Faisal A. Mohamed

Online Management of MicroGrid with Battery Storage Using Multiobjective Optimization

This paper presents a generalized formulation for determining the optimal operating strategy and cost optimization scheme as well as reducing the emissions of a MicroGrid (MG). Multiobjective (MO) optimization is applied to the environmental economic problem of the MG. The proposed problem is formulated as a nonlinear constrained MO optimization problem Prior to the optimization, system model components taken from real manufactural data are constructed. The model takes into consideration the operation and maintenance costs as well as the reduction in emissions of NOx, SO2, and CO2. The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the costumer demand and safety of the system. The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions in one single run.

System Modelling and Online Optimal Management of MicroGrid Using Multiobjective Optimization

This paper presents a generalized formulation to determine the optimal operating strategy and cost optimization scheme as well as the reduction of the emissions for a MicroGrid (MG). Multiobjective (MO) optimization is applied to the environmental/economic problem of the MG. The proposed problem is formulated as a nonlinear constrainted MO optimization problem. Prior to the optimization, a system model components from some real manufactural data are constructed. The model takes into consideration the operation and maintenance costs as well as the emissions NOx, SO2, and CO2 reduction. The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array , and fuel cell. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the costumer demand and safety of the system.The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions in one single run.

MicroGrid Online Management and Balancing Using Multiobjective Optimization

This paper presents a generalized formulation to determine the optimal operating strategy and cost optimization scheme as well as the reduction of the emissions for a MicroGrid (MG). Multiobjective (MO) optimization is applied to the environmental/economic problem of the MG. The proposed problem is formulated as a nonlinear constrained MO optimization problem. The model takes into consideration the operation and maintenance costs as well as the emissions NOx, SO2, and CO2 reduction. The MG considered in this paper consists of a wind turbine, a micro turbine , a diesel generator , a photovoltaic array , a fuel cell, and a battery storage. The optimization is aimed at minimizing the operating cost of the system while constraining it to meet the costumer demand and safety of the system. We also added a daily income and outgo from sold and purchased power. The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions in one single run.

Online self-tuning controller for induction motor based on generalized minimum variance method

The implicit or direct GMV regulator combined with the recursive least squares parameter estimation method was applied to regulate the speed of the induction motor under the influence of load variations. To obtain good tracking and control characteristics, a GMV self-tuning regulator is adopted and a design procedure is developed. The performance of the self-tuning GMV regulator depends a great deal on the specification of weighting polynomials for output, input and the reference point.

Modelling and Environmental/Economic Power Dispatch of MicroGrid Using MultiObjective Genetic Algorithm Optimization

MultiObjective (MO) optimization has a very wide range of successful applications in engineering and economics. Such applications can be found in optimal control systems (Liu et al., 2007), and communication (Elmusrati et al., 2007). The MO optimization can be applied to find the optimal solution which is a compromise between multiple and contradicting objectives. In MO optimization we are more interested in the Pareto optimal set which contains all noninferior solutions. The decision maker can then select the most preferred solution out of the Pareto optimal set. The weighted sum method to handle MO optimization applied in this paper. Furthermore, the weighted sum is simple and straightforward method to handle MO optimization problems. The need for more flexible electric systems to cope with changing regulatory and economic scenarios, energy savings and environmental impact is providing impetus to the development of MicroGrids (MG), which are predicted to play an increasing role of the future power systems (Hernandez-Aramburo et al., 2005). One of the important applications of the MG units is the utilization of small-modular residential or commercial units for onsite service. The MG units can be chosen so that they satisfy the customer load demand at compromise cost and emissions all the time. Solving the environmental economic problem in the power generation has received considerable attention. An excellent overview on commonly used environmental economic algorithms can be found in (Talaq et al., 1994). The environmental economic problems have been effectively solved by multiobjective evolutionary in (Abido, 2003) and fuzzy satisfaction-maximizing approach (Huang et al., 1997). Several strategies have been reported in the literature related to the operation costs as well as minimizing emissions of MG. In (Hernandez-Aramburo et al., 2005) the optimization is aimed at reducing the fuel consumption rate of the system while constraining it to fulfil the local energy demand (both electrical and thermal) and provide a certain minimum reserve