Influences of wind energy integration into the distribution network

Wind energy is one of the most promising renewable energy sources due to its availability and climate-friendly attributes. Large-scale integration of wind energy sources creates potential technical challenges due to the intermittent nature that needs to be investigated and mitigated as part of developing a sustainable power system for the future. Therefore, this study developed simulation models to investigate the potential challenges, in particular voltage fluctuations, zone substation, and distribution transformer loading, power flow characteristics, and harmonic emissions with the integration of wind energy into both the high voltage (HV) and low voltage (LV) distribution network (DN). From model analysis, it has been clearly indicated that influences of these problems increase with the increased integration of wind energy into both the high voltage and low voltage distribution network, however, the level of adverse impacts is higher in the LV DN compared to the HV DN.

Impact of dynamic PHEVs load on renewable sources based distribution system

In this paper, charging effect of dynamic Plug in Hybrid Electric Vehicle (PHEV) is presented in a renewable energy based electricity distribution system. For planning and designing a distribution system, PHEVs are one of the most important factor as it is going to be a spinning reserve of energy, and also a major load for distribution network. A dynamic load model of PHEVs is introduced here based on third order battery model. To determine the system adequacy, it is necessary to do a micro level analysis to know the PHEVs load impact on grid. Scope of such analysis will cover the performance of wind and solar generation with dynamic PHEVs load, as well as the stability analysis of the power grid to demonstrate that it is important to consider the dynamics of PHEVs load in a renewable energy based distribution network.

Characteristics and applications of energy storage system to power network - A review

Maintaining reliability and stability of a power systems in transmission and distribution level becomes a big challenge in present scenario. Grid operators are always responsible to maintain equilibrium between available power generation and demand of end users. Maintaining grid balance is a bigger issue, in case of any unexpected generation shortage or grid disturbance or integration of any renewable energy sources like wind and solar power in the energy mix. In order to compensate such imbalance and to facilitate more renewable energy sources with the grid, energy storage system (ESS) started to be playing an important role with the advancement of the state of the art technology. ESS can also help to get reduction in greenhouse gas (GHG) emission by means of integrating more renewable energy sources to the grid. There are various types of Energy Storage (ES) technologies which are being used in power systems network from large scale (above 50MW) to small scale (up to 100KW). Based on the characteristics, each storage technology has their own merits and demerits. This paper carried out extensive review study and verifies merits and demerits of each storage technology and identifies the suitable technology for the future. This paper also has conducted feasibility study with the aid of E-SelectTM tool for various ES technologies in applications point of view at different grid locations. This review study helps to evaluate feasible ES technology for a particular electrical application and also helps to develop smart hybrid storage system for grid applications in efficient way.

Human passive motions and a user-friendly energy harvesting system

Energy harvesting for wireless sensors and consumer electronic devices can significantly improve reliability and environmental sustainability of the devices. This is achieved by eliminating the dependency of these devices on rechargeable batteries, using clean and/or renewable energy sources. Energy harvesting from various energy sources is widely discussed among researchers and entrepreneurs, including harvesting energy from microscale phenomena. This topic is receiving increasing attention due to the rising numbers of low-power consumer electronic devices and wireless sensors, but also the increasing demand for more convenient and available devices. This article presents a feasibility study for an energy harvesting system based on a human's breathing motion. The system is based on a modified pants belt that is integrated with an array of piezoelectric films and a harvesting circuit. The proposed energy harvester generates electricity from reciprocal abdominal motions of the human subject. In comparison with existing breathing-based energy harvesters, the proposed system allows for safe and convenient energy harvesting with no influence on the natural movement of the lungs. Stomach pressure analysis and measurement, as well as the design and simulations of the proposed harvester, are presented. © 2013 The Author(s).

Low cost MPPT controller for a photovoltaic-based microgrid

 Photovoltaic based microgrid have been increasingly investigated in recent years, ascribable to their fundamental advantages such as the infinite energy source, environmentally friendly aspect and low upkeep cost. However, in practice, they are still considered as an expensive and low output option of renewable energy resources. To extract the maximum possible power from the output of the PV system, a reliable maximum power point tracker (MPPT) is required. Numerous studies have been conducted to introduce the best MPPT techniques suitable for different types of PV systems. However, they are mostly able to track the MPP from the PV system when the output signals (Voltage and Current) of individual array are available. In this study, a meta-heuristic method, based on particle swarm optimization theory, is used to determine the actual MPP of PV system, including several PV arrays, by only single current sensor at the output terminal. The results of the proposed PSO based technique, for tracking the global MPP in a multidimensional search space, have been presented at the end of this paper.

Bead milling for lipid recovery from thraustochytrid cells and selective hydrolysis of Schizochytrium DT3 oil using lipase

Marine microalgae present a renewable alternative source for sustainable production of omega-3 fatty acids, as compared to conventional sources such as krill oil and fish oil. In this study, we optimised a method for lipid extraction from marine thraustochytrids using a bead mill and enzymatic concentration of omega-3 fatty acids from the thraustochytrid oil. The optimised lipid extraction conditions were, bead size 0.4-0.6μm, 4500rpm, 4min of processing time at 5g biomass concentration. The maximum lipid yield (% dry weight basis) achieved at optimum conditions were 40.5% for Schizochytrium sp. S31 (ATCC) and 49.4% for Schizochytrium sp. DT3 (in-house isolate). DT3 oil contained 39.8% docosahexaenoic acid (DHA) as a percentage of lipid, a higher DHA percentage than S31. Partial hydrolysis of DT3 oil using Candida rugosa lipase was performed to enrich omega-3 polyunsaturated fatty acids (PUFAs) in the glyceride portion. Total omega-3 fatty acid content was increased to 88.7%.

Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting

Wind farms are producing a considerable portion of the world renewable energy. Since the output power of any wind farm is highly dependent on the wind speed, the power extracted from a wind park is not always a constant value. In order to have a non-disruptive supply of electricity, it is important to have a good scheduling and forecasting system for the energy output of any wind park. In this paper, a new hybrid swarm technique (HAP) is used to forecast the energy output of a real wind farm located in Binaloud, Iran. The technique consists of the hybridization of the ant colony optimization (ACO) and particle swarm optimization (PSO) which are two meta-heuristic techniques under the category of swarm intelligence. The hybridization of the two algorithms to optimize the forecasting model leads to a higher quality result with a faster convergence profile. The empirical hourly wind power output of Binaloud Wind Farm for 364 days is collected and used to train and test the prepared model. The meteorological data consisting of wind speed and ambient temperature is used as the inputs to the mathematical model. The results indicate that the proposed technique can estimate the output wind power based on the wind speed and the ambient temperature with an MAPE of 3.513%.

Urbanization, openness, emissions, and energy intensity: a study of increasingly urbanized emerging economies

This paper analyses the impact of urbanization and trade openness on emissions and energy intensity in twenty-two increasingly urbanized emerging economies. We employ three second-generation heterogeneous linear panel models as well as recently developed nonlinear panel estimation techniques allowing for cross-sectional dependence. The empirical results show that population density and affluence increase emissions and energy intensity while renewable energy seems to be dormant in these emerging economies, but non-renewable energy increases both CO2 emissions and energy intensity. In addition, openness significantly reduces both pollutant emissions and energy intensity whereas urbanization significantly increases energy intensity, but it is insignificant in increasing emissions. This may be, in part, due to the recent increasing trend in adopting cleaner technologies in these increasingly urbanized developing economies.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: a comparative study

The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones.