108 results about "Bilevel optimization" patented technology

The invention belongs to the field of micro-grid optimization planning and design and relates to a double-layer optimization planning and designing method for a combined cooling, heating and power micro-grid system. The smallest whole life circle economic present value and the minimum CO2 emission are taken as the optimization target of the method, a multi-target genetic algorithm based on NSGA-II is adopted for an outer layer optimization module, and the optimal equipment combination and the equipment capacity of the system are sought according to input equipment physical parameters of the system, input economic parameters and the optimization scheduling result output by an inner layer optimization module; the hybrid integer linear programming algorithm is adopted for the inner layer optimization module, with the lowest system operation cost as the optimization target, the optimal operation strategy on a typical day of the system is obtained according to the optimization configuration scheme given by the outer layer optimization module, the optimization scheduling result of the system is output by the inner layer optimization module, and the system operation cost is transmitted to the outer layer optimization module. The method can achieve combined optimization of the configuration and the operation strategy of the combined cooling, heating and power micro-grid system.

The invention discloses a distribution network expansion planning method considering the optimized operation of a regional integrated energy system. Firstly, a regional integrated energy system modelis established and the internal energy flow relationship is analyzed. Secondly, based on the multi-agent system, a three-layer interactive structure including the main layer, the regional layer and the device layer is built to realize the interaction of different entities on the spatial scale. Then, a bi-level optimization model is established for the distribution network with residential, commercial and industrial regional integrated energy systems. The upper level is used for the expansion planning of distribution network, and the lower level is used for the optimization operation of regional integrated energy systems. Genetic membrane algorithm and sequential quadratic programming are used to solve the upper and lower level models nested nested. Finally, the simulation example shows that the distribution network planning considering the optimal operation of regional integrated energy system can reduce the total cost of planning, improve energy efficiency and new energy consumption rate.

The invention discloses a regional integrated energy system planning optimization method based on double-layer optimization, and the method comprises the following steps: S1, selecting a plurality ofgroups of capacity schemes to carry out the capacity optimization of a regional integrated energy system, and finding out a plurality of groups of capacity optimization configuration schemes of the regional integrated energy system; S2, performing optimization operation on the plurality of groups of capacity optimization configuration schemes planned in the step S1 to obtain a plurality of groupsof operation output results of the regional integrated energy system, and then solving an optimization operation problem of the system by utilizing mixed integer linear programming (MILP); S3, carrying out capacity selection, and comparing multiple groups of operation output results in the step S2 with load requirements to obtain the load supply reliability of each group of planning schemes; S4, feeding the obtained reliability back to the capacity optimization planning in the step S1, reselecting and optimizing the capacity optimization of the regional integrated energy system, and repeatingthe steps S1 to S1; and S3, selecting a capacity planning scheme with the strongest power supply reliability.

The invention discloses a coordinated optimization scheduling method for multiple microgrids and a power distribution network. The method comprises the following steps: S1, establishing an upper-layerpower distribution network optimization scheduling model; S2, establishing a lower-layer multi-microgrid optimization scheduling model; S3, carrying out decoupling operation according to the interaction variables between the upper-layer scheduling model and the lower-layer scheduling model which are established in the step S1 and the step S2, and integrating the upper-layer scheduling model and the lower-layer scheduling model to establish a double-layer optimization scheduling model; S4, according to the double-layer optimization scheduling model obtained in the step S3, setting a double-layer cycle according to a target shunting method to determine an optimal strategy. According to the technical scheme, different optimization decision targets of the power distribution network and the microgrids, interruptible loads in the micro-grid and influences of interruptible loads and electric vehicles in the microgrids are comprehensively considered. Through double-layer planning and load andpower generation prediction, the overall benefits of the microgrids and the power distribution network are maximized on the premise that constraint conditions are met. The problem that the income ofthe power distribution network is only considered when the benefits of the microgrids are ignored in traditional microgrid system optimization scheduling is solved.

