88 results about "Single objective optimization problem" patented technology

The invention relates to an electric vehicle charging station planning method considering traffic network flow. The method comprises the following steps: referencing an intercepting location model in a traffic field, taking largest captured traffic flow, minimum network loss of a power distribution system and minimum node voltage deviation as an objective, establishing a multi-objective decision model, then determining a reasonable weight coefficient of each objective function after being normalized by using a super efficiency data envelopment analysis evaluation method, and transforming a multi-objective optimization problem to a single objective optimization problem, later putting forward an improved binary particle swarm optimization algorithm to solve a single objective optimization model. The method considers the urban traffic network traffic, electric car driving mileage and location problem of an electric vehicle charging station which affects a distribution network. The example results of the method show that the planned charging station location can provide convenient and fast charging services for more electric cars, and further improve the safety and reliability of the system operation.

The invention discloses an aircraft multi-objective optimization method based on a self-adaptive agent model, relates to a multi-objective optimization method for treating complex aircraft design, and belongs to the field of aircraft design optimization. According to the aircraft multi-objective optimization method, an integrated preference function is constructed by use of a physical planning method to realize the conversion of the multi-objective optimization problem into a single-objective optimization problem reflecting design preference; next, the self-adaptive agent model is constructed from the integrated preference function and constraint conditions to take the place of a high-accuracy analysis model, and therefore, the problem of great time taken in calculation of optimization design is solved; finally, the constraint problem is converted into a non-constraint problem by use of an augmentation Lagrange multiplier method, and the non-constraint problem is solved by use of a genetic algorithm. The aircraft multi-objective optimization method has the advantages that the solving process of the aircraft multi-objective optimization method taking much time in calculation is simple and efficient, and therefore, a Pareto noninferior solution meeting the requirements of a user can be obtained quickly to shorten the design period of the aircraft, and the design cost is reduced. Besides, the aircraft multi-objective optimization method is high in universality and convenient for program development.

The invention discloses a multi-objective optimization system and a multi-objective optimization method containing a composite energy storage micro grid. The multi-objective optimization system containing a composite energy storage micro grid is composed of a micro grid central controller, an energy storage controller, a photovoltaic controller, a load controller, and a communication network. According to the multi-objective optimization method containing a composite energy storage micro grid, a multi-objective optimization model is established for a grid-connected composite energy storage micro grid including photovoltaic batteries, flow batteries, lithium batteries and other energy storage devices, with the purpose of minimizing micro grid electricity purchasing cost and tie line power fluctuation. The weighting coefficient is determined based on a two-person zero-sum game method, and the model is converted into a single-objective optimization problem and solved using a linear weighted method. By adopting the multi-objective optimization system containing a composite energy storage micro grid, the utilization rate of renewable energy of the micro grid can be maximized effectively, tie line power fluctuation and distributed power supply grid-connected operation shock can be reduced, and the economical efficiency of micro grid operation can be improved.

The invention discloses an optimized layout method of water environment quality monitoring stations of a river and lake intersected water system. According to the invention, a complicated intersected water system is divided into three levels, namely, a water function division A, a characteristic subarea B and a characteristic area C with various water quality conditions, a multi-objective problem is decomposed into the problem of optimization of a plurality of single objects; and by providing quantitative optimization standard and calculation method, dependence on personal experience is reduced, ambiguity and incompatibility caused by complicated geographical environment factors are avoided, and comprehensive and objective monitoring on the water environment quality of the area can be realized with lowest investment.

The invention relates to a hydrogen-refueling optimized control method and a corresponding system, aiming at providing a three-level refueling optimized method for a hydrogen-refueling station with high-effective hydrogen-refueling, namely, a hydrogen-storing tank in the hydrogen-refueling station is divided into three groups which are high, middle and low.. A multi-object optimization algorithm is put forward and a constraint method is adopted to convert a multi-object problem into a plurality of single-object optimization problems so as to realize high effective hydrogen-taking and quick refueling. Each single-object schedule problem belongs to the dynamic schedule scope and a forward sequential searching method is adopted. A detailed program control flow is figured out according to the optimized result so as to design the optimal refueling mode for the refueling system of the hydrogen-refueling station. The invention also provides a hydrogen refueling system which is used for the high-effective hydrogen-refueling three-level optimized control method of the hydrogen-refueling station and comprises a hydrogen-storing container, a hydrogen-charging valve, a pressure sensor, a hydrogen distributor, a flow meter and a check valve; the hydrogen-storing container comprises a low-pressure hydrogen-storing container, a middle-pressure hydrogen-storing container and a high-pressure hydrogen-storing container; and also a program controller.

