2007 results about "Particle swarm algorithm" patented technology

The invention discloses a method for solving a multiple-depot logistics transportation vehicle routing problem. The method comprises steps as follows: inputting multiple-depot problem basic parameters based on real-time traffic information, establishing a multiple-depot logistics transportation scheduling mathematic model based on the real-time traffic information, adopting a clustering analysis method, introducing the particle swarm optimization algorithm to adjust and optimize ant colony algorithm pheromones, optimizing ant colony algorithm heuristic factors with the particle swarm optimization algorithm, solving an optimal distribution route, and establishing a mathematic model according to the multiple-depot logistics transportation vehicle routing problem based on the real-time traffic information; taking distances between clients and parking lots as main factors, performing area division on the clients and the parking lots with the clustering analysis method, and converting a multiple-depot problem into a single-depot problem; introducing the particle swarm optimization algorithm to improve the ant colony algorithm to solve the model. The method has the better global and local optimization capacity and has higher efficiency and stability when solving the multiple-depot problem.

The invention relates to a static path planning method of a robot. The method comprises that a target point is set, and an artificial potential field is established within the map range by taking the target point as a terminal point; particle swarm optimization is used, a start point of the robot is provided with m particle swarms, the flight speed of the ith particle in the tth step is vi(t), simulated walking along the path from the start point to the terminal point is carried out on each particle by combining the artificial potential field with the particle swarm optimization, and each particle forms self movement track in the simulated walking process; most particles are converged to one of the multiple tracks gradually, and an optical walking path from the start point to the terminal point is obtained within the map range; and the robot moves from the start point to the terminal point according to the optimal walking path. According to the invention, the potential field method, the grid method and the particle swarm optimization are combined, potential field distribution in a grid map is calculated directly, the pre-planned path is obtained from the target point of the potential field along the direction in which the potential field decreases most rapidly, the method is safe and effective, and path planning is accurate and reliable.

The invention relates to a wind power station energy storage capacity control method based on particle swarm optimization. The wind power station energy storage capacity control method includes the steps of taking the interval reference value of the wind power station output power which is adapted to the dispatching cycle of a power grid as a foundation, taking the influence of the wind-abandoning energy of a wind power station and the lost energy of an energy storage system into consideration, taking the lowest costs of the energy storage investment and a wind and power operation system as target functions, establishing a policy model for energy storage capacity optimizing based on a storage battery energy storage system, and then applying the improved particle swarm optimization to solve the functions. By the aid of the wind power station energy storage capacity control method based on the particle swarm optimization, the wind power which is output under effect of the energy storage system can be output smoothly at intervals, so that effective connection between the energy storage system and the existing dispatching operation manner can be realized, and the best economic benefit can be achieved simultaneously.

The invention provides a mobile robot formation control method based on leader-follow. The method is formed by a global positioning system, a wireless communication system, an algorithm processing system, and a speed control system. The global positioning system obtains the pose information of each robot and sends the pose information to an arithmetic processing system through a wireless communication system, and the formation motion control is finally realized through the information interaction with the speed control system. In a control algorithm, firstly a leader-follow formation motion model is established, a follow robot motion control rate is given, then a follow robot trajectory prediction model is established, a nonlinear least squares method prediction model is employed, a prediction model parameter is optimized by using an improved particle swarm algorithm, a communication data abnormal range is defined, and a prediction point is started to substitute an abnormal point so as to ensure formation motion. According to the method, the prediction model is introduced, the formation order deviation phenomenon caused by temporary communication abnormality, the reliability of follow robot motion is ensured, and the stability of the formation is greatly improved.

The invention discloses a method for estimating the health state of a power battery. The method comprises steps of acquiring the constant-current charging voltage V, current I and time t of a batteryto obtain charging capacity Q, establishing a V-Q relationship curve, acquiring a capacity increment curve peak value and peak position information, establishing a RBF neural network, training a RBF neural network model by a particle swarm optimization algorithm, and estimating the health state of the battery by using the generated RBF neural network. The method establishes a mapping relationshipamong the constant-current charging capacity increment curve peak value, the peak position and the health state of the battery by means of a data driving mode without establishing an equivalent circuit of the electric vehicle power battery, thereby improving estimation accuracy and realizing online real-time estimation for the overall estimation of a battery pack.

The invention relates to a WSN routing optimization method based on improved PSO. According to the method, a relational matrix containing topological structure information of the whole network serves as a PSO coding manner and is used for handling the problem of routing optimization; and intersection and variation mechanisms of a genetic algorithm are used to realize global convergence and search. The method comprises the steps of initializing parameters, initializing swarms, calculating the particle fitness, searching for Pbest and Gbest, introducing the intersection and variation mechanisms, and updating the Pbest and Gbest. According to the invention, the structure is simple, the intersection and variation mechanisms of the genetic algorithm are used to realize global convergence and search, optimized solution is realized, generation of a redundancy space and redundancy search are reduced via the relation matrix coding manner, and the instantaneity and stability of the method are improved.

The invention discloses a combined cold heat and power supply microgrid multi-objective dynamic optimal operation method; characteristics of translatable electrical load are firstly considered in an optimization process, then schedulability of source side and energy storage system are considered, contribution in each period in three kinds of controllable units serves as optimization variables, minimum system operation cost and minimum pollutant emission control expense serve as optimal operation targets, and a mathematical model of current multi-objective optimal operation problem is established; an excellent particles leading multi-objective particle swarm optimization algorithm is adopted to solve the optimization problem, that is, a single objective genetic algorithm is utilized to respectively find two points including minimum system operation cost and minimum pollutant emission control expense, and the two points serving as excellent particles is utilized to lead an optimal direction of the multi-objective particle swarm algorithm; the invention provides an effective multi-objective dynamic optimal operation method, and the method is significant for improving energy source comprehensive utilization efficiency of a multiple energy coupled system and promoting renewable energy source development.

The invention relates to a multi-target reactive power optimization method, in particular to a multi-target reactive power optimization method based on an adaptive chaos particle swarm algorithm. The method aims at solving the problem that multi-target reactive power optimization control variables are probably trapped in a locally optimal solution, and the speed for acquiring an optimal solution is low. The method includes the steps that firstly, original data of a particle swarm are input to an adaptive chaos particle swarm optimization algorithm program; secondly, first m particles are selected from the particle swarm as initial positions of the particle swarm according to fitness values in a preferred mode; thirdly, inertia weights w of the particles are acquired through calculation of inertia weight coefficients, and first M preferred particles are selected from the particle swarm for chaos optimization calculation; fourthly, the speed and the positions of the particles are updated according to the particle swarm reactive power optimization algorithm, and then iteration allowances and values of the control variables can be acquired; fifthly, whether iteration stop conditions are met or not is judged, and then the multi-target reactive power optimization method based on the adaptive chaos particle swarm optimization algorithm is finished. The multi-target reactive power optimization method is applied to the field of electric systems.

The present invention discloses a method and system for collaborative scheduling of production and transportation in supply chains based on improved particle swarm optimization. The method includes the following steps: 1. setting algorithm parameters; 2. randomly generating an initial population; 3. correcting codes; 4. calculating fitness values and updating the speed and the position of particles; 5. performing tournament selection; 6. performing crossover mutation; 7. updating the population; and 8. determining whether a termination condition is satisfied; if so, outputting a globally optimal solution; if not, returning to the step 3. In the present invention, an approximately optimal solution can be obtained in view of the collaborative scheduling problem of production and transportation considering distributed storage, so that the cost is reduced for supply chains and the service level of supply chains is enhanced.