The invention discloses a layered and partitioned optimization operation method for a comprehensive energy system based on a master-slave game. The method comprises the steps of establishing a master-slave game layered and partitioned architecture of a multi-park comprehensive energy system; based on a master-slave game layered and partitioned framework of the multi-park comprehensive energy system, establishing a game main body optimization model of the multi-park comprehensive energy system and a game slave body optimization model of the multi-park comprehensive energy system; combining a game subject optimization model of the multi-park comprehensive energy system and a game slave optimization model of the multi-park comprehensive energy system to jointly form a master-slave game double-layer optimization operation model of the multi-park comprehensive energy system; and solving the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system by adopting a chaos adaptive particle swarm optimization algorithm in combination with a mixed integer linear programming method. The complementary advantage of cold, heat, electricity and gas energy sources is effectively exerted, the comprehensive operation cost of the system is further reduced, and economic, flexible, cooperative and optimized operation of the multi-park comprehensive energy system is achieved.

The invention discloses a photovoltaic micro-grid interval optimization scheduling method with multi-time scales, and belongs to the field of renewable energy source micro-grid techniques. The photovoltaic micro-grid interval optimization scheduling method comprises the following steps: by using an interval value description method, describing the intermittency of renewable energy and inaccuracy characteristics of load prediction; meanwhile, by adopting a dual-layer optimization strategy, providing active power instructions for operation of different micro-grid components within 24 hours of a former day and in real time (every 15 minutes); and because of a relatively large amount of indefinite factors in the operation process of a micro-grid and a limited rotation reserve capacity available inside a system, generally confirming positive and negative rotation reserve capacities purchased from a power distribution grid by the micro-grid according to a current scheduling plan, and coordinating operation of different components of the micro-grid. The photovoltaic micro-grid interval optimization scheduling method is capable of solving the problems that the current scheduling is deviated from actual operation situations and the like because of unbalanced micro-grid power, and has the advantages that real-time scheduling can be implemented to comply with the current rotation reserve plans, the plans can be adjusted according to real-time error situations, intermittent energy fluctuation can be balanced, and economic operation of the system can be ensured.

The invention discloses a Nash game-based microgrid voltage regulation method and apparatus. The method comprises the steps of establishing a voltage regulation game model of a microgrid participator;establishing strategy space of participators, wherein the strategy space of each participator comprises a decision variable and a constraint condition; determining the strategy space according to electricity prices and smooth and steady conditions; determining a system benefit function and a constraint condition of the voltage regulation game model; judging whether to execute Nash balance or not;if not, returning to the step 3; and if so, converting the voltage regulation game model into a dual-layer optimization model and performing solving. By virtue of the Nash game-based microgrid voltage regulation method and apparatus, the maximum of the system comprehensive benefit under access of the multiple distributed generations can be realized.

The invention relates to the technical field of power systems and automation thereof, and particularly relates to a virtual power plant double-layer optimization model considering network constraints.The method comprises the specific steps of establishing an upper-layer virtual power plant profit maximization model, establishing a lower-layer market clearing model considering network constraints,converting the double-layer optimization model into a mathematical programming model with balance constraints, and converting the mathematical programming model with the balance constraints into a mixed integer programming model. In order to overcome the defect that line transmission power constraint is not considered in current related research, internal optimization of the virtual power plant and interaction with a market operator and other conventional units are fully considered, a market bidding optimization model in which the virtual power plant participates is established, an optimization bidding strategy is generated under the condition that line power transmission is blocked, the superiority of the bidding strategy is embodied, the profit of the virtual power plant is improved, and the model is close to the practical situation. Scientific theoretical support is provided for the virtual power plant to effectively participate in the power market in future.

