Ahmed Yousuf Saber

Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions

The electricity and transportation industries are the main sources of greenhouse gas emissions on Earth. Renewable energy, mainly wind and solar, can reduce emission from the electricity industry (mainly from power plants). Likewise, next-generation plug-in vehicles, which include plug-in hybrid electric vehicles (EVs) and EVs with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs) by the authors, can reduce emission from the transportation industry. GVs can be used as loads, energy sources (small portable power plants), and energy storages in a smart grid integrated with renewable energy sources (RESs). Smart grid operation to reduce both cost and emission simultaneously is a very complex task considering smart charging and discharging of GVs in a distributed energy source and load environment. If a large number of GVs is connected to the electric grid randomly, peak load will be very high. The use of traditional thermal power plants will be economically and environmentally expensive to support the electrified transportation. The intelligent scheduling and control of GVs as loads and/or sources have great potential for evolving a sustainable integrated electricity and transportation infrastructure. Cost and emission reductions in a smart grid by maximum utilization of GVs and RESs are presented in this paper. Possible models for GV applications, including the smart grid model, are given, and results are presented. The smart grid model offers the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry.

Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles

The power system and transportation sector are our planets main sources of greenhouse gas emissions. Renewable energy sources (RESs), mainly wind and solar, can reduce emissions from the electric energy sector; however, they are very intermittent. Likewise, next generation plug-in vehicles, which include plug-in hybrid electric vehicles and electric vehicles with vehicle-to-grid capability, referred to as gridable vehicles (GVs) by the authors, can reduce emissions from the transportation sector. GVs can be used as loads, energy sources (small portable power plants) and energy storage units in a smart grid integrated with renewable energy sources. However, uncertainty surrounds the controllability of GVs. Forecasted load is used in unit commitment (UC); however, the actual load usually differs from the forecasted one. Thus, UC with plug-in vehicles under uncertainty in a smart grid is very complex considering smart charging and discharging to and from various energy sources and loads to reduce both cost and emissions. A set of valid scenarios is considered for the uncertainties of wind and solar energy sources, load and GVs. In this paper, an optimization algorithm is used to minimize the expected cost and emissions of the UC schedule for the set of scenarios. Results are presented indicating that the smart grid has the potential to maximally utilize RESs and GVs to reduce cost and emissions from the power system and transportation sector.

Efficient Utilization of Renewable Energy Sources by Gridable Vehicles in Cyber-Physical Energy Systems

The main sources of emission today are from the electric power and transportation sectors. One of the main goals of a cyber-physical energy system (CPES) is the integration of renewable energy sources and gridable vehicles (GVs) to maximize emission reduction. GVs can be used as loads, sources and energy storages in CPES. A large CPES is very complex considering all conventional and green distributed energy resources, dynamic data from sensors, and smart operations (e.g., charging/discharging, control, etc.) from/to the grid to reduce both cost and emission. If large number of GVs are connected to the electric grid randomly, peak load will be very high. The use of conventional thermal power plants will be economically expensive and environmentally unfriendly to sustain the electrified transportation. Intelligent scheduling and control of elements of energy systems have great potential for evolving a sustainable integrated electricity and transportation infrastructure. The maximum utilization of renewable energy sources using GVs for sustainable CPES (minimum cost and emission) is presented in this paper. Three models are described and results of the smart grid model show the highest potential for sustainability.

Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing

This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms.

Optimization of vehicle-to-grid scheduling in constrained parking lots

An automatic Vehicle-to-Grid (V2G) technology can contribute to the utility grid. V2G technology has drawn great interest in the recent years. Success of the sophisticated automatic V2G research depends on efficient scheduling of gridable vehicles in constrained parking lots. Parking lots have constraints of space and current limits for V2G. However, V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for only thermal units. Particle swarm optimization (PSO) is proposed to solve the V2G, as PSO has been demonstrated to reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO optimizes the on/off states of power generating units easily. Vehicles are presented by signed integer number instead of 0/1 to reduce the dimension of the problem. Typical discrete version of PSO has less balance between local and global searching abilities to optimize the number of charging/discharging gridable vehicles in the constrained system. In the same model, balanced PSO is proposed to optimize the V2G part in the constrained parking lots. Finally, results show a considerable amount of profit for using proper scheduling of gridable vehicles in constrained parking lots.

Unit commitment with vehicle-to-Grid using particle swarm optimization

Vehicle-to-Grid (V2G) technology has drawn great interest in the recent years. Success of the V2G research depends on efficient scheduling of gridable vehicles in limited parking lots. V2G can reduce dependencies on small expensive units in the existing power systems as energy storage that can decrease running costs. It can efficiently manage load fluctuation, peak load; however, it increases spinning reserves and reliability. As number of gridable vehicles in V2G is much higher than small units of existing systems, unit commitment (UC) with V2G is more complex than basic UC for thermal units. Particle swarm optimization (PSO) is used to solve the UC with V2G, as PSO can reliably and accurately solve complex constrained optimization problems easily and quickly without any dimension limitation and physical computer memory limit. In the proposed model, binary PSO is used to optimize the on/off states of power generating units and in the same model, discrete version of PSO is used to optimize the scheduling of the gridable vehicles in the parking lots to reduce the dimension of the problem. Finally, simulation results show a considerable amount of profit for using V2G after proper UC with V2G scheduling of gridable vehicles in constrained parking lots.

One million plug-in electric vehicles on the road by 2015

It is mentioned that one million plug-in hybrid and electric vehicles will be on the road by 2015 in United States to reduce emission. If one million electric vehicles (EVs) are connected to the existing electric grid randomly, peak load will be very high. Electrified transportation based on a traditional thermal power system will be costly economically and environmentally though it has a great value for electric power and transportation sectors. EVs cannot alone solve the emission problem completely since they need electric power, which is one of the main sources of emission. Therefore, significant emission reduction greatly depends on the maximum utilization of renewable energy. Two models are investigated to show the effect of one million EVs on electric power and transportation sectors. Linear and non-linear systems are used in modeling the emissions generated from the transportation and electric energy sectors respectively.

Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System

In recent years, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict one-day-ahead 24 hours insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only meteorological data.

Application of neural network to 24-hour-ahead generating power forecasting for PV system

In recent years, focus has been on environmental pollution issue resulting from consumption of fossil fuels, e.g., coal and oil. Thus, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by meteorological conditions. In order to predict the power output for a PV system as accurately as possible, a method for insolation estimation is required. In this paper, the authors take the insolation of each month into consideration, and confirm the validity of using neural network to predict insolation by computer simulations. The proposed method utilizes any meteorological data and does not require complicated calculation and mathematical model.

Unit Commitment Computation - A Novel Fuzzy Adaptive Particle Swarm Optimization Approach

This paper presents a fuzzy adaptive particle swarm optimization (FAPSO) for unit commitment (UC) problem. FAPSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable resource size and changing load demand in deregulated market, the fuzzy adaptive criterion is applied for the PSO inertia weight based on the diversity of fitness. In this method, the inertia weight is dynamically adjusted using the fuzzy IF/THEN rules. To increase the knowledge, the global best location is moved instead of a fixed one in each generation. To avoid the method to be frozen, stagnated/idle particles are reset from time to time. Velocity is digitized (0/1) by a logistic function for the binary UC schedule. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method