The invention discloses a travel time prediction method for optimizing an LSTM neural network through a particle swarm optimization algorithm, and the method comprises the following steps: S1, collecting travel time data, performing data normalization, and dividing the data into a training set and a test set proportionally; S2, optimizing each parameter of an LSTM neural network prediction model by using the particle swarm optimization algorithm; S3, inputting the parameters, optimized through the particle swarm optimization algorithm, and the training set, and performing the iterative optimization of the LSTM neural network prediction model; S4, predicting the test set through the trained LSTM neural network model, and evaluating a model error. The method is quick in optimization. Compared with a random forest, SVM and KNN in the traditional prediction algorithm, the method of the invention has the least mean square error and square error for the data prediction, and the model reducesthe calculation burden, so the method shows better prediction performance.

The invention discloses an optimized scheduling method for a wind-solar storage hybrid system. A wind-solar output power reference value is set and day-ahead prediction output curves and load prediction curves of wind power and photovoltaic energy are obtained; a wind-solar storage energy output curve as well as an operation mode of an energy storage device are determined; a multi-objective optimization scheduling model is established by taking intra-day operation cost minimization of system scheduling as an optimization objective; and then an input load prediction curve, a wind-solar storageenergy output curve and the operation mode of the energy storage device are inputted, and the optimization scheduling model is calculated by using an improved particle swarm optimization algorithm toobtain a day-ahead set combination output curve. According to the invention, with utilization of the complementary characteristic and frequency modulation capability of the wind-solar energy, the frequency regulation pressures of the energy storage device and the conventional energy set are reduced; and with consideration of the frequency safety constraint, the optimal operation mode of the wind-solar storage hybrid energy system is determined, so that the economic and security of the operation of the hybrid energy system are improved.

The invention relates to a multi-resolution medical image registration method based on the quantum-behaved particle swarm optimization, which is characterized in that the method comprises the following step of: firstly removing the backgrounds of the two images to be registered to keep the images off the interference of noise; next obtaining the images with low resolution from the two background-removed images via the wavelet transform, using the low resolution to be the object, taking the normalized mutual information as the objective function, using the quantum-behaved particle swarm optimization, then the images with the high resolution to be the object, and obtaining the rotation amount and the translation amount between the two images to be registered to finish the image registration by using the Powell method. With solving a plurality of local extrema based on the objective function of the mutual information, the invention greatly improves the registration precision and speed, reaching up to the sub-pixel level; and is widely applicable to the fields of the image discrimination of the clinical diagnosis, the framing of the radiation treatment, the image guided surgical, etc.

The invention discloses a soft-sensing modeling method and a soft meter of a multi-model neural network in a biological fermentation process. The method comprises the following steps: a data preprocessing module preprocesses input variable data by a normalization and principle component analysis method; and then the data preprocessing module carries out cluster division on a preprocessed principle component variable set; through and then a BP neural network model module respectively establishes sub neural networks according to different clusters and finally establishes a soft-sensing model of the multi-model neural network. The soft-sensing model of the multi-model neural network is used for measuring biomass concentration in a fermentation process on line, and a measurement value is displayed through a biomass concentration soft-sensing value displayer. The invention introduces a core fuzzy C mean clustering algorithm based on a particle swarm algorithm and combines the mean clustering algorithm with the modeling method of the multi-model neural network, and the established model is simple, realizes the on-line measurement of the biomass concentration and has timely control, high measurement accuracy and strong capacity of resisting disturbance.

The invention provides a power distribution network fault positioning method based on improvement of a binary particle swarm algorithm, the conventional binary particle swarm algorithm is improved, and the method is applied to positioning of power distribution network faults. The method comprises following steps: firstly, determining parameters including the particle swarm scale and the maximum iteration frequency etc.; then forming an expectation function of a switch according to fault information of the switch, and constructing a fitness function of power distribution network fault positioning; initializing a particle swarm, setting particle positions, and setting the speed of the particles as 0; calculating the fitness values of the particles according to the fitness function, and setting an initial global extremum; updating an individual extremum and the initial global extremum; updating the speed and position of the particle swarm; and stopping calculation when reaching the maximum iteration frequency, and outputting the global optimal position of the particle swarm, namely the practical fault state of each feed line section of a target power distribution network. According to the method, the problem of premature convergence of the conventional method can be overcome, and the convergence and the stability of the algorithm can be further improved.

