensured. Besides, the online real-time scheduling layer constructs a collaborative decision-making system composed of a task allocation agent, a resource scheduling agent and a disturbance response agent, decision-making dimension pressure faced by a single agent is remarkably reduced through specialized labor division, and a complex interaction relation in man-machine collaborative scheduling is delicately processed.

The invention relates to the technical field of electric energy metering, and discloses an electric energy meter metering error dynamic compensation method. According to the method, original metering data in the running process of the electric energy meter are collected and subjected to principal component analysis processing to obtain a metering principal component sequence of the electric energy meter. Performing wavelet transform processing on the principal component sequence to obtain an approximation coefficient and a detail coefficient; a resistor and an electric reactor are utilized to form a dynamic compensation circuit, an approximation coefficient is used as a metering reference value, detail coefficients are distributed in the dynamic compensation circuit, and a distribution coefficient is obtained. And constructing an electric energy meter measurement error compensation double-layer optimization model, wherein the model comprises an upper layer optimization model and a lower layer optimization model. And after constraint conditions of the two models are determined, inputting the distribution coefficient into the models, solving through gradient descent according to the constraint conditions to obtain metering error compensation parameters of the electric energy meter, and performing dynamic error compensation on the electric energy meter according to the parameters. The method effectively improves the metering precision of the electric energy meter, and has an important practical value.

The invention provides a double-layer optimization scheduling method for source network load storage collaborative loss reduction, and belongs to the technical field of source network load storage collaborative scheduling. A double-layer optimization framework comprising an upper layer planning model and a lower layer operation model is constructed, and a key node set is screened by using a node importance comprehensive scoring method based on a graph theory; the improved wolf pack algorithm is adopted to solve a double-layer coupling problem, a lower-layer operation effect is fed back to an upper layer to serve as a fitness evaluation basis, and in cooperation with population aggregation degree monitoring, a wandering wolf random jumping mechanism and a reverse learning strategy, deep collaboration and global optimal solution of a planning layer and an operation layer are achieved. The technical problem of poor network loss optimization effect caused by lack of an effective double-layer coupling solution mechanism for collaborative optimization planning and operation scheduling of the distributed photovoltaic and energy storage system in the power distribution network is solved.

The invention provides a scheduling method and device for source-network load-storage coordination of an active power distribution network, and computer equipment, and belongs to the technical field of power grid control. The method comprises the following steps: acquiring real-time data of a regulation and control main body, wherein the regulation and control main body comprises a power distribution main network and new energy, an energy storage unit and an adjustable load which are accessed to the power distribution main network; based on real-time data, optimal solutions of an outer-layer optimization problem and an inner-layer optimization problem of the double-layer optimization model are solved, and an optimal scheduling strategy in a scheduling period is obtained; generating a control instruction according to the optimal scheduling strategy, and issuing the control instruction to the regulation main body; wherein the double-layer optimization model is used for scheduling optimization of source-network-load-storage cooperative operation, peak clipping and valley filling maximization and new energy consumption maximization are taken as objective functions of the outer-layer optimization problem, and new energy output fluctuation and load curve fluctuation minimization are taken as objective functions of the inner-layer optimization problem. Through the method, source network load storage collaborative optimization scheduling is realized.

The invention relates to a micro-grid optimization scheduling method with electric vehicle and multi-source uncertainty. The micro-grid optimization scheduling method comprises the following steps: S1, constructing a multi-target optimization scheduling model of a micro-grid system layer; s2, performing robust modeling on multi-source uncertainty in the multi-target optimization scheduling model of the micro-grid system layer based on a multi-target confidence gap decision theory, and establishing a robust optimization model of the micro-grid system layer; s3, constructing an optimal scheduling model of an electric vehicle user layer based on a foreground theory, and taking electric vehicle user comprehensive foreground value maximization as a target function; and S4, constructing a double-layer segment model of day-ahead scheduling, carrying out cooperative solution on an upper layer and a lower layer by adopting a double-layer optimization algorithm, and outputting a day-ahead scheduling plan of each unit in the micro-grid and the electric vehicle. According to the method, the comprehensive operation cost, the load fluctuation level, the electric vehicle owner utility and the source load uncertainty of the micro-grid are integrated, and the internal load stability and the energy utilization rate of the micro-grid are improved by excavating the scheduling potential of the source load side.

