Smart resource control in distribution network to improve the integration level of PV

This project was an innovative approach in developing smart coordination of available energy resources to improve the integration level of PV in distribution network. Voltage and loading issues are considered as the main concerns for future electricity grid which need to be avoided using such resources. A distributed control structure was proposed for the resources in distribution network to avoid noted power quality issues.

Impact of electric energy storage scheduling on reliability of distribution system

The development of Electric Energy Storage (EES) integrated with Renewable Energy Resources (RER) has increased use of optimum scheduling strategy in distribution systems. Optimum scheduling of EES can reduce cost of purchased energy by retailers while improve the reliability of customers in distribution system. This paper proposes an optimum scheduling strategy for EES and the evaluation of its impact on reliability of distribution system. Case study shows the impact of the proposed strategy on reliability indices of a distribution system.

Effect of atmospheric aging on volatility and reactive oxygen species of biodiesel exhaust nano-particles

In the prospect of limited energy resources and climate change, effects of alternative biofuels on primary emissions are being extensively studied. Our two recent studies have shown that biodiesel fuel composition has a significant impact on primary particulate matter emissions. It was also shown that particulate matter caused by biodiesels was substantially different from the emissions due to petroleum diesel. Emissions appeared to have higher oxidative potential with the increase in oxygen content and decrease of carbon chain length and unsaturation levels of fuel molecules. Overall, both studies concluded that chemical composition of biodiesel is more important than its physical properties in controlling exhaust particle emissions. This suggests that the atmospheric aging processes, including secondary organic aerosol formation, of emissions from different fuels will be different as well. In this study, measurements were conducted on a modern common-rail diesel engine. To get more information on realistic properties of tested biodiesel particulate matter once they are released into the atmosphere, particulate matter was exposed to atmospheric oxidants, ozone and ultra-violet light; and the change in their properties was monitored for different biodiesel blends. Upon the exposure to oxidative agents, the chemical composition of the exhaust changes. It triggers the cascade of photochemical reactions resulting in the partitioning of semi-volatile compounds between the gas and particulate phase. In most of the cases, aging lead to the increase in volatility and oxidative potential, and the increment of change was mainly dependent on the chemical composition of fuels as the leading cause for the amount and the type of semi-volatile compounds present in the exhaust.

Grid-connected versus stand-alone energy systems for decentralized power-A review of literature

The decentralized power is characterised by generation of power nearer to the demand centers, focusing mainly on meeting local energy needs. A decentralized power system can function either in the presence of grid, where it can feed the surplus power generated to the grid, or as an independent/stand-alone isolated system exclusively meeting the local demands of remote locations. Further, decentralized power is also classified on the basis of type of energy resources used-non-renewable and renewable. These classifications along with a plethora of technological alternatives have made the whole prioritization process of decentralized power quite complicated for decision making. There is abundant literature, which has discussed various approaches that have been used to support decision making under such complex situations. We envisage that summarizing such literature and coming out with a review paper would greatly help the policy/decision makers and researchers in arriving at effective solutions. With such a felt need 102 articles were reviewed and features of several technological alternatives available for decentralized power, the studies on modeling and analysis of economic, environmental and technological asibilities of both grid-connected (GC) and stand-alone (SA) systems as decentralized power options are presented. (C) 2009 Elsevier Ltd. All rights reserved.

Optimisation Problems in Wireless Sensor Networks : Local Algorithms and Local Graphs

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

The application of islanded photovoltaic cells and battery storage on the reliability of distribution networks

Renewable energy resources, in particularly PV and battery storage are increasingly becoming part of residential and agriculture premises to manage their electricity consumption. This thesis addresses the tremendous technical, financial and planning challenges for utilities created by these increases, by offering techniques to examine the significance of various renewable resources in electricity network planning. The outcome of this research should assist utilities and customers for adequate planning that can be financially effective.

Optimisation problems in wireless sensor networks

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating–dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating–dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs – these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating–dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Decentralized Sustainable Energy Planning of Tumkur District, India