The invention discloses a micro-grid multi-objective energy optimization method taking three-phase currents into consideration. The method includes the steps that a network reconstruction method is established so as to solve the problem of network losses of a micro-grid, and the network reconstruction method has good adaptability to the micro-grid. In addition, according to the situation that multiple asymmetric distributed power supplies are connected to the micro-grid and a large number of three-phase imbalance loads exist in the micro-grid and based on three-phase current calculation of the micro-grid and the network reconstruction method, a micro-grid unified coordinated optimization strategy taking active optimization of a whole system and the network losses into consideration is provided, total energy cost, the network losses and voltage deviation serve as multiple optimization objectives, and multi-objective energy optimization is converted into single-objective optimization through a subordinate function. Optimal energy distribution of the whole micro-grid is obtained through a particle swarm algorithm, energy optimization of the micro-grid is achieved, and good economic benefits are brought to power consumers.

Aiming at the transformation of a micro grid multi-objective optimization problem into a single-objective optimization problem, the invention provides a micro grid multi-objective optimization methodbased on a Pareto file particle swarm optimization algorithm. The method includes the following steps: establishing multiple optimization objective functions; determining the constraint conditions ofa micro grid; transforming a multi-objective optimization problem represented by the optimization objective functions into a single-objective optimization problem; solving the micro grid multi-objective optimization problem using a Pareto file particle swarm optimization algorithm, and outputting a non-inferior solution set; and determining an optimal solution in the non-inferior solution set according to a preset satisfaction evaluation standard, and optimizing the operation of the micro grid. According to the invention, the output power of each distributed power supply and the charge / discharge of an energy storage device in the micro grid are optimized using the Pareto file multi-objective particle swarm optimization algorithm, external file maintenance and global best position selectionare combined, and the validity and feasibility of the algorithm are verified through a comparative analysis of optimization results.

The invention provides a method for generating test data covering parallel program paths based on coevolution, and aims to provide a method for automatically and efficiently generating test data covering parallel program objective paths. The method includes the following specific steps that firstly, a mathematical model of a test data generation problem is built, and a problem for generating the test data covering the parallel program paths is modeled into a single-object optimization problem; secondly, a coevolution genetic algorithm is designed to solve the model. According to the method, groups are divided into a plurality of sub groups and a cooperative team group according to the correlation of course paths and program input components. Each sub group is used for independently optimizing a part of input components relevant to one certain course path. After the sub groups are evolved into a certain period, excellent individuals of the sub groups are combined to form an initial individual of the cooperative team group so as to be used for optimizing complete program input. After the cooperative group is evolved into a certain period, the excellent individuals are returned to the sub groups. Through alternate coevolution of the cooperative team group and the sub groups, the expected test data are generated.

The invention relates to a light storage charging tower random optimization scheduling method considering peak load shifting of a power distribution network. The method comprises the following steps of: 1, establishing a tower day-ahead multi-objective optimization scheduling model by taking the minimum daily operation cost of the tower and the peak clipping and valley filling of the power distribution network as objectives; 2, evaluating the influence of peak load shifting by adopting the minimum variance of the curve of the electricity purchase quantity of each time period of the tower, andconverting multi-objective optimization into a single-objective optimization problem through an economic conversion coefficient; 3, considering the influence of the output uncertainty of the photovoltaic unit in the tower on day-ahead scheduling, describing by adopting a representative scene, and constructing a random optimization scheduling model by taking the tower day-ahead optimization scheduling and representative scene adjustment as the sum of first-stage decision and second-stage decision, day-ahead scheduling cost and real-time adjustment expected operation cost as targets; and 4, testing the actual light storage charging tower, and solving the model. According to the method, the probability optimal scheduling strategy under the scene that the light storage charging tower participates in peak load shifting of the power distribution network can be effectively obtained, economic, efficient and reliable operation of the tower is guaranteed, and peak load shifting of the regional power distribution network are achieved.