The invention relates to the technical field of computers, in particular to a double-layer optimization model training method and denoising method based on regularization outer gradient. The method comprises the steps that a training sample set is acquired, a to-be-trained model is subjected to a first preset number of times of iterative training based on the training sample set, each time of iterative training comprises multiple times of iterative updating processes, and a global target and a local target are considered through optimization structures of two levels of iterative training and iterative updating. And by introducing a variance reduction technology, a non-negligible calculation error introduced by a random sampling calculation gradient is avoided, the training speed of the model is improved, and the training cost of model training is reduced.

The invention discloses an optimal preset output solving method based on a minimum-maximum criterion and global robustness, and relates to the technical field of power system optimization scheduling. An existing scheduling method mainly focuses on robustness improvement of a local scene, cost deviation controllability of a global extreme scene is not achieved, the solving efficiency of a multi-layer optimization model is low, and the real-time scheduling requirement of an actual power system is difficult to meet. Establishing a power system economic dispatching minimum cost objective function, and establishing a linearized KKT condition constraint objective function; presetting a segmented cost function and a segmented constraint condition, and constraining the linearized preset cost function by using linear constraint; establishing an optimization target according to the minimum cost target function and the linearized preset cost function, processing the optimization target into a multi-layer optimization model, converting the multi-layer optimization model into a double-layer optimization model, and solving the double-layer optimization model by using a Benders decomposition method to obtain optimal solutions including preset wind power output, preset photovoltaic output and thermal power generating unit output. The method is used for obtaining power system output.

Decentralized bilevel optimization techniques for personalized learning over a heterogenous network are provided. In one aspect, a decentralized learning system includes: a distributed machine learning network with multiple nodes, and datasets associated with the nodes; and a bilevel learning structure at each of the nodes for optimizing one or more features from each of the datasets using a decentralized bilevel optimization solver, while maintaining distinct features from each of the datasets. A method for decentralized learning is also provided.

A multi-objective, two-layer optimization method suitable for gravitational wave detection spacecraft aims to simultaneously suppress electromagnetic and self-gravitational noise. This method employs a two-layer optimization structure to ensure bidirectional interaction and feedback between the upper and lower layers, meeting the spacecraft's requirements for extremely high sensitivity. By optimizing the layout of internal equipment, the cleanliness and stability of the surrounding environment for testing are improved. This invention defines layout optimization as a two-layer optimization problem, clearly defining decision variables, constraints, and objective functions, and solving it as a mixed-integer optimization problem. It utilizes an efficient multi-objective, two-layer hybrid optimization method combining NSGA-III and the DE algorithm, and employs collision detection and penalty function strategies to handle geometric constraints. This invention effectively resolves optimization conflicts under multi-physics coupling effects, achieving efficient resource allocation and performance improvement. It has significant advantages in efficiency, scalability, and adaptability in handling geometric constraints, greatly improving the solution efficiency and stability of optimization problems.

The invention discloses a multi-view image fusion representation learning method, device and equipment based on double-layer optimization, and relates to the technical field of machine learning. The method comprises the following steps: splicing feature matrixes of a multi-view data set to obtain a multi-view data matrix; calculating an initial affinity matrix of each view, and initializing a fusion affinity matrix, a view weight set, potential feature representation and a shared mapping matrix; a bilayer optimization model is constructed to learn potential feature representations. The model comprises an objective function of an upper-layer optimization task for updating potential feature representation and sharing a mapping matrix under the constraint of a fixed fusion affinity matrix, and an objective function of a lower-layer optimization task for updating the fusion affinity matrix and a view weight set under the constraint of the fixed potential feature representation. And executing alternative optimization iteration: executing upper-layer optimization task update and first, and then executing lower-layer optimization task update and until ending. And outputting the final potential feature representation for executing the task.