The invention discloses a charging and discharging scheduling method for electric vehicles connected to a micro-grid. The method comprises the steps of: (1) determining a system structure of the micro-grid and the characteristics of various units; (2) building a micro-grid optimal scheduling target function of considering the battery depreciation cost of the electric vehicles under time-sharing electrovalence; (3) determining constraints of various distributed power supplies, batteries of the electric vehicles and the like and forming a micro-grid optimal scheduling model by the constraints and the micro-grid optimal scheduling target function; (4) determining the number, the start-stop time, the start-stop charged states and other basic calculated data of the electric vehicles connected to the power grid under the time-sharing electrovalence; and (5) solving the micro-grid optimal scheduling model through a particle swarm algorithm and determining the charge and discharge power when the electric vehicles are connected to the power grid. The batteries of the electric vehicles are connected to the micro-grid as mobile distributed energy storage devices to achieve the peak load shifting effect; the operation safety and stability of the micro-grid in the time-sharing electrovalence environment are improved; and the energy utilization efficiency and the power grid operation efficiency are simultaneously improved.

A particle swarm algorithm is applied to an optimum coverage problem of wireless sensor network nodes, and the invention provides a coverage mechanism of the particle swarm optimization algorithm based on a discrete binary edition, and the mechanism is used for performing optimal solution on the wireless sensor network node coverage problem. The method comprises the following steps: defining the wireless sensor network node coverage problem as a 0/1 planning problem, encoding individuals of a binary particle swarm algorithm as a 0/1 binary string and then performing optimization by an evolutionism of the particle swarm algorithm. In the method, two important indexes are defined to evaluate the solution results, one index is 'coverage' of a region, and the other index is 'consumption rate' of the sensor. Validity and high efficiency of the algorithm are verified by a simulation experiment.

The present invention discloses a short-term load prediction method based on variable selection and Gaussian process regression. The method includes the following steps that: 1) bad data elimination, supplementation and normalization pre-processing are performed on sample data; 2) candidate input variables are selected from the perspectives of historical load, temperature and humidity, and the date type of a prediction date, and the scores of the importance of the variables are calculated through a random forest algorithm, and the scores of the importance of the variables are sequenced; 3) an optimal variable set is determined through adopting a sequence forward search strategy and based on a Gaussian process regression model; 4) the Gaussian process regression model is trained based on the determined optimal variable set, and the parameters of the model are optimized based on improved particle swarm optimization; and 5) the predictive performance of the model is verified in a test set. With the method provided by the invention adopted, prediction accuracy can be effectively improved, and the load prediction problem of a power system can be solved.

The invention provides a microgrid economic operation optimization method based on a fuzzy particle swarm algorithm and an energy saving system. The method comprises the following steps of: (1) determining an economic dispatch strategy of a micro source in the microgrid; (2) building a microgrid grid-connected economic operation mathematical model combined with economic benefit and environment benefit; (3) putting forward a fuzzy control strategy for charging/discharging control of the energy saving system; and (4) optimizing based on the particle swarm algorithm, and determining the output of all micro sources and the generation cost per day. Compared with a method in which an optimization dispatch scheme is not adopted by the energy saving system, the method has the advantages that the generation cost per day of microgrid running and pollution emission are reduced; and compared with a method in which the fuzzy control charging/discharging strategy is not adopted by the energy saving system, the method has the advantages that optimal solution quality is improved, operational convergence rate is increased, and the reliability of microgrid running is further enhanced.