The invention provides a power grid annual power purchase plan formulation method and system based on the game theory. The method comprises the following steps of: collecting the annual total load electric quantity of a power grid, the generator set number of a total power grid, the grid purchase price of each generator set, the coal consumption of each generator set, the upper limit and the lower limit of the annual power generation amount of each generator set, the annual average load rate of each generator set and the upper limit of the difference of the annual average load rate of each generator set; setting the parameter of the annual power generation amount of each generator set; determining a target to be optimized in the power grid annual power purchase plan; building a sub-target functional equation and a constraint equation; converting the power purchase plan into a multi-target optimization problem; on the basis of the game theory, comprehensively optimizing each target to be optimized to obtain the weight value of each target functional equation; converting a multi-target optimization problem into a single-target optimization problem; and determining to obtain the power generation plan value of the annual power generation amount of each generator set. According to the power grid annual power purchase plan formulation method disclosed by the invention, a plurality of targets can be both considered while the power purchase plan is formulated, and meanwhile, a plurality of targets are optimized.

The invention relates to an urban electric vehicle charging network planning method considering multiple optimization targets simultaneously. According to the method, for multiple construction schemes of an electric vehicle charging network in a to-be-planned traffic system, a planning model considering two optimization targets of the charging service capacity and the distribution network loss is built for each construction scheme, the construction scheme of charging service capacity maximization and urban distribution network loss minimization is solved and obtained to serve as the optimal urban electric vehicle charging network planning scheme. Accordingly, two sub optimization targets in the model are subjected to fuzzification separately, and an original optimization model is converted into a single target optimization problem based on maximum satisfaction. At last, the urban electric vehicle charging network planning model is solved through a genetic algorithm based on real number encoding. By means of the method, the practical value of the planning model is guaranteed, the obtained optimization result is more reasonable and convincing.

The invention relates to a method for reconstructing a non-convex compression congnitive image based on evolution multi-target optimization. The method is used for reconstructing a medical image and a natural image. The invention discloses a segmentation break-apart compression sampling method, the shortcoming that some image blocks are not sparse or weakly sparse in the segmentation compression congnitive reconstruction technology in the prior art is overcome, and the quality of a reconstructed image is improved. Restricted single target optimization is converted into multi-target optimization enabling the sparseness to also serve as an optimization target, and compression congnitive optimization reconstruction method based on a wavelet domain is achieved by utilizing an evolution multi-target optimization thought for the images, the shortcoming that the sparseness is hard to determine in the existing compression cognitive reconstruction technology is overcome, and the quality of the reconstructed image is improved. According to the method for reconstructing the non-convex compression congnitive image based on evolution multi-target optimization, edge position information of the image serves as position prior for guiding the sparse coefficient solving in an IHT method, and the shortcoming that the sparse coefficient position is less considered in the existing compression congnitive reconstruction technology is overcome, so that the quality of the reconstructed image is improved.

The invention provides an optimization operation method for an optical diesel store micro-network system with electric vehicle access. The method comprises the steps that a number of optimization objective functions which are used for optimizing to-be-optimized factors which influence the optimization operation of the micro-network system are established; the constraint condition of the micro-network system is determined; according to the constraint condition, a multi-objective optimization problem represented by a number of optimization objective functions is transformed into a single objective optimization problem; and the operation of the micro-network system is optimized. According to the invention, the method focuses on the optimization of the environmental economic operation of the micro-network system; in the states of grid-connection and isolated island operation, an economic optimization operation model which takes the power generation expense, the depreciation expense and the environmental management expense of a distributed power generation unit as objectives and considers the operation constraint condition of the micro-network system is established; a multi-objective constraint optimization problem is formed; optimization solution is carried out on the output power of a distributed power supply in the micro-network system and charge / discharge of an energy storage device; and an optimization result verifies the validity of the proposed model, policy and algorithm.

The invention discloses an automatic power grid thematic map model generating method based on multi-objective optimization. The method comprises the following steps that a layout space is set as a rectangular area, grids are built according to certain transverse and longitudinal spaces, and therefore space is provided with the grids; the length of a line between nodes is described based on the Manhattan or Euclidean distance between the nodes, and a topological connection relation between the nodes is determined; an outgoing line intersection of busbar equipment is described; intersections caused by unreasonable layout positions of elements in different rows on the grids are described; a given multi-objective optimization problem is optimally solved, the important degree of each sub objective function is represented, and the multi-objective optimization problem is converted into a single-objective optimization problem; a multi-objective model for automatic power grid thematic map layout is generated. The automatic power grid thematic map model generating method has adequate scalability, can meet different requirements of different regions, is easy and efficient to implement, and can achieve productization conveniently.

