Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

Integrated Model for Resilience Evaluation of Power-Gas Systems Under Windstorms

Integrated power-gas systems(IPGS)have developed critical infrastructure in integrated energy systems.Moreover,various extreme weather events with low probability and high risk have seriously affected the stable operation of IPGSs.Due to close interconnectedness through coupling elements between the power system(PS)and natural gas system(NGS)when a disturbance happens in one system,a series of complicated sequences of dependent events may follow in another system.Especially under extreme conditions,this coupling can lead to a dramatic degradation of system performance,resulting in catastrophic failures.Therefore,there is an urgent need to model and evaluate resilience of IPGSs under extreme weather.Following this development trend,an integrated model for resilience evaluation of IPGS is proposed under extreme weather events focusing on windstorms.First,a framework of IPGS is proposed to describe states of the system at different stages under disaster conditions.Furthermore,an evaluation model considering cascading effects is used to quantify the impact of windstorms on NGS and PS.Meanwhile,a Monte Carlo simulation(MCS)technique is utilized to characterize chaotic fault of components.Moreover,time-dependent nodal and system resilience indices for IPGS are proposed to display impacts of windstorms.Numerical results on the IPGS test system demonstrate the proposed methods.

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.