smart grid

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SMART GRIDS: A PARADIGM SHIFT ON ENERGY GENERATION AND

DISTRIBUTION WITH THE EMERGENCE OF A NEW ENERGY

MANAGEMENT BUSINESS MODEL

JESUS ALVARO CARDENAS International Business

APPROVED:

Leopoldo A. Gemoets, D.Sc., Chair

Jose H. Ablanedo-Rosas, Ph.D

Kallol K. Bagchi, Ph.D.

Robert J. Sarfi. Ph.D.

Bess Sirmon-Taylor, Ph.D.

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by

Jesus A. Cardenas 2014

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SMART GRIDS: A PARADIGM SHIFT ON ENERGY GENERATION AND

DISTRIBUTION WITH THE EMERGENCE OF A NEW ENERGY

MANAGEMENT BUSINESS MODEL

by

JESUS ALVARO CARDENAS, BSEE, MBA, MSIE

DISSERTATION

Presented to the Faculty of the Graduate School of The University of Texas at El Paso

in Partial Fulfillment of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY

International Business

THE UNIVERSITY OF TEXAS AT EL PASO May 2014

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ABSTRACT

An energy and environmental crisis will emerge throughout the world if we continue with our current practices of generation and distribution of electricity. A possible solution to this problem is based on the Smart grid concept, which is heavily influenced by Information and Communication Technology (ICT). Although the electricity industry is mostly regulated, there are global models used as roadmaps for Smart Grids’ implementation focusing on technologies and the basic generation-distribution-transmission model. This project aims to further enhance a business model for a future global deployment. It takes into consideration the many factors interacting in this energy provision process, based on the diffusion of technologies and literature surveys on the available documents in the Internet as well as peer-reviewed publications. Tariffs and regulations, distributed energy generation, integration of service providers, consumers becoming producers, self-healing devices, and many other elements are shifting this industry into a major change towards liberalization and deregulation of this sector, which has been heavily protected by the government due to the importance of electricity for consumers.

We propose an Energy Management Business Model composed by four basic elements: Supply Chain, Information and Communication Technology (ICT), Stakeholders Response, and the resulting Green Efficient Energy (GEE). We support the developed model with an exhaustive literature survey, diffusion analysis of the different technologies under the umbrella of Smart Grids (SG), and two surveys: one administered to peers and professionals, and another for experts in the field, based on the Smart Grid Carnegie Melon Maturity Model (CMU SEI SGMM). The contribution of this model is a simple path to follow for entities that want to achieve environmental friendly energy with the involvement of technology and all stakeholders.

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LIST OF TABLES

Table 2.1: Results of Hypotheses H1 ... 25

Table 2.2: General classification of this paper ... 34

Table 2.3: Disciplines and Descriptions (Chicco, 2010) ... 39

Table 2.4: Row & Column Percentages for Research Classification versus Focus ... 47

Table 2.5: Row & Column Percentages for Data Collection versus Paper’s Focus ... 48

Table 2.6: Row & Column Percentages for Data Analysis vs. Paper’s Focus ... 48

Table 2.7: Row & Column Percentages for Conference papers’ SG technologies vs. Purpose ... 49

Table 2.8: Row & Column Percentage for Journal Papers’ SG technologies vs. Purpose ... 50

Table 2.9: Row & Column Percentages for Country vs. Paper’s Purpose ... 51

Table 2.10: Row & Column Percentages for Country vs. SG Technology ... 52

Table 2.11: Regression statistics for relationship of Journal and Conferences’ papers ... 54

Table 2.12: Category mixes related to SGD techs ... 56

Table 2.13: Hypotheses H2 results ... 63

Table 2.14: Top 25 words on number of mentions in the past 5 years ... 64

Table 2.15: Top 20 SGD Technologies mentions in the past 6 years ... 65

Table 2.16: Bass’ Diffusion Model results for SGD technologies ... 69

Table 2.17: Distribution of top 20 countries of origin of SGD papers’ authors ... 71

Table 2.18: Distribution of papers by number of authors ... 71

Table 2.19: Comparison of Journal and Conference papers by country of authors ... 72

Table 2.20: Distribution of authors by Country and writing order ... 73

Table 2.21: Distribution of top 20 most prolific writers of SGD papers ... 74

Table 2.22: Type of publication of most prolific authors (co-authors)... 74

Table 2.23: DoE distribution on selected categories ... 84

Table 2.24: EPRI distribution on selected categories ... 85

Table 2.25: Academic Research distribution by categories ... 85

Table 2.26: Comparisons between DoE, EPRI and the Literature Survey ... 86

Table 2.27: Gap from the average of DoE and EPRI versus Literature survey ... 86

Table 2.28: Correlation results for comparison groups ... 86

Table 2.29: Hypotheses H3 results ... 87

Table 3.1: Consumption and generation regression lines’ statistics ... 98

Table 3.2: Sensitivity analysis for the crossing-point year ... 99

Table 3.3: Consumption’s sensitivity analyses ... 101

Table 3.4: Generation sources sensitivity analysis ... 102

Table 3.5: Regression statistics for Generation and Imports ... 103

Table 3.6: Regression lines for Energy use and availability ... 105

Table 3.7: Sensitivity analysis for crossing line year for use and available energy ... 106