The invention relates to a security analysis method for a power system suffering from a terror attack. The method comprises the following steps: 1) establishing a double-layer mixed integer nonlinear optimization model regarding the terror attack; 2) replacing an inner layer optimization model in the double-layer mixed integer nonlinear optimization model by adopting a KKT (Karush-Kuhn-Tucker) condition to transform the double-layer optimization model into a single-layer optimization model; 3) performing active and reactive power decoupling on the single-layer optimization model to decompose the single-layer optimization model into an active power optimization sub-model and a reactive power adjusting sub-model; 4) simplifying the active power optimization model into a combined model according to a Lagrangian multiplier of the KKT condition, and performing approximate solving by using a max-flow min-cut model; and 5) alternatively solving the active power optimization sub-model and the reactive power adjusting sub-model till the voltage difference of two times of iteration satisfies a corresponding convergence condition, and finally obtaining an optimal solution. Compared with the prior art, the method has the advantages that solution nesting is avoided, the model is comprehensive, the result is accurate and the like.

The invention relates to an optimal configuration method for a micro grid, which comprises the following steps: S1, acquiring the topological structure, basic parameters and power generation data of amicro grid; S2, constructing the actual unbalanced power between the output of renewable energy and the load; S3, establishing a water / wind / diesel micro grid double-layer optimal configuration model,wherein the model includes inner and outer optimization models, the outer layer is a multi-objective optimized micro power configuration model, and the inner layer is a micro grid operation model considering uncertainty; S4, transforming a constraint with random variables into a deterministic model; S5, combining an elitist strategy-based non-dominated sorting genetic algorithm and a mixed integer linear programming (ILP) algorithm to solve the double-layer optimal configuration model; and S6, selecting a most satisfactory configuration scheme from a Pareto solution set by using a TOPSIS method. The method of the invention not only improves the robustness of the micro grid, but also ensures the reliability and economy of the configuration scheme and provides technical support for the micro grid planning work.

The invention discloses a large power grid adaptive robust backup optimization method considering risk costs, which can comprehensively consider the risk costs caused by the cooperative scheduling ofmultiple-type generator sets and the uncertainty of new energy output. The method includes the following steps: firstly, depicting the uncertainty of output by using a new energy polyhedron uncertainty set, establishing a risk model of expected abandoning new energy and expected load shedding taking into account the error probability of new energy output, comprehensively considering the operationcharacteristics of multi-type power-supply sets, taking the power generation benefits and potential expected risks as backup targets, constructing a rotating backup optimization model for the cooperative scheduling of the multi-type power-supply sets considering the risk costs, and proposing a two-layer optimization algorithm combining an adaptive genetic algorithm and a branch cutting algorithm to solve the constructed model. According to the backup method disclosed by the invention, the optimal uncertainty of the new energy can be adaptively searched, the economic benefits and the risk costsof a backup strategy can be reasonably weighed, and the optimal comprehensive benefits can be realized.

The invention discloses a comprehensive energy market competition strategy determination method, device and equipment and a storage medium. The method comprises the steps that a double-layer optimization model of a comprehensive energy market competition strategy is constructed; wherein the upper-layer model of the double-layer optimization model is a competition strategy optimization model whichtakes energy selling price data, energy quotation data and energy demand quantity data as decision variables for a comprehensive energy service provider, and the lower-layer model of the double-layeroptimization model comprises an energy market clearing model and a user response model based on price demand elasticity; performing structure conversion on the double-layer optimization model to obtain a single-layer linear constraint quadratic programming problem; and determining an optimal competition strategy of the comprehensive energy market based on the quadratic programming problem. By means of the technical scheme, the purpose of determining the optimal competition strategy of the comprehensive energy market is achieved, and the method has the advantages of being high in processing speed and high in precision.

