Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 7 of 7
Full-Text Articles in Entire DC Network
Multi-Objective Dynamic Economic Dispatch With Demand Side Management Of Residential Loads And Electric Vehicles, Mohammad Rasoul Narimani, Fnu Maigha, Jhi-Young Joo, Mariesa Crow
Multi-Objective Dynamic Economic Dispatch With Demand Side Management Of Residential Loads And Electric Vehicles, Mohammad Rasoul Narimani, Fnu Maigha, Jhi-Young Joo, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated.
Economic Modeling Of Compressed Air Energy Storage, Yang Gu, James D. Mccalley, Ming Ni, Rui Bo
Economic Modeling Of Compressed Air Energy Storage, Yang Gu, James D. Mccalley, Ming Ni, Rui Bo
Electrical and Computer Engineering Faculty Research & Creative Works
Due to the variable nature of wind resources, the increasing penetration level of wind power will have a significant impact on the operation and planning of the electric power system. Energy storage systems are considered an effective way to compensate for the variability of wind generation. This paper presents a detailed production cost simulation model to evaluate the economic value of compressed air energy storage (CAES) in systems with large-scale wind power generation. The co-optimization of energy and ancillary services markets is implemented in order to analyze the impacts of CAES, not only on energy supply, but also on system …
Comparison Of De And Pso For Generator Maintenance Scheduling, Yusuf Yare, Ganesh K. Venayagamoorthy
Comparison Of De And Pso For Generator Maintenance Scheduling, Yusuf Yare, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a comparison of a differential evolution (DE) algorithm and a modified discrete particle swarm optimization (MDPSO) algorithm for generating optimal preventive maintenance schedules for economical and reliable operation of a power system, while satisfying system load demand and crew constraints. The DE, an evolutionary technique and an optimization algorithm utilizes the differential information to guide its further search, and can handle mixed integer discrete continuous optimization problems. Discrete particle swarm optimization (DPSO) is known to effectively solve large scale multi-objective optimization problems and has been widely applied in power systems. Both the DE and MDPSO are applied …
Optimal Scheduling Of Generator Maintenance Using Modified Discrete Particle Swarm Optimization, Yusuf Yare, Ganesh K. Venayagamoorthy
Optimal Scheduling Of Generator Maintenance Using Modified Discrete Particle Swarm Optimization, Yusuf Yare, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a modified discrete particle swarm optimization (PSO) based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. While GA and other analytical methods might suffer from premature convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. PSO is known to effectively solve large scale multi-objective optimization problems. Here, a modified discrete PSO approach is proposed for the GMS optimization problem in order to overcome the limitations of the conventional methods and come up …
Evaluating The Repair Of System-On-Chip (Soc) Using Connectivity, Minsu Choi, Nohpill Park, Vincenzo Piuri, Fabrizio Lombardi
Evaluating The Repair Of System-On-Chip (Soc) Using Connectivity, Minsu Choi, Nohpill Park, Vincenzo Piuri, Fabrizio Lombardi
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a new model for analyzing the repairability of reconfigurable system-on-chip (RSoC) instrumentation with the repair process. It exploits the connectivity of the interconnected cores in which unreliability factors due to both neighboring cores and the interconnect structure are taken into account. Based on the connectivity, two RSoC repair scheduling strategies, Minimum Number of Interconnections First (I-MIN) and Minimum Number of Neighboring Cores First (C-MIN), are proposed. Two other scheduling strategies, Maximum Number of Interconnections First (I-MAX) and Maximum Number of Neighboring cores First (C-MAX), are also introduced and analyzed to further explore the impact of connectivity-based repair …
Robust Resource Allocation For Sensor-Actuator Distributed Computing Systems, Shoukat Ali, Jong-Kook Kim, A. A. Maciejewski, Howard Jay Siegel
Robust Resource Allocation For Sensor-Actuator Distributed Computing Systems, Shoukat Ali, Jong-Kook Kim, A. A. Maciejewski, Howard Jay Siegel
Electrical and Computer Engineering Faculty Research & Creative Works
This research investigates two distinct issues related to a resource allocation: its robustness and the failure rate of the heuristic used to determine the allocation. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are number of quality of service (QoS) constraints that must be satisfied. A heuristic failure occurs if the heuristic cannot find an allocation that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, …
Security-Constrained Optimal Rescheduling Of Real Power Using Hopfield Neural Network, S. Ghosh, Badrul H. Chowdhury
Security-Constrained Optimal Rescheduling Of Real Power Using Hopfield Neural Network, S. Ghosh, Badrul H. Chowdhury
Electrical and Computer Engineering Faculty Research & Creative Works
A new method for security-constrained corrective rescheduling of real power using the Hopfield neural network is presented. The proposed method is based on solution of a set of differential equations obtained from transformation of an energy function. Results from this work are compared with the results from a method based on dual linear programming formulation of the optimal corrective rescheduling. The minimum deviations in real power generations and loads at buses are combined to form the objective function for optimization. Inclusion of inequality constraints on active line flow limits and equality constraint on real power generation load balance assures a …