Table 3.8: Capacity factors ... 108

Table 3.9: Hydro capacity (KWh) ... 109

Table 3.10: Hydro production (KWh) ... 110

Table 3.11: Nuclear Capacity (KWh) ... 110

Table 3.12: Nuclear Production (KWh) ... 111

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Table 3.16: Thermal Production (KWh) ... 113

Table 3.17: Wind Capacity (KWh) ... 113

Table 3.18: Wind Production (KWh) ... 114

Table 3.19: Evolution of Sources of Electricity Generation in the US ... 114

Table 3.20: Diffusion Indexes for Wind Electricity Generation in the US by States ... 115

Table 3.21: t-test for top 10 States using Wind generated Electricity ... 116

Table 3.22: Diffusion Indexes for Solar Electricity Generation in the US by States ... 118

Table 3.23: t-test for top 10 States using Solar generated Electricity ... 118

Table 3.24: Hypotheses H4 Results ... 123

Table 3.25: Regional Clustering for the US... 124

Table 3.26: Ownership of the utility Company ... 125

Table 3.27: Urban concentration indexes for the US ... 127

Table 3.28: Cluster Totals for Diffusion ... 129

Table 3.29: t-test Results for Clusters ... 129

Table 3.30: Average statistics by States’ Clusters ... 130

Table 3.31: Standard Deviation statistics by States’ Clusters... 130

Table 3.32: Standard Deviation t-test for States’ Clusters ... 130

Table 3.33: Diffusion Results by States’ Division... 131

Table 3.34: t-test Results for Divisions ... 132

Table 3.35: Average statistics by States’ Clusters ... 132

Table 3.36: Standard Deviation statistics by States’ Divisions ... 133

Table 3.37: Statistics from the Individual States ... 133

Table 3.38: Standard Deviation t-test for States’ Divisions ... 134

Table 3.39: Top 10 States Diffusion Statistics ... 134

Table 3.40: Diffusion Statistics by Company Ownership ... 135

Table 3.41: Diffusion Statistics for Ownership Categories ... 136

Table 3.42: Diffusion Statistics for Urban Concentration ... 138

Table 3.43: Hypotheses H5’s Results ... 139

Table 3.44: Regression results for gasoline price vs PHEV sales ... 142

Table 3.45: Average tax credits vs. PHEVs sold in the US ... 144

Table 3.46: Regression results for PHEV price vs sales ... 146

Table 3.47: Registered PHEVs vs. charging stations per state ... 147

Table 3.48: PEV Sales in 2013, Battery Size and Miles Run in One Charge ... 148

Table 3.49: Hypotheses H6 Results ... 149

Table 3.50: Categories of Security Breaches (Source: Privacy Rights Clearinghouse) ... 155

Table 3.51: Breaches’ Victims Categories ... 156

Table 3.52: Number of Breaches versus Type of Attack ... 158

Table 3.53: Number of Victims by Type of Attack versus Breach ... 158

Table 3.54: Number of Breaches and Victims per State ... 159

Table 3.55: Detail of Victims per Type of Breach ... 160

Table 3.56: Types of Breaches versus Victimized Areas ... 161

Table 3.57: Breached Victims versus Victimized Sector ... 161

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Table 4.2: Statistics from Question # 4 ... 192

Table 4.3: Row and Columns Percentages of Bucket Assignments ... 194

Table 4.4: ANOVA analyses for Bucket’s Ranks ... 195

Table 4.5: Statistics from Society Responsibility’s Question ... 196

Table 4.6: Word mining of individual inputs ... 198

Table 4.7: Residence States of the Survey Respondents ... 199

Table 4.8: Elements, Questions and Definitions ... 200

Table 4.9: Loading and Cross-loading ... 201

Table 4.10: Latent Variables Coefficients ... 201

Table 4.11: Correlations among I vs. Square root of AVE ... 202

Table 4.12: P-Values for Model’s Correlations ... 202

Table 4.13: SGMM Survey’s Questions ... 206

Table 4.14: Our Survey’s Questions ... 206

Table 4.15: Elements and Questions for the PLS Analysis ... 210

Table 4.16: Means and t-tests for Questions not used in the Model ... 211

Table 4.17: Gender Differences on SG Technology (Means & Std. Dev.) ... 212

Table 4.18: Occupation Driven Differences on SG Technology (Means & Std. Dev.) ... 212

