ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified

A Gas Scheduling Optimization Model for Steel Enterprises

Regarding the scheduling problems of steel enterprises, this research designs the gas scheduling optimization model according to the rules and priorities. Considering different features and the process changes of the gas unit in the process of actual production, the calculation model of process state and gas consumption soft measurement together with the rules of scheduling optimization is proposed to provide the dispatchers with real-time gas using status of each process, then help them to timely schedule and reduce the gas volume fluctuations. In the meantime, operation forewarning and alarm functions are provided to avoid the abnormal situation in the scheduling, which has brought about very good application effect in the actual scheduling and ensures the safety of the gas pipe network system and the production stability

A Gas Scheduling Optimization Model for Steel Enterprises

Regarding the scheduling problems of steel enterprises, this research designs the gas scheduling optimization model according to the rules and priorities. Considering different features and the process changes of the gas unit in the process of actual production, the calculation model of process state and gas consumption soft measurement together with the rules of scheduling optimization is proposed to provide the dispatchers with real-time gas using status of each process, then help them to timely schedule and reduce the gas volume fluctuations. In the meantime, operation forewarning and alarm functions are provided to avoid the abnormal situation in the scheduling, which has brought about very good application effect in the actual scheduling and ensures the safety of the gas pipe network system and the production stability

Research of self-power Generation Scheduling Model Base on Multi-objective in Iron and Steel Enterprises

With the targets of the minimum cost of power generation and the lowest rate of gas emission in iron and steel enterprises, a multi-objective self-power generation optimal scheduling model was built based on multi-objective particle swarm optimization the research of coupling relationship of gas and power. And by using the hierarchical decomposition method, the model was broken down into two parts: optimization of gas system and optimization of thermal and power system. The case analysis indicated that: the model could distribute the energy of gas and power reasonably, safely and efficiently when the production condition was changed, and improve the energy utilization efficiency

Study on Dynamic Reconfiguration of Distribution Network Considering Distribution Generation

A new dynamic reconfiguration method considering distribution generation (DG) was presented. The dynamic reconfiguration problem was solved from three aspects: firstly, the time interval partition of the entire scheduling period was optimized by load distribution variation index; secondly, a plurality of time intervals were optimized and reconfigured by using the multi-objective particle swarm optimization model of multi-period encoding; finally, the optimal time interval numbers was determined by gradually approaching the falling threshold of net loss. If the system contained DG, DG’s output curve was determined by the analysis of its dynamic characteristics. The results of the test showed that the proposed method was effective to solve the dynamic reconfiguration with DG

Study on Dynamic Reconfiguration of Distribution Network Considering Distribution Generation

A new dynamic reconfiguration method considering distribution generation (DG) was presented. The dynamic reconfiguration problem was solved from three aspects: firstly, the time interval partition of the entire scheduling period was optimized by load distribution variation index; secondly, a plurality of time intervals were optimized and reconfigured by using the multi-objective particle swarm optimization model of multi-period encoding; finally, the optimal time interval numbers was determined by gradually approaching the falling threshold of net loss. If the system contained DG, DG’s output curve was determined by the analysis of its dynamic characteristics. The results of the test showed that the proposed method was effective to solve the dynamic reconfiguration with DG

Research of self-power Generation Scheduling Model Base on Multi-objective in Iron and Steel Enterprises

With the targets of the minimum cost of power generation and the lowest rate of gas emission in iron and steel enterprises, a multi-objective self-power generation optimal scheduling model was built based on multi-objective particle swarm optimization the research of coupling relationship of gas and power. And by using the hierarchical decomposition method, the model was broken down into two parts: optimization of gas system and optimization of thermal and power system. The case analysis indicated that: the model could distribute the energy of gas and power reasonably, safely and efficiently when the production condition was changed, and improve the energy utilization efficiency

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified

A Method of UAVs Route Optimization Based on the Structure of the Highway Network

It is essential for transportation management centers to establish a network of fixed and mobile sensors to collect traffic information of highway network, especially for very important links with frequent traffic events. Emerging Unmanned Aerial Vehicles (UAVs), it is introduced as a mobile sensor to collect road traffic information and its cruise route planning problem is researched based on the highway network physical structure. First, according to existing traffic data, a method used to calculate the link importance degree index is proposed, and the index is used to evaluate the link's information. Second, a multiobjective optimization model is proposed, its aim is to minimize the total cruise time under detecting as many important links as possible and minimize the information value undetected by UAVs, and the fuzzy operator is introduced to the constraint conditions. Finally, a case study is used to demonstrate the feasibility and effectiveness of proposed model about UAVs’ route planning