The invention relates to an interconnected microgrid energy storage capacity expansion optimization method. A multi-stage interconnected microgrid energy storage capacity double-layer optimization model is constructed by setting a planning period and considering year-by-year increase of a load peak value, an NDDG unit and DDG unit configuration capacity. The upper-layer model takes the lowest annual net present value of the interconnected microgrid in the yth year after the capacity expansion of energy storage units as a target, and optimizes the capacity of each energy storage unit additionally arranged in each microgrid in the year on the basis of the energy storage configuration capacity in each microgrid in the (y-1)th year; and the lower-layer model formulates an optimal operation strategy by taking the lowest total operation cost of the interconnected microgrid in each operation scene in the yth year as a target. The double-layer optimization model is constructed into a large-scale double-layer mixed integer linear programming problem, and the rated capacity and rated power of energy storage of each microgrid in the year are optimized by taking the lowest total cost of the energy storage units in the microgrid i after capacity expansion in each operation scene s in the yth year as a target, and the operation strategy of the interconnected microgrid in the scene after energy storage and capacity expansion is optimized.

The invention provides a microgrid energy storage configuration method considering the schedulability of a microgrid system. According to the method, the schedulability of the microgrid is modeled with entropies; based on a double-layer optimization model, a battery energy storage capacity optimization configuration model considering a schedulability constraint is put forwards; and the optimization configuration model is solved, and a microgrid energy storage configuration scheme considering the schedulability of the microgrid is obtained. Compared with an existing power prediction error probability distribution-based energy storage configuration method, the optimization model provided by the invention additionally considers the schedulability constraint of the microgrid, optimizes the rated power and rated capacity of energy storage, and effectively considers the influence of operating state quantities such as energy storage state of charge and cumulative life loss on the operation and energy storage configuration of the microgrid; and the configuration result of the method is beneficial to the realization of the user-friendly access of the microgrid to an external power grid.

A multi-object tracking system and method are provided. The multi-object tracking system includes at least one camera configured to capture a set of input images of a set of objects to be tracked. The multi-object tracking system further includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The multi-object tracking system also includes a processor configured to (i) detect the objects and track locations of the objects by applying the learning model to the set of input images in a multi-object tracking task, and (ii), provide a listing of the objects and the locations of the objects for the multi-object tracking task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.

The invention discloses a distributed power supply optimal connection position and capacity planning method taking multiple subjects into consideration. The method includes the steps: building a distributed power supply model, namely, a prediction model of photovoltaic power generation system output; building an outer layer optimization model taking a power distribution network as a subject; building an inner layer optimization model taking a user as a subject; building a distributed power supply optimal access location and capacity solution algorithm based on a genetic algorithm. According tothe method, a distributed power supply model can be accurately built, many-sided benefits are comprehensively considered, and distributed power supply optimal access capacity and location is determined by two-layer optimization and multi-subject game. An important basis can be provided for optimal access of a distributed power supply in a power distribution network, an important foundation servesas optimal planning of an active distribution network, the power distribution network is planned based on the important foundation, adverse effect of a distributed power supply on the power distribution network can be decreased, efficient, economical and excellent running of the power distribution network is facilitated, the planning level of an urban distribution network can be improved, and reasonable development of an urban active distribution network is facilitated.

The present disclosure discloses a method for optimizing equipment capacity and equipment power of an energy hub system. The method includes establishing an energy hub model containing natural gas boilers, electric boilers, coolers and heat pumps, establishing a bilevel optimized upper model to solve the optimal heat pump capacity, and establishing a bilevel optimized lower model to solve the optimal power utilization of each energy device based on the binary search algorithm of the quadratic function solves the upper model by using the multi-objective evolutionary algorithm NSGA-II to solve the lower model. The optimization method of the present invention can solve the multi-objective bilevel model problem without the help of commercial optimization software. Obtaining a reasonable, efficient and green planning scheme makes the total operating cost and total exhaust gas emissions of the energy hub relatively optimal.