Table 4.19: Year of Experience Driven Differences on SG Technology (Means & Std. Dev.) . 213 Table 4.20: Education Driven Differences on SG Technology (Means & Std. Dev.) ... 214

Table 4.21: Location Driven Differences on SG Technology (Means & Std. Dev.) ... 215

Table 4.22: Survey’s Sample Analysis ... 215

Table 4.23: Model Fit Results ... 216

Table 4.24: Loading and Cross-loading Results ... 217

Table 4.25: Model’s Path Coefficients ... 218

Table 4.26: Model’s Path Coefficients p-values ... 218

Table 4.27: Model’s Correlations of I vs. Square root of AVEs ... 219

Table 4.28: Model’s Correlations p-values ... 219

Table 4.29: Model’s Latent Variable coefficients ... 220

Table 4.31: Sources of Electricity Generation by Census Division ... 227

Table 4.32: Sources of Electricity Generation per State ... 228

Table 4.33: Generation Costs for Sources of Electricity ... 229

Table 4.34: States with Electricity Deregulation Status ... 231

Table 4.35: Competition by State and Ownership of Utility Companies ... 232

Table 4.36: Competition compared to Deregulation Status ... 233

Table 4.37: Cost per Energy generation Mix by State ... 234

Table 4.38: 27 Regulated Entities Regression Results with R2=0.794 ... 234

Table 4.39: Regulated formerly Deregulated Entities Regression Results ... 235

Table 4.40: 16 Deregulated Entities Regression Results with R2=0.765 ... 235

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LIST OF FIGURES

Figure 2.1: SWOT Analysis ... 15

Figure 2.2: Log trend line of SG articles ... 18

Figure 2.3: Selected articles by categories ... 19

Figure 2.4: Growth of SG by discipline ... 20

Figure 2.5: Time series for SG technologies (Cardenas et al., 2011) ... 20

Figure 2.6: SG technologies per country ... 21

Figure 2.7: SG technologies by state ... 23

Figure 2.8: Normalized articles’ mentions of technology per state ... 23

Figure 2.9: Distribution of Research Papers ... 34

Figure 2.10: Primary purpose of papers ... 36

Figure 2.11: Analyzed papers by research purpose ... 36

Figure 2.12: Research by collection approach ... 37

Figure 2.13: Papers by data collection categories & time ... 37

Figure 2.14: Data analysis techniques used ... 38

Figure 2.15: Data analysis techniques evolution in time ... 39

Figure 2.16: Distribution of papers by category ... 40

Figure 2.17: Number of papers by type in time ... 40

Figure 2.18: Categories of papers by topics... 42

Figure 2.19: Categories by time ... 43

Figure 2.20: Distribution of SGD technologies ... 44

Figure 2.21: Trend of SGD technologies in time ... 45

Figure 2.22: Distribution of papers by country ... 46

Figure 2.23: Trends of Papers by First Author’s Country and Year ... 46

Figure 2.24: Conference papers by continent and year ... 52

Figure 2.25: Journal papers by continent and year ... 53

Figure 2.26: Conference papers’ topics by continent and Chicco’s categories ... 54

Figure 2.27: Journal papers’ topics by continent and Chicco’s categories ... 55

Figure 2.28: Category mixes versus time ... 56

Figure 2.29: ICT Papers by year & country ... 57

Figure 2.30: ICT Papers by SG Technology and Country ... 57

Figure 2.31: Physical Infrastructure Papers by Country and Year ... 58

Figure 2.32: Physical Infrastructure Papers by SG Technology and Country ... 59

Figure 2.33: Economics Related Papers by Country and Year ... 60

Figure 2.34: Economics related papers by SG Technology and Country ... 60

Figure 2.35: Environmental Related Papers by Country and Year ... 61

Figure 2.36: Environmental Related Papers by SG Technology and Country ... 62

Figure 2.37: Text Mining Cloud ... 70

Figure 2.38: Distribution of papers by number of authors ... 72

Figure 2.39: DOE recovery awards ... 78

Figure 2.40: EPRI funded projects... 81

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Figure 3.3: Projected lines of consumption and generation until 2035 ... 99

Figure 3.4: Evolution of the crossing point from the sensitivity analysis ... 100

Figure 3.5: Other uses of electricity to consider (source: UN data) ... 103

Figure 3.6: Importations of electricity and current trend ... 104

Figure 3.7: Trend of global use and availability of electricity (source: UN data) ... 104

Figure 3.8: Projected lines of use and availability of energy (source: UN data) ... 105

Figure 3.9: Growth of electricity capacity assuming 6870 hours per year (source: UN data) ... 107

Figure 3.10: Sources of production of electricity (source: UN data) ... 107

Figure 3.11: Capacity percentage -Production vs. installed capacity (source: UN data) ... 108

Figure 3.12: Diffusion of Wind Generation by State ... 116

Figure 3.13: Diffusion of Solar Generation by State ... 117

Figure 3.14: Energy generation using coal (source: World Bank database) ... 119

Figure 3.15: Electricity generated using hydro power (source: World Bank database) ... 119