Present work shows the feasibility of decentralized energy options for the Tumkur district in India. Decentralized energy planning (DEP) involves scaling down energy planning to subnational or regional scales. The important aspect of the energy planning at decentralized level would be to prepare an area-based DEP to meet energy needs and development of alternate energy sources at least-cost to the economy and environment. The geographical coverage and scale reflects the level at which the analysis takes place, which is an important factor in determining the structure of models. In the present work, DEP modeling under different scenarios has been carried out for Tumkur district of India for the year 2020. DEP model is suitably scaled for obtaining the optimal mix of energy resources and technologies using a computer-based goal programming technique. The rural areas of the Tumkur district have different energy needs. Results show that electricity needs can be met by biomass gasifier technology, using biomass feedstock produced by allocating only 12% of the wasteland in the district at 8 t/ha/yr of biomass productivity. Surplus electricity can be produced by adopting the option of biomass power generation from energy plantations. The surplus electricity generated can be supplied to the grid. The sustainable development scenario is a least cost scenario apart from promoting self-reliance, local employment, and environmental benefits. (C) 2010 American Institute of Chemical Engineers Environ Prog, 30: 248-258, 2011

Solar energy potential assessment using GIS

Renewable energy resources are those having a cycling time less than 100 years and are renewed by the nature and their supply exceeds the rate of consumption. Renewable energy systems use resources that are constantly replaced in nature and are usually less polluting. In order to tap the potential of renewable energy sources, there is a need to assess the availability of resources spatially as well as temporally. Geographic Information Systems (GIS) along with Remote Sensing (RS) helps in mapping on spatial and temporal scales of the resources and demand. The spatial database of resource availability and the demand would help in the regional energy planning. This paper discusses the application of geographical information system (GIS) to map the solar potential in Karnataka state, India. Regions suitable for tapping solar energy are mapped on the basis of global solar radiation data, and this analysis provides a picture of the potential. The study identifies that Coastal parts of Karnataka with the higher global solar radiation is ideally suited for harvesting solar energy. The potential analysis reveals that, maximum global solar radiation is in districts such as Uttara Kannada and Dakshina Kannada. Global solar radiation in Uttara Kannada during summer, monsoon and winter are 6.31, 4.40 and 5.48 kWh/sq.m, respectively. Similarly, Dakshina Kannada has 6.16, 3.89 and 5.21 kWh/sq.m during summer, monsoon and winter.

The Sustainability of `Sustainable´ Energy Use: Historical Evidence on the Relationship between Economic Growth and Renewable Energy

20 p.

Energy Supply and the Sustainability of Endogenous Growth

29 p.

Sustainable IT and IT for sustainability

Energy and sustainability have become one of the most critical issues of our generation. While the abundant potential of renewable energy such as solar and wind provides a real opportunity for sustainability, their intermittency and uncertainty present a daunting operating challenge. This thesis aims to develop analytical models, deployable algorithms, and real systems to enable efficient integration of renewable energy into complex distributed systems with limited information.

The first thrust of the thesis is to make IT systems more sustainable by facilitating the integration of renewable energy into these systems. IT represents the fastest growing sectors in energy usage and greenhouse gas pollution. Over the last decade there are dramatic improvements in the energy efficiency of IT systems, but the efficiency improvements do not necessarily lead to reduction in energy consumption because more servers are demanded. Further, little effort has been put in making IT more sustainable, and most of the improvements are from improved "engineering" rather than improved "algorithms". In contrast, my work focuses on developing algorithms with rigorous theoretical analysis that improve the sustainability of IT. In particular, this thesis seeks to exploit the flexibilities of cloud workloads both (i) in time by scheduling delay-tolerant workloads and (ii) in space by routing requests to geographically diverse data centers. These opportunities allow data centers to adaptively respond to renewable availability, varying cooling efficiency, and fluctuating energy prices, while still meeting performance requirements. The design of the enabling algorithms is however very challenging because of limited information, non-smooth objective functions and the need for distributed control. Novel distributed algorithms are developed with theoretically provable guarantees to enable the "follow the renewables" routing. Moving from theory to practice, I helped HP design and implement industry's first Net-zero Energy Data Center.

The second thrust of this thesis is to use IT systems to improve the sustainability and efficiency of our energy infrastructure through data center demand response. The main challenges as we integrate more renewable sources to the existing power grid come from the fluctuation and unpredictability of renewable generation. Although energy storage and reserves can potentially solve the issues, they are very costly. One promising alternative is to make the cloud data centers demand responsive. The potential of such an approach is huge.

To realize this potential, we need adaptive and distributed control of cloud data centers and new electricity market designs for distributed electricity resources. My work is progressing in both directions. In particular, I have designed online algorithms with theoretically guaranteed performance for data center operators to deal with uncertainties under popular demand response programs. Based on local control rules of customers, I have further designed new pricing schemes for demand response to align the interests of customers, utility companies, and the society to improve social welfare.

