M. Etezadi-Amoli

Bio: M. Etezadi-Amoli is an academic researcher from University of Nevada, Reno. The author has contributed to research in topics: Electric power system & Fault (power engineering). The author has an hindex of 18, co-authored 47 publications receiving 1879 citations. Previous affiliations of M. Etezadi-Amoli include Sharif University of Technology.

Genetic-Algorithm-Based Optimization Approach for Energy Management

Abstract: This paper proposes a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system. Historical hourly wind speed, solar irradiance, and load data are used to stochastically model the wind generation, photovoltaic generation, and load. Using fuzzy C-Means (FCM) clustering, these data are grouped into 10 clusters of days with similar data points to account for seasonal variations. In order to minimize cost and increase efficiency, we use a GA-based optimization approach together with a two-point estimate method. Minimizing the cost function guarantees minimum PV and wind generation installation as well as storage capacity selection to supply the HVAC load. Different scenarios are examined to evaluate the efficiency of the system with different percentages of load shifting. The maximum capacity of the storage system and excess energy are calculated as the most important indices for energy efficiency assessment. The cumulative distribution functions of these indices are plotted and compared. A smart-grid strategy is developed for matching renewable energy generation (solar and wind) with the HVAC load.

Rapid-Charge Electric-Vehicle Stations

Abstract: Mass production of total electric vehicles capable of traveling longer distances results in a need for electric service stations that can satisfy the requirements for a significant amount of power provided in a time duration similar to that of filling a car with oil-based fuel. These vehicles would pull into the station, and need a large amount of power delivered over a short period of time for the rapid recharging of batteries which serve as their “fuel tank.” This paper investigates the effect of fast-charging electric vehicles on an existing utility company distribution system at several specified sites. This paper will cover power-flow, short-circuit, and protection studies at these sites using utility-grade software packages. In addition, the analysis of simulations of a recharging station where up to eight rapidly rechargeable vehicles come online at once will be presented.

A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration

Abstract: This paper deals with optimal placement of the energy storage units within a deregulated power system to minimize its hourly social cost. Wind generation and load are modeled probabilistically using actual data and a curve fitting approach. Based on a model of the electricity market, we minimize the hourly social cost using probabilistic optimal power flow (POPF) then use a genetic algorithm to maximize wind power utilization over a scheduling period. A business model is developed to evaluate the economics of the storage system based on the energy time-shift opportunity from wind generation. The proposed method is used to carry out simulation studies for the IEEE 24-bus system. Transmission line constraints are addressed as a bottleneck for efficient wind power integration with higher penetration levels. Distributed storage is then proposed as a solution to effectively utilize the transmission capacity and integrate the wind power more efficiently. The potential impact of distributed storage on wind utilization is also evaluated through several case studies.

Stochastic Performance Assessment and Sizing for a Hybrid Power System of Solar/Wind/Energy Storage

Abstract: This paper proposes a stochastic framework for optimal sizing and reliability analysis of a hybrid power system including the renewable resources and energy storage system. Uncertainties of wind power, photovoltaic (PV) power, and load are stochastically modeled using autoregressive moving average (ARMA). A pattern search-based optimization method is used in conjunction with a sequential Monte Carlo simulation (SMCS) to minimize the system cost and satisfy the reliability requirements. The SMCS simulates the chronological behavior of the system and calculates the reliability indices from a series of simulated experiments. Load shifting strategies are proposed to provide some flexibility and reduce the mismatch between the renewable generation and heating ventilation and air conditioning loads in a hybrid power system. Different percentages of load shifting and their potential impacts on the hybrid power system reliability/cost analysis are evaluated. Using a compromise-solution method, the best compromise between the reliability and cost is realized for the hybrid power system.

Smart meter based short-term load forecasting for residential customers

Abstract: This paper examines the potential impact of automatic meter reading (AMR) on short-term load forecasting for a residential customer. Real-time measurement data from customers' smart meters provided by a utility company is modeled as the sum of a deterministic component and a Gaussian noise signal. The shaping filter for the Gaussian noise is calculated using spectral analysis. Kalman filtering is then used for load prediction. The accuracy of the proposed method is evaluated for different sampling periods and planning horizons. The results show that the availability of more real-time measurement data improves the accuracy of the load forecast significantly. However, the improved prediction accuracy can come at a high computational cost. Our results qualitatively demonstrate that achieving the desired prediction accuracy while avoiding a high computational load requires limiting the volume of data used for prediction. Consequently, the measurement sampling rate must be carefully selected as a compromise between these two conflicting requirements.

Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles

Abstract: This paper reviews the current status and implementation of battery chargers, charging power levels, and infrastructure for plug-in electric vehicles and hybrids. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical on-board chargers restrict power because of weight, space, and cost constraints. They can be integrated with the electric drive to avoid these problems. The availability of charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. Future aspects such as roadbed charging are presented. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, and other factors.

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Abstract: As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

Electrical energy storage systems: A comparative life cycle cost analysis

Abstract: Large-scale deployment of intermittent renewable energy (namely wind energy and solar PV) may entail new challenges in power systems and more volatility in power prices in liberalized electricity markets. Energy storage can diminish this imbalance, relieving the grid congestion, and promoting distributed generation. The economic implications of grid-scale electrical energy storage technologies are however obscure for the experts, power grid operators, regulators, and power producers. A meticulous techno-economic or cost-benefit analysis of electricity storage systems requires consistent, updated cost data and a holistic cost analysis framework. To this end, this study critically examines the existing literature in the analysis of life cycle costs of utility-scale electricity storage systems, providing an updated database for the cost elements (capital costs, operational and maintenance costs, and replacement costs). Moreover, life cycle costs and levelized cost of electricity delivered by electrical energy storage is analyzed, employing Monte Carlo method to consider uncertainties. The examined energy storage technologies include pumped hydropower storage, compressed air energy storage (CAES), flywheel, electrochemical batteries (e.g. lead–acid, NaS, Li-ion, and Ni–Cd), flow batteries (e.g. vanadium-redox), superconducting magnetic energy storage, supercapacitors, and hydrogen energy storage (power to gas technologies). The results illustrate the economy of different storage systems for three main applications: bulk energy storage, T&D support services, and frequency regulation.

Electricity price forecasting: A review of the state-of-the-art with a look into the future

Abstract: A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.