Figure 3.16: Electricity generated with natural gas (source: World Bank database) ... 120

Figure 3.17: Electricity generated using nuclear plants (source: World Bank database) ... 120

Figure 3.18: Electricity generated burning oil (source: World Bank database) ... 121

Figure 3.19: Global energy generation sources (source: World Bank database) ... 121

Figure 3.20: Worldwide top electricity generation producers ... 122

Figure 3.21: Diffusion Speeds of AMI for different states clusters ... 128

Figure 3.22: Diffusion Curves by Cluster of States by Division ... 131

Figure 3.23: Diffusion curves for the Top Ten States ... 135

Figure 3.24: Diffusion Speeds of AMI for different states clusters ... 136

Figure 3.25: Diffusion curves by Utility Company Ownership ... 137

Figure 3.26: Diffusion Speeds for AMI depending on Urban Concentration... 138

Figure 3.27: Electric vehicles produced in the U.S. ... 142

Figure 3.28: Average Gasoline Prices (Source: eia.gov) ... 143

Figure 3.29: Hybrid vehicles breakdown by brand ... 144

Figure 3.30: Number of PHEVs sold versus average price ... 145

Figure 3.31: Plug-in Electric vehicles breakdown in the U.S. ... 147

Figure 3.32: Types of Cyber Attacks based on Chen et al. (2012) ... 153

Figure 3.33: Number of Breaches per Year ... 156

Figure 3.34: Number of Breaches Victims per Year ... 156

Figure 3.35: Number of Breaches per Categories and Groups of Victims ... 157

Figure 3.36: Number of Breached Victims by Group ... 157

Figure 3.37: Diffusion of Wind and Solar Generated Electricity by Households ... 164

Figure 3.38: Smart Meters Diffusion by Census Division and Total US ... 165

Figure 3.39: Electric Vehicles Diffusion in the US ... 166

Figure 3.40: Cyber Attacks Diffusion in the US... 167

Figure 3.41: Smart grid Technologies Diffusion Comparison... 168

Figure 4.1: What is the smart grid? (Source: http://www.smartgrid.gov/the_smart_grid) ... 170

Figure 4.2: Environmental Performance Index (Esty et al., 2006) ... 173

Figure 4.3: ENSG Road Map for UK Smart Grid Deployment ... 175

Figure 4.4: The European Union’s Smart Grid vision (source: VDE, 2010) ... 177

Figure 4.5: NIST Smart Grid Framework 1.0 ... 178

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Figure 4.8 Masdar City Energy (source: http://masdarcity.ae/en/) ... 181

Figure 4.9: Texas Smart Grid Investment Model (Source: SGRC) ... 182

Figure 4.10: Ontario Smart Grid (source: http://ieso-public.sharepoint.com) ... 183

Figure 4.11: Enhanced Model by Blocks related to PDCA Cycle ... 186

Figure 4.12: Proposed Enhanced Model ... 187

Figure 4.13: Comparison of 2010 Census results and survey respondents... 190

Figure 4.14: Comparison of Educational Level of the Respondents vs. 2010 Census ... 191

Figure 4.15: Household income comparison of 2010 census and survey respondents ... 192

Figure 4.16: Survey smart grid focus’ importance evaluation ... 193

Figure 4.17: Who is responsible for smart grid’s technologies? ... 194

Figure 4.18: What is important for the society? ... 196

Figure 4.19: Personal importance responses ... 197

Figure 4.20: Range on cost of energy responses... 198

Figure 4.21: Smart Use of Energy Survey Model... 200

Figure 4.22: First PLS Model with Results ... 203

Figure 4.23: Detailed Enhanced Energy Management Business Model ... 205

Figure 4.24: Second PLS Model with Results ... 219

Figure 4.25: Total Electricity Consumption by Census Division ... 225

Figure 4.26: Total Electricity Revenue by Census Division ... 226

Figure 4.27: Cost of Electricity per Census Division ... 226

Figure 4.28: Regression Predicted Values for the Regulated Entities ... 236

Figure 4.29: Regression Predicted Values for the Formerly Deregulated Entities ... 237

Figure 4.30: Regression Predicted Values for the Deregulated Entities ... 237

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CHAPTER 1: INTRODUCTION

1.1 BACKGROUND INFORMATION

In the past one hundred years, countries around the world have been investing in monolithic transmission, distribution and generation infrastructures to support growing electricity needs usually referenced in the sequence of the value chain, generation, transmission and distribution. Unfortunately, these infrastructures have not been robust enough, as there is an estimated global loss of energy ranging from 10 to 52%, mostly due to distribution losses and thefts—which depend on the advancement of the available technology and implemented theft controls in every country (Najjar et al., 2012). These losses in the US are smaller than many other countries, for instance in 1995 they were 7.2%, with 40% of the losses coming from transformers and 60% from the lines (Hong & Burke, 2010).