The invention provides a multiple target-oriented enhanced social D2D relay selection method. According to the method, input data are preprocessed to obtain data of a required type; index (namely target) decision weight generation calculation is carried out on the obtained data; and an obtained single-target output amount and quality-of-service (QoS) conditions are converted and integrated, and then solving is carried out through a distributed message passing mechanism to obtain a final output result. According to the algorithm, a multi-target optimization problem is converted into a single-target optimization problem on the basis of combination of a modified IFAHP (MIFAHP) method and an entropy weight method, an expression of the quality-of-service conditions is further converted, and then the D2D UE-NW (UE-to-Network) relay selection result is obtained by distributed solving through a suitable message passing algorithm. According to the method, decision weights of subjective preference of VUE with hesitancy degrees and objective information are combined for consideration, and thus the relay selection result is enabled to obtain more comprehensive performance improvement. The method has a higher equipment access ratio, higher throughput and higher system fairness.

The invention relates to the technical field of electrical power systems, and relates to an electrical power system multi-target optimal power flow implementation method and device. The method provided by the invention comprises the following steps of: (1) calculating an admittance matrix according to designated power grid data, and constructing a multi-target planning problem corresponding to the optimal power flow; (2) normalizing a target function of the multi-target optimal power flow problem; (3) establishing self-adaptive control of parameters by utilizing a flexibility analyzing technique; (4) resolving each single target optimizing problem in an alternative iteration mode; and (5) acquiring an approximate set of an effective solution set of the multi-target optimal power flow problem. The device provided by the invention comprises a constructing unit, a normalizing unit, a creating unit, a processing unit and an acquisition unit. According to the invention, a plurality of mutually conflicted power supply targets can be reasonable compromised and coordinated well, information of the whole effective solution set or the approximate set with almost equal distances of the effective solution set is obtained, and a strategy support is provided for energy conservation and emission reduction as well as safe and economical running of the electrical power system.

The invention provides an inter-provincial power purchase plan arrangement method taking regard of energy saving and emission reduction. The method comprises steps that I, a multi-target power purchase model comprehensively taking regard of the least total power purchase cost, the least total energy consumption of a thermal power set, the least SO2 emissions and the least CO2 emissions is established; II, single-target power purchase models respectively taking regard of the least total power purchase cost, the least total energy consumption of the thermal power set, the least SO2 emissions andthe least CO2 emissions are established and solved; III, dimensionless processing is carried out based on the four indexes; IV, a weighted value of each index is determined according to actual conditions; and V, the index values after dimensionless processing and the respective weighted value are utilized to establish a target function, a multi-target optimization problem is converted into a single-target optimization problem, the particle swarm optimization algorithm is further utilized for solution, and an inter-provincial power purchase plan taking regard of energy saving and emission reduction is made. The method is advantaged in that for the made inter-provincial power purchase plan, energy consumption of the thermal power set and pollution gas emission are reduced on the condition that the power purchase cost can be reduced.

The invention provides a multi-objective optimal dispatching method of a power system based on a two-layer consistency algorithm. The process comprises the following steps: establishing a multi-objective optimal dispatching model of the power system; searching for the best condition; designing a two-level consistency algorithm to find out the optimal output of the generator and calculating the minimum value of comprehensive expectation. The invention considers the influence of the transmission loss on the system supply-demand balance and the unit output optimization, and can realize the real-time calculation of the transmission loss. The multi-objective optimization problem is transformed into a single objective optimization problem by using linear weighted sum method. The supply side cansatisfy its own interests by adjusting the weighted coefficient according to the demand of the enterprise. The optimal configuration of electric energy is realized, and the supply side is guided to make a reasonable capacity plan. On the premise of considering the transmission loss, it can meet the user's load demand and improve the economic benefit and social environmental protection benefit of the enterprise. Low dependence on communication and effective protection of enterprise and user privacy, and can achieve plug and play group and effectively respond to network topology changes.

The invention provides a charging station electric vehicle online smooth charging system and method. The method includes a queuing step, a mediation step, an online charging step and a power update step. For the problem, only one optimization object can be considered, which is to minimize the charging costs of a charging station, or to maximize the stationarity of the charging power of the charging station. In the system and method, the minimizing of the charging costs of the charging station, the stability of the whole charging power of the charging station and the stability of the charging power of charging piles can be simultaneously considered; a multi-target optimization model can be established; the multi-target optimization model can be converted into a simple target optimization problem by utilizing an efficacy function method; and the charging power of an electric vehicle at the charging station at each time can be solved by designing an online smooth charging algorithm. The system and method can effectively enhance the charging efficiency of the charging station and reduce the waiting time of the electric vehicle on charging; and the system and method have advantages of being smooth in charging, low in charging cost and stable in charging power.