The invention discloses a cascade hydropower station group optimized dispatching method based on an improved quantum-behaved particle swarm algorithm. The problems that local optimum happens to the quantum-behaved particle swarm algorithm at the later iteration period due to premature convergence for the reason that population diversity is decreased, and an obtained hydropower station group dispatching scheme is not the optimal scheme are mainly solved. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is characterized by comprising the steps that first, power stations participating in calculation are selected, and the corresponding constraint condition of each power station is set; then, a two-dimensional real number matrix is used for encoding individuals; afterwards, a chaotic initialization population is used for improving the quality of an initial population, the fitness of each particle is calculated through a penalty function method, the individual extreme value and the global extreme value are updated, an update strategy is weighed, the optimum center location of the population is calculated, neighborhood mutation search is conducted on the global optimum individual, the positions of all the individuals in the population are updated according to a formula, and whether a stopping criterion is met or not is judged. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is easy to operate, small in number of control parameters, high in convergence rate, high in computation speed, high in robustness, reasonable and effective in result, and applicable to optimized dispatching of cascade hydropower station groups and optimal allocation of water resources.

The invention provides a capacity configuration method of a hybrid energy storage system for stabilizing wind power fluctuation. The hybrid energy storage system comprises a power type energy storage system and an energy type energy storage system. The method includes the steps that (1) the grid connection active power of wind power and the active power P (t) of the hybrid energy storage system are acquired, and a wavelet packet decomposition method is utilized to acquire power type energy storage power and energy type energy storage power respectively; (2) the hybrid energy storage system at different rated power P and under different capacities is configured, and the stabilizing effect of the hybrid energy storage system is analyzed; (3) according to the stabilizing effect and an improved particle swarm algorithm, the capacity of the hybrid energy storage system is configured. Compared with the prior art, according to the capacity configuration method of the hybrid energy storage system for stabilizing wind power fluctuation, a control policy is built according to the current electric quantity conditions, power magnitude required to be exerted and the rated power of the power type energy storage system and the energy type energy storage system and the improved particle swarm algorithm, and wind power fluctuation is stabilized and effectively controlled.

The invention belongs to the computer field, and discloses a method for scheduling a flow shop based on a multi-swarm hybrid particle swarm algorithm, which solves the problems that the flow shop scheduling method based on the hybrid particle swarm algorithm is easy to result in premature convergence and local optimum. The method comprises the following steps of: setting parameters and generating S sub-swarms; judging whether the terminal condition is satisfied, if so, outputting a current optimum scheduling scheme, otherwise, updating positions of particles in each sub-swarm with the particle swarm algorithm, carrying out a local search on odd and even sub-swarms respectively by using searching operators 1 and 2 to obtain an optimum scheduling sequence of each sub-swarm; sharing information of the obtained optimum scheduling sequence by using a statistics-based probability model; and optimizing an optimum working sequence with a simulated annealing algorithm. In the invention, multiple swarms are added, the local search is carried out by using different searching operators, a good flow shop scheduling scheme is obtained, the production time is shortened, and the method can be used for the selection of the job shop scheduling scheme.

The invention discloses a solution method for independent and joint dispatching of a distribution network with micro-grids. The method comprises the following steps: establishing a model of the distribution network with the micro-grids; establishing an objective function for dispatching of the micro-grids and an objective function for dispatching of the distribution network; determining constraints for independent and joint dispatching of the micro-grids and the distribution network; and solving household microgrids and distribution network by a particle swarm optimization algorithm, and solving thermoelectric microgrids with a Benders decomposition method. In the household microgrids, the demand response is considered, and a load curve is optimized by a genetic algorithm. Aiming at the prediction error of wind power, a wind field model with three-parameter Weibull distribution is established. The method can be applied in the technical field of economic dispatching of a plurality of microgrids, and a plurality of stakeholders are satisfied on the premise of satisfying system constraints. The Benders decomposition method is used to solve a thermoelectric system, thereby effectivelyprotecting the privacy of the information of electric and thermal systems, and improving the accuracy of the calculation.