Optimization and Control of Power Flow in Distribution Networks

Climate change is arguably the most critical issue facing our generation and the next. As we move towards a sustainable future, the grid is rapidly evolving with the integration of more and more renewable energy resources and the emergence of electric vehicles. In particular, large scale adoption of residential and commercial solar photovoltaics (PV) plants is completely changing the traditional slowly-varying unidirectional power flow nature of distribution systems. High share of intermittent renewables pose several technical challenges, including voltage and frequency control. But along with these challenges, renewable generators also bring with them millions of new DC-AC inverter controllers each year. These fast power electronic devices can provide an unprecedented opportunity to increase energy efficiency and improve power quality, if combined with well-designed inverter control algorithms. The main goal of this dissertation is to develop scalable power flow optimization and control methods that achieve system-wide efficiency, reliability, and robustness for power distribution networks of future with high penetration of distributed inverter-based renewable generators.

Proposed solutions to power flow control problems in the literature range from fully centralized to fully local ones. In this thesis, we will focus on the two ends of this spectrum. In the first half of this thesis (chapters 2 and 3), we seek optimal solutions to voltage control problems provided a centralized architecture with complete information. These solutions are particularly important for better understanding the overall system behavior and can serve as a benchmark to compare the performance of other control methods against. To this end, we first propose a branch flow model (BFM) for the analysis and optimization of radial and meshed networks. This model leads to a new approach to solve optimal power flow (OPF) problems using a two step relaxation procedure, which has proven to be both reliable and computationally efficient in dealing with the non-convexity of power flow equations in radial and weakly-meshed distribution networks. We will then apply the results to fast time- scale inverter var control problem and evaluate the performance on real-world circuits in Southern California Edison’s service territory.

The second half (chapters 4 and 5), however, is dedicated to study local control approaches, as they are the only options available for immediate implementation on today’s distribution networks that lack sufficient monitoring and communication infrastructure. In particular, we will follow a reverse and forward engineering approach to study the recently proposed piecewise linear volt/var control curves. It is the aim of this dissertation to tackle some key problems in these two areas and contribute by providing rigorous theoretical basis for future work.

Energy analysis of the ring seine operations, off Cochin, Kerala

Ring seines are lightly constructed purse seines adapted for operation in the traditional sector. Fish production and energy requirement in the ring seine operations, off Cochin, Kerala, India are discussed in this paper, based on data collected during 1997- 1998. The results reflect the Gross Energy Requirement (GER) situation that existed during 1997-1998. Mean catch per ring seiner per year worked out to be 211.9 t of which sardines (Sardinella spp.) constituted 44.3%, followed by Indian mackerel (Rastrelliger kanagurta) 29.7%, carangids 11.4%, penaeid prawns 2.2%, pomfrets 1.1% and miscellaneous fish 11.3%. Total energy inputs into the ring seine operations were estimated to be 1300.8 GJ. Output by way of fish production was determined to be 931.85 GJ. GER is the sum of all non-renewable energy resources consumed in making available a product or service and is a measure of intensity of non-renewable resource use. GER per tonne of fish landed by ring seiners was estimated to be 6.14. Among the operational inputs, kerosene constituted 73.4% of the GER, followed by petrol (12.7%), diesel (6.7%) and lubricating oil (2.4%). Fishing gear contributed 3.8%, engine 0.8% and fishing craft 0.3% of the GER. Energy ratio for ring seining was 0.72 and energy intensity 1.40.

Gross energy requirement in fishing operations

Energy is a key input into the fish harvesting process. Efficient use of energy helps in reducing operational costs and environmental impact, while increasing profits. Energy optimisation is an important aspect of responsible fishing as enunciated in the Code of Conduct for Responsible Fisheries. Gross Energy Requirement (GER) is the sum of all non-renewable energy resources consumed in making available a product or service and is expressed in energy units per physical unit of product or service delivered. GER is a measure of intensity of non-renewable resource use and it reflects the amount of depletion of earth’s inherited store of non-renewable energy in order to create and make available a product or service. In this study, GER in fish harvesting up to the point of landing is estimated in selected fish harvesting systems in the small-mechanised sectors of Indian fisheries and compared with reported results from selected non mechanised and motorised fishing systems to reflect the situation during 1997-1998. Among the fish harvesting systems studied, GER t fish-1 ranged from 5.54 and 5.91 GJ, respectively, for wooden and steel purse seiners powered by 156 hp engines; 6.40 GJ for wooden purse seiner with 235 hp engine; 25.18 GJ for mechanised gillnet/line fishing vessel with 89 hp engines; to 31.40 and 36.97 GJ, respectively, for wooden and steel trawlers powered by 99-106 hp engines.