The present invention discloses a cloud business uplink scheduling method based on improved Lyapunov optimization. The Lyapunov optimization method is improved, a virtual queue is established, the limiting condition of the cloud business is required uploading the certain amount of data volume in a certain time is converted to the queue stability problem so as to convert the original energy optimization problem to a dual-target optimization problem of joint optimization of the queue stability and energy. The utility function is employed to combine the dual-target optimization problem into a single-target optimization problem for solution. A guarantee mechanism is established to ensure the complete uploading of the data. The cloud business uplink scheduling method based on the improved Lyapunov optimization can obtain the analytical solution of the optimal scheduling policy in the condition of the unknown future channel gain, and the energy consumption of the uploading is far lower than that of the traditional algorithm.

The invention discloses a distributed robust optimization method for flexible resources of a power system. The method comprises steps of obtaining cost expectation and constraint conditions of power supply energy sources in the power system, and establishing a flexible resource robust optimization model; converting a flexible resource robust optimization model into a plurality of single-target optimization problems by adopting the duality method; and solving a plurality of single-target optimization problems to obtain a power system flexible resource distributed robust optimization scheme. Themethod is advantaged in that problems existing in flexible resource optimization in the prior art can be solved, the purpose of minimizing the total cost is achieved, moreover, the calculation burdencan be relieved, data privacy can be protected, and system stability can be improved.

The invention relates to a near-earth asteroid rendezvous task orbit optimization method based on hybrid power. The orbit optimization method comprises the following steps: establishing an asteroid detector kinetic model of solar pressure and solar photovoltaic hybrid propulsion power; an asteroid detection orbit optimization method is designed based on physical planning and a Gaussian pseudo-spectral method. The method has the advantages that a physical planning framework is adopted, and the multi-objective orbit optimization problem can be converted into a single-objective optimization problem reflecting the preference of a designer. An iterative optimization strategy based on a Gaussian pseudo-spectral method can effectively solve the problem of initial values of multiple variables in rendezvous orbit optimization. A multi-objective optimization simulation result shows that the hybrid small-row detector can generate different orbital forms to realize the minimum fuel consumption andthe shortest flight time. Simulation also shows that the task period of the hybrid power detector for continuously crossing the four near-earth asteroids is about five years, and is saved by about one and a half years compared with a solar sail detector for completing the same crossing task.

The embodiment of the invention discloses an optimization method for balancing energy efficiency and spectral efficiency in 5G IOT communication, and the method comprises the steps: firstly building asystem model, and obtaining the position information of user equipment; secondly, determining the number of activated antennas required by base station communication by adopting an MIMO antenna distribution mechanism; then proposing a multi-objective optimization problem taking simultaneous maximization of energy efficiency and spectral efficiency as an objective; rewriting a multi-objective optimization problem as a single-objective optimization problem, and simplifying the multi-objective optimization problem as a D.C. Planning problem; and finally, approximating D.C. The planning problem is a convex optimization problem, and the optimal sending power of the base station is obtained by adopting a weighing power distribution algorithm. According to the embodiment of the invention, the influence of the antenna selection sleep mechanism on the system energy consumption is considered, the resource allocation can be dynamically adjusted according to the energy efficiency and frequency efficiency preference requirements in engineering practice, the effect of optimizing the energy efficiency and the spectrum efficiency at the same time is achieved, and the high-speed and low-energy-consumption transmission of mass connections in 5G IOT communication can be realized.

The invention belongs to the technical field of power system power distribution network planning and operation, and particularly relates to a reconstruction method for a power distribution network with a distributed power supply after serious fault. The method for reconstructing the power distribution network with the distributed power supply after serious fault is suitable for rapid reconstruction of the power distribution network with the distributed power supply after the serious fault, and is convenient to model, high in simulation precision, high in calculation efficiency and strong in expansibility. The method comprises the following steps: 1, establishing an objective function and constraint condition model of a reconstruction problem after the power distribution network has the fault; 2, performing modeling analysis on the distributed power supply when power distribution network fault occurs; 3, adopting a spanning tree model based on the graph theory to solve an optimal methodof island division; 4, completing the weight dynamic division of a multi-objective function through a membership function, and normalizing a power distribution network reconstruction multi-objectiveoptimization problem into a single-objective optimization problem; and 5, solving the reconstruction optimization problem based on an empire competition algorithm to obtain an optimal reconstruction strategy after the serious fault of the power distribution network.

The invention discloses an MIMO radar sparse array optimization method based on an MODE algorithm, and relates to the technical field of radar imaging systems. The method comprises two steps to complete sparse array optimization. On the basis of completing the establishment of a covariance matrix, problem modeling is carried out mainly for sparse array optimization. According to the method, a CA+CA+DE algorithm is used for solving a single-objective optimization problem, then single-objective optimization is used as a constraint condition of the optimization, then the single-objective optimization problem is converted into a multi-objective optimization problem, and finally a multi-objective MODE optimization algorithm is used for solving to obtain a sparse array which meets directional diagram matching and inhibits side lobes. The method can converge to global optimization under the condition that the directional diagram is fixed, and the convergence speed is high.

The invention relates to a bat algorithm and a system, belonging to the field of data processing, and particularly relates to a single target optimization problem method integrating explosion strategy, opposition-based learning and bat algorithm and a system. In the optimization process, the opposition-based population of each bat in the bat algorithm is obtained by opposition-based learning, andthe elite selection is performed on the two populations, thus the diversity of the population is increased, and the global search ability is improved, the local optimal solution is avoided, the explosion strategy is added during the flight process, thus the local exploration ability of the algorithm is improved, and the optimal solution is more accurately searched. Through the simulation test of 12 typical test functions and knapsack problems, it is proved that DGOBA has higher efficiency for solving continuous and discrete optimization problems.

The invention discloses an AGN and CNN based optimizing method and device of a single-objective optimization problem. A trained AGN coding part is used to convert an optimization problem model in which the amount of decision variables is k (k is not 2) into an optimization problem model in which the amount of decision variables is 2, the two converted decision variables are input to a trained CNNtwo-decision-variable optimizing device to output a global optimal result rapidly, a trained AGN decoding part is used to convert the two decision variables in the global optimal result output by theCNN two-decision-variable optimizing device into an original space in which the amount of decision variables is k, and a final global optimal result of the optimization problem is output. Via the technical schemes provided by the invention, optimizing calculation of the CNN two-decision-variable optimizing device is linear parallel calculation, the optimizing speed is high, global optimization canbe searched directly, and the condition of local optimization is avoided. In addition, the method and device are also suitable for constrained optimization problems.

The invention discloses an electric vehicle and new energy scheduling optimization modeling and algorithm considering a Vehicle-to-Grid (V2G) function. An electric power system containing an electricvehicle capable of being connected into a network, wind power and solar energy serves as a study object, a scheduling method considering the V2G function is constructed and aims to simultaneously lower system load fluctuation and the charging cost of an electric vehicle user, through fuzzy processing of multiple targets, a to-be-solved problem is converted into a single target optimization problem, a latest vertical and horizontal cross optimization algorithm is applied to conduct solving, and an optimal scheduling scheme is obtained.

The invention relates to a design optimization method of a wave-transparent, invisible and heatproof radome, and belongs to the field of radome design. In order to convert a multi-objective optimization problem into a single-objective optimization problem which is easier to solve under the condition that an objective weight factor is not introduced, and enable the electrical performance and the stealth performance of the radome to be optimal synchronously, the method comprises the steps of: establishing a mapping relationship between a radome design variable and a state parameter; determiningthe specific expression form of the radome design variable; determining an objective function and constraint conditions in the radome multidisciplinary design optimization problem; representing the stealth performance by using the electrical performance index, and reducing the dimension of the target function; constructing a radome multidisciplinary optimization module and an analysis module, obtaining sensitivity information, constructing a linear approximation function, and completing multidisciplinary design optimization by adopting a single-stage optimization algorithm. Therefore, the difficulty of optimization design is reduced, the performance of the radome is synchronous and optimal, and the design optimization period is shortened.

The invention relates to a grid-connected microgrid day-ahead multi-objective optimization method considering the influence of an energy storage system, and the method comprises the following steps: 1) building a multi-objective microgrid optimization model considering the energy storage system according to the wind speed, illumination intensity and temperature prediction value in the future 24 h;(2) converting a multi-objective optimization problem into a single-objective optimization problem according to the linear weighted summation method; and (3) solving the single-objective optimizationproblem to obtain an optimal operation scheme. Compared with the prior art, the method has the advantages of considering an energy storage system, reducing the operation cost of the system and the like.