Considering some of the concerns that have an environmental impact, the transportation and energy generation sectors are the top contributors of CO2 to the atmosphere, with 20% and 40% of the emissions respectively (Lo & Ansari, 2012). These sectors mostly depend on burning hydrocarbon based fuels that create emissions which then affect the environment. There is an old initiative toward the generation of energy using renewable resources but it has not reached the right price yet, but at the same time, consumers are buying more devices for modern world needs, namely consumer electronics, which only increases the demand of energy and, as a result of this process, more energy needs to be generated, hence more harm to our environment.

By the year 2030, the consumption of electricity throughout the world is expected to increase 76% (Ramchurn et al., 2012). In order to satisfy this required electricity demand, the actual generation processes need to increase, thus increasing the emission of CO2 and SOx to the

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reduce contamination in the short term. Battaglini et al. (2009) refers specifically to the many environmental protection requirements set forth by the European Community.

To address these environmental concerns, there is an ongoing integration and growth of new cleaner sources of energy such as wind, photovoltaic, natural gas, nuclear, and others. Renewable resources have been growing as well, including nuclear generation which has reached 6% of the world’s total produced energy. However, following the incident provoked by the tsunami in the Fukushima nuclear plant in Japan on March 11, 2011, the reaction of the Federal Republic of Germany was to immediately close 8 nuclear sites and schedule the closure of the 9 remaining plants in 10 years (Römer et al., 2012). The rest of the world is also taking precautions to reduce or even eliminate nuclear energy generation, unless a breakthrough technology is discovered.

With the task of reducing global contamination, scientists and engineers face the challenge of reducing contamination and making better use of the currently generated electricity via reduction of losses inherent to the distribution and transmission processes of the traditional grid. One possible solution to this problem is the Smart Grid, which is based on recent technologies: The Smart grid is characterized by: the efficient distribution of energy with the inclusion of state of computer power, the use of renewable resources to generate electricity, participation of the consumers in the process by generating and/or conserving energy, low latency feedback to consumers and utility companies about real-time consumption via smart meters to be able to take advantage of smart rates, the use of electric vehicles’ batteries to store and distribute energy at homes, and distributed energy resources, among others.

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The United States seems to be the leader in this effort with the support of the Electric Power Research Institute (EPRI), National Institute of Standards and Technology (NIST), Department of Energy (DoE), National Rural Electric Cooperative Association (NRECA), Edison Electric Institute (EEI), and the American Public Power Association (APPA) (Lo & Ansari, 2012).

Other national governments are promoting efforts for the implementation of Smart Grids in the near future. For instance, Korea launched the K-grid project in 2002 (Son & Chung, 2009), India created the Indian Smart Grid Task Force (Mukhopadhyay et al., 2012), and China formed the Strong and Smart Grid (SSGC) (Uslar et al., 2012).

There are important efforts in the promotion of Smart Grids in Europe, where one of the biggest concerns seems to be the implementation of advanced meters and “green” energy. The US, being leader in developing smart grids appropriated $4.5 billion of the American Recovery and Reinvestment Act (ARRA) to the Department of Energy (DOE) and the Office of Electricity Delivery and Energy Reliability (OE) for deployment of programs such as Smart Grid Investment Grant (SGIG) and the Smart Grid Demonstration (SGDP) Program The Electric Power Research Institute (EPRI) has also been working on this effort with the IntelliGrid Program which focuses on standards, interoperability and cyber security.

When we consider the global amount of monies invested on Smart Grid research, the majority comes from the United States, with 31% as reported by Bloomberg New Energy Finance (BNEF). Although the US is the leader, analysts predict that China will overtake this leadership position because the Smart Grid program launched by the Obama administration comes to an end in the year 2015. At the same time, China is continuously growing, with an investment of $3.2 billion compared to the US that has already spent $4.3 billion in 2012.

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The Smart Grid brings a two-way communication and flow of energy, instead of the traditional one-way flow from traditional electricity (Ramchurn et al., 2012) and information systems (Fadlullah et al., 2012). The US government has invested $3.4 billion dollars in grants for the investigation of Smart Grids (Güngör et al., 2011).

As ICT has been evolving from wire-line to wireless media, there are important proposals about the concept of “ZigBee smart energy” with wireless communications to remotely control devices. The utility company or the consumer will be able to remotely turn appliances, or other devices, –on or off depending on their needs and based on the cost of energy or present environmental conditions.

With all these technologies under the umbrella of Smart Grids, we chose to focus only on energy distribution for the literature survey in chapter 2. Distribution is a current fast-growing area and very visible to consumers, utility companies, and governments who are trying to involve the general public in this discussion. If distribution is enhanced, the expected result is energy conservation to avoid unnecessary investments in new large generating plants by reducing energy consumption.