The invention provides a multi-energy park scheduling method and system based on double-layer reinforcement learning. The method comprises the steps of: obtaining scheduling controllable objects, i.e., a source side unit, a load side unit, an energy conversion unit and a storage unit, in an integrated energy system, constructing a double-layer optimization decision model which comprises an upper-layer reinforcement learning sub-model and a lower-layer mixed integer linear programming sub-model, enabling the upper-layer reinforcement learning sub-model to acquire action variable information ofthe storage unit under the state variable information at the current moment and transmit the action variable information to the lower-layer mixed integer linear programming sub-model, enabling the lower-layer mixed integer linear programming sub-model to acquire a corresponding award variable and state variable information of the storage unit at the next moment, and feed back the award variable and the state variable information to the upper-layer reinforcement learning sub-model, and iteratively executing the above steps until the scheduling is finished. According to the embodiment of the invention, through a data-driven reinforcement learning method, a decision only needs to be made according to the current state, future information does not need to be predicted, the decision timelinessis high, the decision effect is excellent, and a real-time optimization decision can be realized.

The invention discloses a calculation unloading method in a multi-user multi-edge server scene, and belongs to the field of mobile cloud computing. The method comprises the following specific steps that: 1, a controller models a multi-user multi-edge server calculation unloading problem by utilizing a calculation unloading modeling method under a multi-user multi-edge server scene according to collected all user information and a target of a system, so that a double-layer optimization model is obtained; 2, the controller converts an original double-layer optimization problem into an equivalentsingle-layer optimization problem according to the characteristics of the initial problem model obtained in the previous step; and 3, the controller solves the single-layer optimization problem modelobtained in the previous step by using a branch limit method. According to the method provided by the invention, the problems of calculation time delay and calculation energy of the user are considered at the same time, the diversity of user requirements is met, and the economic benefit is greatly improved; according to the method provided by the invention, local and cloud computing costs of theuser are simultaneously considered, and the computing cost of the overall user is effectively reduced.

The invention relates to a scene analysis method and considerable voltage adjustment strategy based distributed photovoltaic optimization planning method. The method comprises the steps of 1, carrying out scene analysis by utilizing the scene analysis method, adopting a high-dimensional vector to represent the real running states of a distributed power source and a load, and realizing scene compression through a clustering method; and 2, building a distribution network distributed power source double-layer optimization planning method, wherein the step 2 comprises the sub-steps of (1) planning to build multiple targets by using minimum network damage of a distribution network and the maximum profits of a distributed power source operator at the upper layer, and planning the access capacity of the distributed power source; and (2) adopting different scheduling strategies in a scheduling model in the lower layer, comparing unused voltage adjustment strategies, the adopted voltage adjustment strategy and a voltage adjustment strategy with reactive compensation, and planning the distributed power source.

The invention discloses an optimal scheduling method based on a multi-energy complementary micro-grid and an active power distribution network. The method comprises the following steps: respectively establishing an optimal scheduling target model of the multi-energy complementary micro-grid and an optimal scheduling target model of the active power distribution network; establishing an optimization scheduling constraint condition; establishing a double-layer optimization model by taking the optimization scheduling target model of the multi-energy complementary micro-grid and the optimization scheduling target model of the active power distribution network as optimization targets and the optimization scheduling constraint conditions as constraint conditions; and solving the double-layer optimization model, and carrying out optimization scheduling operation based on a solving result. According to the method, coordinated optimization of the multi-energy complementary micro-grid and the active power distribution network is realized, various energy requirements in the micro-grid can be met, gradient utilization of different energy sources is realized, the operation state of the active power distribution network can be considered, support is provided for safe operation of the power distribution network, and the energy benefit of a user-side comprehensive energy system can be improved.

The invention relates to an incremental distribution network double-layer optimal allocation method with distributed power supplies, and belongs to the technical field of power system planning. The technical scheme is as follows: obtaining the data of the distributed resources such as the regional wind speed and the sunlight intensity of the incremental distribution network in a certain time series and analyzing and modeling the DG and the load of the distribution network; classifying the uncertainty factors of the DG and constructing a comprehensive fuzzy evaluation method to evaluate the uncertainty of distributed power generation on the basis of fuzzy mathematics theory and analytic hierarchy process (AHP); establishing the mathematical model of incremental distribution network system planning and design based on the double-layer optimization theory and determining the upper-layer capacity optimization model and the lower-layer dispatching optimization model as well as the related constraints; and selecting the optimal solution by the system. According to the incremental distribution network double-layer optimal allocation method with the distributed power supplies, a better reference can be provided for planning and decision-making of the incremental distribution service.

