A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

The growing global demand for energy from fossil fuels plays a key role in the upward trend in greenhouse gas (GHG) emissions and air pollutants. Rapid population growth and increasing energy demand in the developing countries have brought many concerns such as poverty, pollution, health and environmental problems. While for these countries, particularly the poorest ones, modern energy is necessary to stimulate production, income generation and social development plus reduce the serious health issues that are caused by the use of fuelwood, charcoal, animal dung and agricultural waste. Solar energy is the best answer to energy poverty and it can provide excellent opportunities for reduction of GHG emissions and indoor air pollution through substituting kerosene for lighting and firewood for cooking. Solar photovoltaic (PV) can be an appropriate technology for a source of renewable electricity in developing nations especially in remote rural areas where grid extensions are financially or technically not viable. PV can also be used to reduce demand for fossil fuels and associated emissions, including carbon dioxide (CO2), nitrogen oxides (NOx) and sulfur dioxide (SO2). The use of PV systems can reduce 69–100 million tons of CO2, 126,000–184,000 t of SO2 and 68,000–99,000 t of NOx by 2030. In case countries use concentrating solar power (CSP) systems, each square meter of concentrator surface is enough to save about 200–300 kg (kg) of CO2 emissions annually. Although there are excellent renewable opportunities in many developing countries, several key barriers have prevented large-scale deployment of solar energy technologies in these countries. This study reviews the sources of energy-related emissions, risks of climate change, global solar energy potential, sustainability indicators of renewable energies, environmental impacts of fossil fuels and renewable energies, benefits of solar energy utilization. It also discusses barriers to widespread use of solar energy.

Potential of solar energy in developing countries for reducing energy-related emissions

In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.

Applications of fuzzy logic in renewable energy systems – A review

The increasing penetration of renewable energy sources (RES) in power systems intensifies the need of enhancing the flexibility in grid operations in order to accommodate the uncertain power output of the leading RES such as wind and solar generation. Utilities have been recently showing increasing interest in developing Demand Response (DR) programs in order to match generation and demand in a more efficient way. Incentive- and price-based DR programs aim at enabling the demand side in order to achieve a range of operational and economic advantages, towards developing a more sustainable power system structure. The contribution of the presented study is twofold. First, a complete and up-to-date overview of DR enabling technologies, programs and consumer response types is presented. Furthermore, the benefits and the drivers that have motivated the adoption of DR programs, as well as the barriers that may hinder their further development, are thoroughly discussed. Second, the international DR status quo is identified by extensively reviewing existing programs in different regions.

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An overview of Demand Response: Key-elements and international experience

Rapidly increasing energy demand and growing concern about economic and environmental consequences call for renewable/sustainable energy technologies׳ adoption in India. Renewable/sustainable energy technologies have faced a number of constraints that have affected their rate of adoption. In this paper an attempt has been made to identify and rank the major barriers in the adoption of ‘renewable and green’ energy technologies in the Indian context. Twenty-eight barriers have been identified from an extensive literature review. These identified barriers have been categorized into seven dimensions of barriers, i.e. Economical & Financial; Market; Awareness & Information; Technical; Ecological and Geographical; Cultural & Behavioral; and Political & Government Issues. Analytical Hierarchy Process (AHP) technique has been utilized for ranking of barriers to adopt renewable/sustainable technologies in the Indian context. All pair comparisons in AHP have been made based on experts’ opinions (selected from academia and industry). Sensitivity analysis has also been made to investigate the priority ranking stability of barriers to adopt renewable/sustainable technologies in the Indian context. This paper may help practitioners, regulators and academician focus their future efforts in adoption of ‘renewable/sustainable energy technologies’ in India. Further, this understanding may be helpful in framing the policies and strategies towards adoption of renewable/sustainable energy technologies.

Barriers to renewable/sustainable energy technologies adoption: Indian perspective

Global electricity demands are increasing at rapid pace Energy supply, their usage and technologies involved need to be more economical, environment friendly and socially sustainable Efforts are being done all over the globe to reduce this greenhouse effect; and renewable energy technologies to combat climate changes, which require extensive changes to the current electricity generation and distribution systems To meet this goal, it is required to optimize the grid operations and available resources to meet the ever increasing energy demands in an efficient, effective and environment sustainable way It has been found that smart grid technologies have not been so popular due to some obstacles that are hindering its maturation and rapid deployment An attempt has been made to identify and analyze the barriers to implement smart grid technologies adoption Twelve relevant barriers towards implementation of smart grid technologies have been identified from extensive literature review and duly validated with experts׳ (from academia and industry) opinions Also, valuable experts׳ opinions have been utilized to identify contextual relationships among these important barriers and a hierarchical model has been developed based on Interpretive Structural Modeling methodology Matrice d׳Impacts Croises-Multipication Applique´ an Classment (MICMAC) analysis has also been used to: classify the barriers based upon dependence and driving power; and validate developed ISM based model “Lack of Regulatory Framework” barrier has been identified as driver or independent level barrier ie most important bottom level barrier hindering adoption of smart grid technologies The developed structured model will help to understand interrelationships and interdependencies among the identified barriers to implement smart grid technologies Different solutions for handling these identified barriers have also been suggested in the paper Organizations involved in power generation and distribution may be benefited by understanding of these barriers, their interactions and suggested mitigation solutions towards effective adoption of smart grid technologies