1.2 THE BIRTH OF A NEW MODEL

Through this dissertation we developed the advent of a new decentralized Energy Management Business Model that is changing the role of the consumer, utilities and government with the emergence of new and advanced technology. The elements of the model are analyzed and measured in regards to their impacts on the implementation of ICT in the energy management systems. Prior efforts have tried to focus on consumers’ participation via smart houses (Tanaka et al., 2012) interconnected with control systems that include distributed

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in a way the diffusion of distributed generation (DG), we research and analyze all sources of electricity generation in chapter 3.

Another component of the model also includes the vehicle-to-grid (V2G) as a source of energy, where the battery pack is charged while being connected to the grid, and later, the battery will become a major source of energy (Khayyam et al., 2012; Erol-Kantarci & Mouftah, 2011). Chapter 5 presents the diffusion of electric vehicles for both Plug-In Hybrid (PHVE) and Plug-in Electric Vehicles (PEV). Although EVs are not considered commonly as sources of energy, their role in the future seems to be critical to achieve the environmental goals because one of the major sources of contamination are the emissions of the fuel vehicles on the road. A factor that needs to be carefully considered is the timing and places for charging batteries of these cars, or the suburbs will collapse at the evening hours when most people go home and plug the car to the grid. There are many studies using Particle Swarm Optimization (PSO) as a strategy for the battery charging process during the night (Celli et al., 2012; Sousa et al., 2012; Silva et al., 2012)

Other scholars focus on the outcome of their own models, environmental protection being one of the most mentioned, (Lo & Ansari, 2012), energy conservation (Feng & Yuexia, 2011), and social participation via consumers who explore energy resources possibilities (Aliprantis et al., 2010). Based on the prior models, we developed our own energy management model that includes some of the above mentioned elements that are further analyzed in the following chapters.

1.3 THE MOST IMPORTANT ELEMENTS

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of the model. Without contemporary advances in technology, it would be very difficult to look for the levels of efficiency that we can achieve nowadays. For instance, we can have a blackout and the backup source can be activated without our human senses perceiving it; also, informing consumers about the instantaneous price of electricity throughout the day could not be possible without computerized networks reaching every home and maintaining two-way communication.

This communication has a potential issue, namely cybersecurity because with all meters connected to huge networks, it would be very difficult to develop robust enough systems to prevent attacks, and what is more critical and vulnerable for the energy sector is the false data injection, as presented in Chapter 3. Although these types of attacks are not frequent, the potential capability of detection is low, increasing the vulnerability of all energy systems, which now require a computerized system to monitor the requests and, compared to normal consumption patterns, they can detect these types of breaches.

From the different technologies under the umbrella of Smart Grids (SG), we specifically focus on the most visible element for SG integration, that is, the Advanced Metering Infrastructure (AMI), or smart meters as they are called in Europe. Their diffusion can be representative of the SG progress, so we analyzed them using the Bass Diffusion model in Chapter 3. As a new business model was proposed by Lehr (2013) with focus on the utility companies, the analysis is conducted by region, division, ownership, and urban concentration of the population to better understand the trends and develop our prognosis.

1.4 THE ENHANCED ENERGY MANAGEMENT BUSINESS MODEL

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among the selected elements to determine if they are strongly influenced among themselves. The extensive literature survey extends the academic knowledge on the areas where literature is focusing and those areas that are being neglected at this time. The Model is presented in detail in Chapter 4 of this dissertation and is supported by two models that are similar and grounded in theory. We have conducted also two surveys that backup the model, as both the structural equation modeling (SEM) and Partial Least Squares (PSL) models support our proposal. In general, our model seems to cover the key requirements for a Smart Grid model, and it is supported by other research and our primary data.

The enhanced model that we propose in this dissertation introduces how distributed generation and possibly the electric vehicle will provide electricity that will be stored in distributed storage to use this energy when necessary, not as generated. Once the energy is stored, it can be automatically distributed with ICT devices communicating through networks to the consumers that require it. Consumers in general will have the ability of responding to the tariff information, so they can increase or decrease consumption based on the real-time cost of this service. By reducing peaks and optimizing consumption, we expect efficient and environmental friendly energy.

There are some new models but they are unilateral, with the utility company making all decisions, and consumers just accepting them. The enhanced model foresees decentralized generation where consumers are important actors in the process. Another figure that might come forward very strongly is the service providers who will do the distribution of the available stored energy in a free market. The consumers will also have an important role on the demand response area, as they will decide when to use energy and when not.

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The involvement of all stakeholders in the process will certainly help for better results. Utility companies have traditionally made most decisions along with the regulating bodies, while the consumer perception and opportunities have not been explored yet. Allowing two-way communication with the consumer opens up a horizon of possibilities to make the distribution process more efficient.