The invention discloses a distributed power flow controller configuration optimization method comprising the following steps: collecting the information of a power system; building a first-layer optimization function according to the constraint of the total number of relax distributed power flow controllers installed, and calculating the maximum amount of new energy consumed; and building a second-layer optimization function with the maximum amount of new energy consumed as an additional constraint, and calculating the maximum number of distributed power flow controllers installed. Based on the operation characteristics of distributed power flow controllers and by considering the installation quantity constraint of distributed power flow controllers on each line, the operation parameter constraint and the traditional power system operation constraint, the maximum amount of new energy consumed and the optimal configuration scheme of distributed power flow controllers under the performance index conditions can be calculated using the idea of double-layer optimization, which can provide guidance for the installation of distributed power flow controllers.

The invention relates to a planning method for DC upgrade and reconstruction of the multi-stage AC / DC hybrid power distribution network. The method comprises steps that based on a current actual source-load operation scenario, a genetic algorithm is utilized, an AC line and an AC node in the AC / DC hybrid power distribution network topology obtained in previous stage planning are subjected to DC reconstruction to obtain multiple power distribution network topology sub generations based on the power distribution network topology, the minimum output of a unit is taken as a sub goal, nested optimization is based on the minimum sum of investment expense and operating expense, the optimal power distribution network topology sub generation is screened, the corresponding AC / DC hybrid power distribution network topology is obtained, and the above process is repeated. The method is advantaged in that the AC line and the AC node in the previous planning result are subjected to DC reconstruction,the minimum expense of the reconstruction process is taken as a main goal, multi-stage reconstruction is performed, orderly introduction of DC components in the power distribution network can be effectively realized, moreover, the idea of two-layer optimization is introduced, the genetic algorithm is improved for optimal planning, and planning problem solving is realized.

The invention provides a multi-energy complementary system two-stage optimization scheduling method and system considering source storage load cooperation, and the method comprises the steps: first-stage optimization: obtaining load data, taking economic optimization as a target, taking the comfort degree of a user as a constraint, obtaining the optimal load data through the optimization of a genetic algorithm, and taking the optimized load as the input of second-stage optimization; second-stage optimization: optimizing equipment output and an energy storage state on the basis of random dynamic planning by taking the lowest operation cost as a target, and outputting the operation cost to first-stage optimization; performing loop iteration on the first-stage optimization and the second-stage optimization to obtain an optimal load curve and an optimal operation scheduling plan of the system, and realizing source-load optimal matching. Through double-layer optimization loop iteration, theoptimal load curve and the system operation plan are finally obtained, demand side response, energy storage and random optimization are effectively unified in one optimization framework, the source-load random problem is effectively solved, source-load optimal matching is achieved, and the economy of the system is further improved.

The invention relates to a distributed power source configuration method considering dynamic path optimization of an active distribution system, comprising the following steps: 1) establishing a double-layer optimal configuration model of the distributed power source considering dynamic path optimization of the active distribution system; 2) according to the improved OFCMC method, the dynamic pathoptimization problem of the active distribution system is transformed into a static path optimization problem in which C representative load data are used as clustering centers; 3) the double-layer optimal configuration model of the distribute power source is solved to obtain the optimal configuration scheme. Compared with the prior art, the invention has the advantages of considering the operation dispatch factor, reasonable modeling, good applicability of the scheme and the like.