The invention discloses a thrust distribution method for a power positioning system of an offshore drilling platform, which solves the problem of thrust distribution optimization of the power positioning system by applying a particle swarm algorithm through taking minimized propulsion system power consumption and a thrust error as target functions of the thrust distribution optimization and considering the thrust, thrust change rate, azimuth change rate and constriction conditions of a thrust forbidden sector. The method comprises the following steps of setting input quantity of the thrust distribution, including vertical resultant force, transverse resultant force and yawing moment, as given parameters; setting the thrust amplitude and direction of each thruster as unknown variables, namely, solutions to be optimized; analyzing platform stress, wherein the resultant forces and resultant force moments generated by all the thruster are equal to the input quantity of the thrust distribution; and building a thrust distribution mathematical model according to the distribution of offshore drilling platform thrusters and applying the particle swarm algorithm to solve the problem of thrust distribution optimization. The thrust distribution method has no special requirements to the target functions of the thrust distribution and less parameter to be regulated, is simple in operation, easy for realizing and fast in calculation speed, so that the requirement of high real-time capability of the power positioning system is fulfilled.

The invention discloses a welding robot welding path planning method based on discrete particle swarm optimization. The method comprises the steps of: (1) building a mathematical model of a path of a welding robot; (2) building a model of particle swarm optimization; (3) analyzing behavior parameters solved by the particle swarm optimization; (4) planning the path of the welding robot by the particle swarm optimization; and (5) outputting optimal path. The method adopts the particle swarm optimization; and through setting of different inertia weight formulas, the self-adaption adjusting capacity of the particle swarm optimization is improved, so that the purpose of balancing local search capacity and global search capacity of the particle swarm optimization is achieved, and the optimal solution is searched quickly by the particle swarm optimization.

The invention provides a self-adaptive genetic particle swarm hybrid algorithm optimization method. The self-adaptive genetic particle swarm hybrid algorithm optimization method includes: calculatingthe density and the radius of a center region of a parent population in a genetic algorithm, and distinguishing whether the parent population is in the overall centralized distribution, the local centralized distribution or the uniform distribution; performing a selection operation of the genetic algorithm, and selecting a parent individual to be evolved; establishing computational formulas of thecrossover probability and the mutation probability according to the three distributions of the parent population; performing crossover and mutation operations according to the established crossover and mutation probability formulas so as to achieve chromosome recombination and gene mutation, and forming an offspring individual; selecting a part of individuals with high fitness from a part of offspring individuals to perform the particle swarm algorithm to form offspring particles, and combining the offspring individuals and the offspring particles into an offspring population and saving the optimal individual thereof. The invention adaptively adjusts crossover probability mutation probability parameter values in the genetic particle swarm hybrid algorithm, so that the convergence speed and the convergence precision are greatly improved.

The invention discloses a multi-UAV (unmanned aerial vehicle) helicopter and warship cooperating path planning method. The method comprises the steps of 1, calculating a route cost matrix according to the euclidean distance between a target node and a tactical node; 2, modeling the multi-UAV helicopter and warship cooperating path planning through the solved route cost matrix; 3, creating coding rules to enable corresponding between the solution of the built model and the position vector of particles in a particle group, and initializing the solution of the model; 4, iterating the initial solution by the improved particle group algorithm to obtain the optimal solution; 5, treating the scheme corresponding to the optimal solution as the optimal scheme for multi-UAV helicopter and warship cooperating path planning. With the adoption of the method, the multi-UAV helicopter and warship cooperating path planning structure can be determined, and the multi-UAV helicopter and warship cooperating path planning scheme can be quickly provided, so that the efficiency, reasonability and accuracy of preparing the cooperating route planning scheme can be improved, and as a result, the war power of a fleet can be increased.