To validate the proposed enhanced model we used the literature survey to show how these elements are the most important ones for published researches at this time. The relationships among the elements were modeled and validated with a survey conducted to specialists in the field who show that the storage element is the only one that they do not see as important because we are in the process of designing better batteries, and this is the Achilles heel. We know that at this time there is no good option as of yet. The practical proof of the model is not part of the dissertation, because it would have to be implemented and we do not have the resources. This is a conceptual enhanced model.

1.5 RESEARCH QUESTION AND OBJECTIVES

With the proposed model we aim to answer the following problem statement: How can we, as a global society, prepare for the imminent paradigm shift towards distributed generation and distribution automation with ICT and other technologies, which introduce a new business model where consumers might also be producers, whereby millions of connections can make our systems vulnerable, and the economics seem unfeasible? In order to answer this question, we divide the dissertation into three major blocks to achieve the following objectives:

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 Analyze and design a business model with consumers being also producers of energy.

1.6 CONTRIBUTIONS OF THIS DISSERTATION

This dissertation brings forth contributions to the business administration, information systems and energy areas. The Smart Grid is a topic that recently appeared and along with the Information and Communication Technology (ICT) is provoking major changes in the distribution of electricity throughout the world. Being such a new topic, there are not many studies focusing on this area and this dissertation contributes with studies never done before. The major contributions in this dissertation are:

 A Fad or fashion study for peer-reviewed literature, from Internet & Web of Science about Smart Grid, applied to this area for the first time.

 An exhaustive literature survey was conducted on 966 papers about Smart Grid to classify them into six categories set by Chicco and the different technologies.

 A model was developed for Green Efficient Energy based on global Roadmaps.

 The perspectives of government, practitioners and academics on regards to Smart Grids were compared with a novel simple classification method.

 Diffusion curves for Solar, Wind, Electric Vehicles and Cyber breaches for the first time.  Partial least Squares (PLS) models were developed to support the proposed model.

 First evaluation of Smart Grid by 184 professionals not involved in the specific field, but responding as consumers of electricity.

 A snapshot of the opinion of 32 specialists in the field about the elements in the model



A proposed energy management model validated with a survey using questions from the

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CHAPTER 2: COMPREHENSIVE RESEARCH IN SMART GRID

2.1 BACKGROUND INFORMATION

It is projected that oil and gas reserves will be depleted by 2060-2065 (Klimenko et al., 2008). Needless to say, in the face of dwindling carbon based fuel reserves and fears associated with energy independence, there is considerable attention being paid to energy conservation.

While there are numerous initiatives to conserve energy, one of the more promising approaches of achieving energy efficiency is a suite of intelligent technologies held under the umbrella of a Smart Grid. The Smart Grid relies on intelligent systems to make real-time decisions that can save energy without inconveniencing the consumer. Making the smart grid successful will require a creative and multidisciplinary approach from areas such as power and systems engineering, security, business intelligence, social networking, mathematical research, and others. (He, 2010)

In 1940, 10% of the energy consumption in the US was used to generate electricity; in 2003 it was 40% (US DoE, 2003), and in 2012 the level still remains around 40%, according to the DoE website (http://www.eia.gov). The largest man-made contributing factor that harms the environment is the energy production processes that use CO2 emissions (Jiang et al., 2009). Among this and other factors, we are facing global warming, which is expected to increase 5 degrees in global temperature by the end of the 21st century, which has not occurred in 70 million years (Klimenko et al., 2009).

For the past 25 years the construction of transmission facilities in the United States has decreased as energy demand increased, resulting in grid congestion. To prevent this situation, the

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President Bush’s signing of the project “Grid 2030” in 2003 (US DoE, 2003).

Smart grids seem to be the future for energy conservation, as they are expected to save 10% of the energy used in the US by focusing on providing the required amount at the right time. The smart grid concept also includes other technologies, including:

 Demand Response (DR) to manage consumption responding to supply conditions  Electric Vehicle (EV)/ Plug-in hybrid electric vehicle (PHEV)

 Distribution Automation (DA) intelligent control over electrical power grid distribution level

 Community Energy Storage (CES) presenting an alternative to store energy at suburbs  Advanced Metering Infrastructure (AMI) systems that collect and analyze energy

consumption data. The nomenclature of smart meters was included in this category as it is the name used at Europe.

 Distributed Storage (DS) as a smart way to reserve the available energy

 Distributed Generation (DG) looking for a better way to generate decentralized electricity.

Although Smart Grids are very popular, there are still some unanswered questions about future uses, as its strengths and weaknesses are not recognized because their anticipated benefits have not been fully received yet. The purpose of this chapter is twofold:

 Clearly define what is expected from the use of smart grids; this can be accomplished by conducting a SWOT analysis using the available information; and

 Investigate whether the multiple technologies under the umbrella of the smart grid concept are being accepted and promoted worldwide—in what jurisdictions and under

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The first part of the chapter will research definitions and develop a SWOT model. The second part of the paper uses a bibliographic analysis to identify mentions of smart grids in worldwide literature, applying the Management Fashion Theory (Abrahamson, 1991).