The invention discloses a method for optimizing heat pump capacity and energy equipment power in an energy hub. The method comprises the following steps: establishing an energy hub model containing anatural gas boiler, an electric boiler, a cooler and a heat pump; establishing a double-layer optimized upper-layer model, and solving the optimal heat pump capacity; establishing a double-layer optimized lower-layer model, and solving the optimal power of each energy device; solving the upper-layer model by utilizing a binary search algorithm based on a quadratic function; and solving a lower-layer model by using a multi-objective evolutionary algorithm NSGAII. By adopting the optimization method provided by the invention, the problem of a multi-target double-layer model can be solved withoutthe help of commercial optimization software. And a reasonable, efficient and green planning scheme is obtained, so that the total operating cost and the total exhaust emission of the energy hub arerelatively optimal.

The invention discloses a transmission network short-term congestion management method for transmission and distribution cooperation, It is a bilevel optimization method that equates a distribution network with a virtual power plant to participate in a transmission network congestion management market, in which the upper layer is transmission network layer and the lower layer is distribution network layer, which is suitable for short-term congestion management of transmission network when multi-distributed generation and demand-side response resources are connected to distribution network. Based on minimizing the congestion management cost of the transmission network to fully utilize the resources of the transmission and distribution network, the invention proposes a transmission network congestion management bi-level optimization model of transmission and distribution cooperation. The transmission network layer determines the congestion management scheme according to the quotation andcapacity information of each distribution network and the power plant; the transmission network layer determines the congestion management scheme according to the quotation and capacity information of each distribution network. Each distribution network determines the dispatching plan according to the adjustment requirements of the transmission network layer, and updates the quotation and capacity information to return to the transmission network layer. The invention utilizes the good function of the virtual power plant in the electric power market, maintains the existing dispatching mode andthe market structure of the transmission and distribution network, solves the transmission network congestion management problem of the transmission and distribution cooperation through the double-layer optimization method, and is conducive to making full use of the dispatchable resources of the distribution network and reducing the congestion management cost. The invention has the advantages ofhigh calculation accuracy and wide applicability.

The invention discloses an optimal scheduling method for a distributed new energy power distribution network, and aims to solve the problem in optimal scheduling of a power distribution network containing a distributed new energy power supply under the maximum power supply capacity. The method comprises: establishing a power grid dispatching double-layer model considering the output uncertainty ofthe distributed new energy; converting the model with the lowest output cost of the lower-layer power distribution network into a linear constraint condition by adopting a KKT condition; converting the double-layer optimization model into a single-layer linear optimization problem; increasing the load capacity in a variable step size manner until the maximum load capacity is reached; and finally,obtaining a power distribution network optimization scheduling scheme considering the uncertainty of the distributed new energy output under the maximum load capacity. According to the method, the lowest power supply cost of the power distribution network and the worst new energy output uncertainty in the power market environment are considered, so that the power grid optimal scheduling scheme when the power supply capacity of the power distribution network is maximal is obtained, the calculation process is simplified, the calculation efficiency is greatly improved, and the method has guidingsignificance in practical application.

The invention discloses a distribution network self-healing optimization method considering both safety and economy, which is applied to a distribution network comprising distributed generation and isoptimized by adopting a double-layer planning model. The double-layer planning model comprises an upper-layer optimization model and a lower-layer optimization model, wherein the upper-layer optimization model takes the highest safety index as an objective function, and the lower-layer optimization model takes the lowest power supply recovery cost as an objective function; and the double-layer planning model takes an on-off operation mode of a tie line switch in the distribution network during a fault as a decision variable. According to the invention, the distribution network self-healing double-layer optimization model based on the safety and the power supply recovery economy of the distribution network is established in allusion to the optimization problem of a self-healing scheme after a large-area power failure of the distribution network, so that an obtained result not only can meet the expectation of an upper-layer decision maker on safety but also can meet the requirement of alower-layer decision maker on economy, and effective compromise and consideration of benefits of the upper-layer decision maker and the lower-layer decision maker are realized.