The present invention provides a multi-population genetic particle swarm optimization method containing the micro-grid capacity configuration of electric automobiles. The method realizes the energy storage function of an electric automobile on the premise that the electricity demand of the electric automobile can be met. According to the technical scheme of the invention, a multi-target model, with the annual cost, the annual loss of load probability and the peak-valley difference of a load curve as targets, is proposed. Based on the multi-population genetic particle swarm algorithm, a target function is solved out. In this way, the optimal capacity of each unit in a micro-grid system can be obtained precisely. On the premise that the system reliability is ensured and the load fluctuation is stabilized and inhibited, a higher economic benefit is obtained. Through optimizing the micro-grid system containing the electric automobile, the mobile energy-storage device of the electric automobile is utilized to realize the peak-load shifting purpose on the basis that the reliability and the economy of the system are guaranteed. Meanwhile, the peak-valley difference of the system curve is reduced. Not only is the stability of the power system improved, but also the economic benefit is higher. Therefore, the popularization and the utilization of a cleaning device of the electric automobile are facilitated.

The invention relates to a wind electric power prediction method and a device thereof. The method comprises the following steps of: step one: extracting data from SCADA (Supervisory Control and Data Acquisition) relative to a numerical weather prediciton system or a power system, and carrying out smoothing processing on the extracted data; step two: determining input and output of training samples of a least squares support vector machine according to the processed data; step three: initializing relevant parameters of a smallest squares support vector machine and an improved self-adaptive particle swarm algorithm; step four: optimizing model parameters according to an optimization process; step five: acquiring a model of the smallest squares support vector machine according to the optimized parameters; and step six: carrying out prediction according to the model of the smallest squares support vector machine. According to the wind electric power prediction method disclosed by the invention, a modelling process is simple and practical, wind electric power can be rapidly and effectively predicted, and the wind electric power prediction method has an important significance on safety and stability, and scheduling and running of the electric power system, and therefore, the wind electric power prediction method has wide popularization and application values.

The invention discloses a combined heat and power dispatching method for improving wind power dissipation, which relates to the field of large power grid dispatching. The invention calculates the initial carbon quota of the thermoelectric unit and the actual carbon quota. By comparing the continuous output of the thermoelectric unit due to the increase of the heat load, the excess heat load is supplied by the regenerative electric boiler. And then charging the electric energy storage through the abandoned wind. Then, in order to reduce the control difficulty of heat storage electric boiler andelectric energy storage system, the combined heat and power multi-agent model for load distribution and information transmission is established. Then an optimization model considering only the cost of coal consumption is established. Finally, the dynamic inertia weight and compression factor are introduced to improve the particle swarm optimization algorithm to solve the model. The method of theinvention enables the system to reduce the system cost and improve the utilization rate of wind energy under the condition of guaranteeing the operation reliability. The simulation with MATLAB 7.10 verifies the rationality and effectiveness of the method, and proves that the utilization ratio of wind power can be improved by using regenerative electric boiler to supply heat load and using abandoned air to charge electric energy storage.

The invention proposes a charging pile setting method for dividing regions based on driving data and a Voronoi diagram, and belongs to the field of electric car. The method comprises the steps: firstly carrying out the dividing of a region where charging piles need to be set into subregions through a Voronoi diagram method; calculating the maximum charging load of each subregion through the driving data, and selecting the maximum value and the subregion corresponding to the maximum value; building a value model for the selected subregion, solving the model through a particle swarm algorithm, obtaining the positions of added charging piles in the subregion and an optimization result of the number of the charging piles; adding the added charging piles to a map of the region, carrying out thenew subregion dividing of all charging piles and the optimization calculation of a charging station till the constraint condition exceeds a preset upper limit, and ending the setting of the chargingpiles in the region. According to the invention, the method takes the obtaining of the high-efficiency charging of an electric car and the reduction of the construction cost of the charging facility as the objectives, achieves the reasonable site selection and capacity fixing for the setting of the charging piles through the driving data of the car, and is very high in globality and accuracy.