Adoption of smart grid technologies: An analysis of interactions among barriers

India is fast emerging economy in Asia and world. India's manufacturing sector is growing faster and domestic demand is also increasing. India has a severe electricity shortage. It needs massive additions in capacity to meet the demand of its rapidly growing economy. To maintain the pace of economic growth with reduction in emission of greenhouse gases, India must reduce its dependency on fossil fuels for electrification. Hence the requirement of solar power installations in India has increased. In this context, this study aims to develop a structural model of the barriers to implement solar power installations in India. Thirteen relevant barriers to implement solar power installations have been identified from the literature and subsequent discussions with experts from academia and industry. Contextual relationships among these barriers have been identified and interpretive structural modeling (ISM) technique based, a structural model of barriers to implement solar power installations in India has been developed. MICMAC analysis has also been used to carry out the classification of barriers based on dependence and driving power. One barrier has been identified as top level barrier and six bottom level barriers. This paper also suggests the different ways of removal of these barriers. Better understanding of these barriers would help organizations and government bodies to prioritize and manage their resources in an effective and efficient way so that maximum number of solar power projects can be installed in India.

Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique

Solar energy, at the present time is considered as an important source in electricity generation. Electricity from the solar energy can be generated using solar photovoltaic (PV) modules. The maximization of solar power extracted from a PV module is of special concern as its efficiency is very low. The output power of a PV module is highly dependent on the geographical location and weather conditions such as solar irradiation, shading and temperature. To obtain maximum power from PV module, photovoltaic power system usually requires maximum power point tracking (MPPT) controller. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) based maximum power point tracker for PV module has been presented. To extract maximum power, a DC–DC boost converter is connected between the PV module and the load. The duty cycle of DC–DC boost converter is modified with the help of the ANFIS reference model, so that maximum power is transferred to load. Due to the complexity of the tracker mechanism and non-linear nature of photovoltaic system, the artificial intelligence based technique, especially the ANFIS method, is used in this paper. In order to observe the maximum available power of PV module, the ANFIS reference model directly takes in operating temperature and irradiance level as input. The response of proposed ANFIS based control system shows accuracy and fast response. The simulation result reveals that the maximum power point is tracked satisfactorily for varying irradiance and temperature of PV module. Simulation results are provided to validate the concept.

Modeling of solar PV module and maximum power point tracking using ANFIS

Maximum power point tracking (MPPT) is used to increase the efficiency of solar photovoltaic (PV) systems under varying weather conditions. Conventional MPPT methods have drawbacks in terms of efficiency, accuracy, and flexibility. This paper presents the design and implementation of a maximum power point tracker that uses adaptive neuro fuzzy inference system (ANFIS). Open loop dc-dc boost converter is interfaced between solar PV module and resistive load. The duty ratio of boost converter is varied using ANFIS and PI controller in order to extract maximum possible power under varying solar irradiance and temperature. The simulated results of proposed tracker show improved performance in terms of oscillations about the maximum power point, speed, high gain and sensitivity to parameter variation. The simulation results of this research are presented to validate the concept

Design and Implementation of ANFIS basedMPPT Scheme with Open Loop BoostConverter for Solar PV Module

the goal of this paper is to use the solar power to charge Lithium-ion (Li-ion) batteries, pulse width modulator (PWM) control method is implemented to design and build a solar battery charger prototype. Maximum power point tracking (MPPT) is used in the photovoltaic (PV) system to maximise the PV output power, irrespective of the temperature and irradiation conditions. MPPT system, consisting of a buck-type dc-dc converter, which is controlled by a microcontroller unit, is implemented. It is presented a model for the lithiumion battery (Li-Ion) that is suitable for computer simulation. The used model can be easily modified to fit data from different batteries. The simulation results achieved by using Pspice programs and are in good agreement with the experimental results. These results allowed demonstrating the reliability and validity of the proposed MPPT technique. The battery charger prototype was tested and the results obtained allowed to conclude about the conditions of permanent control on the battery charger.

/pdf/pspice-model-for-optimization-of-battery-charging-using-27qkmem4ue.pdf

Md. Fahim Ansari

Papers

Adoption of smart grid technologies: An analysis of interactions among barriers

Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique

Modeling of solar PV module and maximum power point tracking using ANFIS

Design and Implementation of ANFIS basedMPPT Scheme with Open Loop BoostConverter for Solar PV Module

PSpice Model for Optimization of battery Charging using Maximum power point Tracker