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.

The application provides a source network load storage virtual power plant-oriented internal microgrid balance optimization method and system, relates to the virtual power plant field, and the method comprises the following steps: obtaining first target power information from a power system, and performing cleaning, standardization, clustering and hierarchical classification processing to obtain second target power information; constructing a load model and designing a time sequence network according to the second target power information to construct a time sequence model; determining adjustable variables and adjustable capacity ranges to obtain boundary constraint conditions; building a double-layer optimization architecture facing user demand and user equipment end response scheduling, and constructing a multi-task learning model; solving the multi-task learning model, and outputting a target solution to adjust the internal microgrid balance of the virtual power plant. The target solution obtained by solving the multi-task learning model can represent the resource allocation instructions required by each device under the optimization target, and then the response strategy is determined to flexibly adjust the user load, so that the multi-objective optimization of source network load storage resources is realized.

Comprising a method and apparatus including computer code configured to cause one or more processors to construct a similarity score between a plurality of words, initialize a similarity structure that depends on a task and is based on the similarity score, and initialize the similarity structure based on the similarity score by implementing a bilayer optimization. And performing machine learning on the task dependency of the similarity structure, the double-layer optimization comprising: a search stage comprising learning a model weight by estimating a model parameter corresponding to a first entry of the similarity structure, learning a parameter of a second entry of the similarity structure by applying the model parameter to the second entry; and a fine tuning stage: updating the model parameters under the condition of keeping the similarity structure unchanged.

The invention provides a robust difunctional radar communication beam forming optimization method considering DoA estimation errors, and belongs to the technical field of communication. Comprising the following steps: S1, designing a transmission protocol, S2, constructing two types of optimization problems, and S3, designing a core algorithm. Through unified error modeling and a double-layer optimization framework, synchronous suppression of two types of CRBs of worst situations and statistical average is realized, and meanwhile, the rate threshold value of each user is maintained; and by means of a low-complexity iterative algorithm, the scheme has real-time implementation capability. According to the robust beamforming optimization method provided by the invention, in a difunctional radar communication (DFRC) system, the negative influence of DoA estimation errors on the system performance is effectively solved by jointly optimizing the target sensing precision and the communication performance.

The present application relates to the technical field of electric energy metering, and discloses a kind of electric energy metering error dynamic compensation method.The method collects the metering original data in the operation process of electric energy meter, and obtains electric energy metering principal component sequence by principal component analysis processing.Then the principal component sequence is processed by wavelet transform, and the approximation coefficient and the detail coefficient are obtained.A dynamic compensation circuit is formed by using resistor and reactor, the approximation coefficient is used as the metering reference value, the detail coefficient is distributed in the dynamic compensation circuit, and the distribution coefficient is obtained.A double-layer optimization model for electric energy metering error compensation is constructed, which includes an upper optimization model and a lower optimization model.After determining the constraint conditions of the two models, the distribution coefficient is input into the model, and the electric energy metering error compensation parameters are obtained by gradient descent solution based on the constraint conditions.The electric energy meter is dynamically compensated according to the parameters.The present application effectively improves the electric energy metering accuracy and has important practical value.

The invention relates to the technical field of intelligent generative model and diffusion model sampling, and discloses a rapid robust sampling method based on a hierarchical scheduling optimizer and a diffusion model, comprising the following steps: constructing a hierarchical optimizer, and performing global search on the upper layer in a low-dimensional space to output an optimal initialization strategy; the lower layer receives the initial scheduling generated by the strategy and carries out local optimization in a high-dimensional space; according to the local optimization, midpoint error proxy is taken as a target, an interval penalty fitness function is adopted for evaluation, the fitness function is combined with midpoint error proxy and a penalty over-short-step-length robustness item, and an evaluation result is fed back to an upper layer to form closed-loop iterative optimization. The high-dimensional scheduling problem is decomposed into double-layer optimization, the dimension disaster is effectively avoided, the search efficiency is remarkably improved, and the innovative error proxy target and the fitness function ensure that the finally generated sampling scheduling has low theoretical error and high actual robustness.

The application discloses a site selection and capacity determination method and device for a traction substation of a flexible direct current power supply system based on a double-layer planning, and the method comprises the following steps: a double-layer optimization model of the traction substation of the traction power supply system is established, and decision variables of the double-layer optimization model are determined, objective functions of a planning layer optimization model and a running layer optimization model are established; a planning layer constraint, a planning layer additional constraint and a running layer constraint are established; an updated double-layer optimization model is obtained; an iterative solution algorithm is used to solve the updated double-layer optimization model, optimal configuration parameters are obtained, and the site selection and capacity determination of the traction substation of the traction power supply system are performed based on the optimal configuration parameters. The application considers the investment cost change caused by the system structure parameter change, considers the influence of the system running parameter change on the system running cost, proposes an iterative algorithm of the double-layer optimization model, realizes efficient and accurate solution of the double-layer optimization model, and makes the final optimization scheme more in line with engineering requirements.

An addressing optimization method for establishing an energy storage system by considering multiple factors belongs to the technical field of energy storage optimization configuration, and comprises the following steps: establishing a double-layer optimization configuration model of the energy storage system, the outer layer comprising investment cost and daily operation cost; the inner layer comprises system active power loss and system voltage deviation; establishing a double-layer optimization configuration model constraint condition according to the energy storage charge state; adopting an improved multi-target particle swarm algorithm to obtain the minimum installation capacity and the operation power of each energy storage; and taking the output power as the rated power, and optimizing a configuration scheme with the minimum investment cost as an energy storage system addressing scheme. According to the invention, a multi-strategy improved multi-objective particle swarm optimization (CMOPSO-MSI) is introduced to solve a multi-objective optimization model of hybrid energy storage. Through combination of various strategy improvements, the algorithm aims to overcome a series of problems that premature convergence is in a local non-dominated solution, inertia weight setting lacks guidance and the like, and better Pareto frontier distribution is realized.

The invention belongs to the technical field of power spot market clearing, and provides a trans-provincial and trans-regional power spot market clearing optimization method, system and application, and the method comprises the steps: firstly constructing a double-layer optimization clearing model, enabling a lower-layer model to be a provincial-level power spot market pre-clearing model, independently optimizing a local unit combination strategy, and enabling a lower-layer model to be a provincial-level power spot market pre-clearing model; the upper-layer model is a regional electric power spot market coordination optimization model, and uniformly adjusts clearing price, trans-provincial transaction electric quantity and power generation resource distribution; the lower-layer model solves a unit commitment problem through a Q-Learning algorithm, the upper-layer model takes a unit commitment strategy output by a lower layer as an input parameter, and inter-provincial transaction optimization is solved by adopting a linear programming method; and the lower-layer model transmits the optimal unit combination strategy as a parameter to the upper-layer model to obtain the optimal clearing price, the unit operation strategy and the interconnection line power of the provincial power grid, and then the results are transmitted back to the lower-layer model for implementation.

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.

The invention provides a demand side response management and control method and system for minimizing a peak-to-average power ratio, and the method comprises the steps: dividing time periods, clustering user subdivision consumption blocks, and setting an incremental electricity price and energy consumption breakpoint, and converting a double-layer optimization problem of a power supply company and a user side into a problem that a nonlinear term is relaxed into a mixed integer linear programming problem; and the optimal solution of the intra-day scheduling scheme is solved by using a standard optimization tool, so that the functions of optimizing the resource configuration of the power system and balancing the power supply and demand are realized. According to the method, different electricity prices are set based on demand distribution of multiple time periods in the day, a more stable interval block electricity price scheme is formed, loads can be effectively prevented from being concentrated in a certain time period, the peak-valley difference of the system is reduced, balanced distribution of demands is facilitated, and power grid load fluctuation is relieved. According to the method, the demand response of the electric power system consumer is promoted, the income of an electric power company is guaranteed under the condition that the burden of the consumer is not increased, and higher economic benefits and better sustainability are brought to power grid operation.