Salman Kahrobaee

Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households

In the near future, a smart grid will accommodate customers who are prepared to invest in generation-battery systems and employ energy management systems in order to cut down on their electricity bills. The main objective of this paper is to determine the optimum capacity of a customers distributed-generation system (such as a wind turbine) and battery within the framework of a smart grid. The proposed approach involves developing an electricity management system based on stochastic variables, such as wind speed, electricity rates, and load. Then, a hybrid stochastic method based on Monte Carlo simulation and particle swarm optimization is proposed to determine the optimum size of the wind generation-battery system. Several sensitivity analyses demonstrate the proper performance of the proposed method in different conditions.

Risk-based Failure Mode and Effect Analysis for wind turbines (RB-FMEA)

Failure Mode and Effect Analysis (FMEA) has already been used as a qualitative measure for identifying failure modes and causes, in order to mitigate the effects of failure in different sectors of power systems. This paper presents a quantitative approach called Risk-Based-FMEA, based on the failure probabilities and incurred failure costs instead of rating scales. As a case study, this approach has been applied to a direct drive wind turbine. The results show that the definition of failure modes priorities based on their contribution to the total failure cost of the wind turbine is more realistic and practical than the common FMEA approach. Using MS Excel spreadsheet platform, the proposed method can be generalized for different types of wind turbines. In addition, the effective failure cost factors are investigated through sensitivity analysis, by which the wind turbine owner can determine the suitable approach to reduce the total failure cost.

Vibration energy harvesting for wireless underground sensor networks

Recent developments in wireless underground communication have enabled the realization of underground sensor network applications. To this end, it is desirable to provide a sustainable operation for wireless underground sensor networks (WUSNs) with extended lifetimes as maintenance is significantly costly. One promising method towards sustainable operation is to harvest energy underground based on the vibration sources in the environment. However, to the best of our knowledge, underground vibration energy harvesting has not been investigated before. In this paper, the feasibility of vibration energy harvesting for WUSNs is investigated. First, an analytical framework is developed to model the maximum harvestable power by a piezoelectric energy harvester at a certain depth underground, due to an above-ground vibration source. Then, field experiments are conducted to measure the vibration in an agricultural testbed and evaluated the harvestable output power. The results from this study illustrate the feasibility of vibration energy harvesting as a promising approach to be considered for the future underground sensor networks.

Optimum planning and operation of compressed air energy storage with wind energy integration

The integration of increasingly available renewable energy sources, such as wind energy, into the power grid will have the potential to reduce dependence on fossil fuels and minimize greenhouse gas emission. However, due to the stochastic nature of renewable generation, balancing of generation and load becomes difficult. Energy storage is expected to play a major role in promoting the development of renewable energy by intermittent power source balancing, storing surplus generation, and providing electricity during high demands. One of the various emerging energy storage technologies is Compressed Air Energy Storage (CAES). In this paper, we model a wind generation-CAES system which can generate, store, and sell electricity to the grid. In addition, two optimization methodologies based on particle swarm optimization (PSO) are used to optimize the short-term operation and long-term planning of the wind generation-CAES system. The goal is to determine the optimum capacities of these resources as well as the optimum day-to-day operation strategy in order to maximize profit. The variables considered in this study include electricity market price, wind speed, gas price, etc., from a local electric utility. A number of sensitivity analyses are performed to evaluate the profitability of the wind generation-CAES system and the impact of different factors on the results.

Short and long-term reliability assessment of wind farms

Wind energy has been considered to be one of the main participants in supplying global energy needs. Meanwhile reliability and availability are critical issues to be studied with high wind penetration. While previous studies have focused on steady state behavior of wind turbines, this paper presents a Markovian method to study time-based reliability of wind farms. This method considers wind speed, failure and repair rates of wind turbines as well as load demands for short-term and long-term reliability calculation and comparison. Effect of change in initial number of working wind turbines and repair crew will also be investigated.

Optimum renewable generation capacities in a microgrid using generation adequacy study

Microgrids, as small power systems, may be comprised of different types of loads and distributed generation. As the integration of renewable power generation increases, the total available generation capacity of the system will be more derated due to the effect of equipment failures and the intermittent nature of these resources. Therefore, it is critical to determine optimum renewable generation capacities and provide enough reserve margin to meet the target reliability of the microgrid. In this paper, we first model a microgrid, including conventional and renewable distributed generation and the loads. Second, we determine the renewable generation capacity required to meet growth in demand at a certain level of grid reliability through a generation adequacy study. Adequacy of the microgrid is evaluated using parameters such as loss of load probability (LOLP) and expected energy not served (EENS). Third, the impact of different conditions, such as wind speed diversity (captured by correlating the wind power output), a combination of wind and solar power, and load diversity, on generation adequacy is studied through sensitivity analyses. Finally, the optimum renewable generation capacities are determined such that the total cost of generation and unserved power is minimized. The optimization process is based on the particle swarm optimization (PSO) method which uses Monte Carlo (MC) simulation for generation adequacy studies in each iteration.

Optimum sizing of distributed generation and storage capacity in smart households

Summary form only given. In the near future, a smart grid will accommodate customers who are prepared to invest in generation-battery systems and employ energy management systems in order to cut down on their electricity bills. The main objective of this paper is to determine the optimum capacity of a customers distributed-generation system (such as a wind turbine) and battery within the framework of a smart grid. The proposed approach involves developing an electricity management system based on stochastic variables, such as wind speed, electricity rates, and load. Then, a hybrid stochastic method based on Monte Carlo simulation and particle swarm optimization is proposed to determine the optimum size of the wind generation-battery system. Several sensitivity analyses demonstrate the proper performance of the proposed method in different conditions.