2.2 THEORETICAL BACKGROUND

2.2.1 Smart Grid Concept Defined

The largest machine with multiple interconnections in the world is the US power grid, which includes over 9,200 generators and 300,000 miles of transmission lines. The US power grid generates more than 1,000,000 megawatts (He, 2010). Modernizing and further developing this huge machine will require a clear focus on the goals of the project.

To better understand SG, we define the concept as “an approach to modernize electrical distribution that would transform the way that a utility interacted with its customers in order to provide a higher level of service and reliability, put the customer in control of their energy costs, and to achieve energy conservation and sustainability goals” (Sarfi et al., 2010, p.200).

After conducting thorough research, we can define Smart grids as efficient ways to conserve energy and prevent waste; they ought to be accommodating, as the future of energy might not be based on hydrocarbons but other sources of energy. Motivating users to do energy required activities at the proper time can be accomplished with smart grids. SG’s shall be quality focused to do things correctly every time. The opportunistic concept of the vision means that it will take advantage of any opportunity that might arise and integrate it as a plug and play. The resilient requirement of the model is critical, as the smart grid shall be prepared to resist any cyber-attack. Green is the name given to any environmental activity and the vision of a smart

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indeed the toughest requirement, as it is expected that the grid shall have enough information and programming that it would react smartly to any behavior (He, 2010)

The architecture of a smart grid is very important but its decision making mechanisms are equally critical for they:

• Shall be flexible to accommodate needs of different utilities,

• Shall extend to the ever changing requirements,

• Shall be open to interoperate with other different providers,

• Shall handle and degrade faulty conditions such as noisy data (Davidson et al., 2010).

Less essential, but certainly desirable, are extended capabilities such as utilizing all available data within a utility to influence operation, and allowing utilities themselves to select the level of automation for a given situation or scenario.

The areas of application of smart grids include: smart meters integration, demand management, smart integration of generated energy, administration of storage and renewable resources, using systems that continuously provide and use data from an energy network (Davidson et al., 2010)

2.2.2 SWOT Analysis

 Strengths:

Self-healing systems are desirable to prevent dependence on human intervention at critical moments; by providing the systems with enough data, they can make smart decisions at the right moment: artificial intelligence (AI).

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errors in communication, the smart grid will utilize a digital platform (Jiang et al., 2009)

Demand and load management are critical parts of the concept, as they helps to optimize delivery and consumption by reducing customer demands at peak hours (Liu, 2010)

The smart grid shall not have a central vulnerable system that could deactivate the whole network using decentralized control schemes (Jiang et al., 2009)

One of SG’s most important features is that it can be customized to specific needs/wants (Jiang et al., 2009)

The future of hydrocarbon resources is looking weaker, Therefore, some generations have to consider the integration of intermittent renewable resources, such as wind, solar, etc. (Liu, 2009) Smart sub-stations ought to be autonomous and have enough information and data to optimize their continuous operation (Jiang et al., 2009)

Another important feature of SG is that, due to the system transparency, we are able to see what is happening at all times in real-time (Liu, 2009)

 Weaknesses:

Cyber security anticipates compromises of adjacent systems. This has been a major concern area addressed by IT under SG (Overman & Sackman, 2010)

A smart grid contains so many sensors and devices that it increases the system complexity for maintenance and repairs (Overman & Sackman, 2010)

There could be failures in communications link, sensor and/or actuator, unplanned control center system failure, and nonexistent, late, or improper commands by untrained and/or distracted control room personnel (Overman & Sackman, 2010)

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 Opportunities:

Cyber security controls will become more critical in future systems (Overman & Sackman, 2010) Balancing demand and generation using SGD can achieve optimal flow (Davidson et al, 2010) An information security active defense model will not only protect but also defend the system from attacks and unexpected responses (Zhang, 2010)

SG can have decentralized storage areas to achieve the desired system balance (Slootweg, 2009)  Threats:

Communication channels in the future may be more dedicated (Jiang et al, 2009), creating a need for dedicated conduits for SG, affecting cost and reliability of the system.

Due to the complexity of grid, it might not be easy to provide technical support from a single source (Bull, 2010)

As web applications are preferred targets of hackers (Bull, 2010), the SG might be attacked until its vulnerabilities are found.

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2.2.3 Hypotheses H1

In this global environment that we live in, we are seeing that some concepts related to conservation and better utilization of energy are in vogue. We expect that worldwide literature should also reflect this emphasis on Smart Grid Distribution (SGD), as this is one of the goals of this dissertation to achieve. Therefore, we present our first hypothesis which claims that there shall be an upward trend in scholarly literature about this subject.

H1a: Smart Grids research shows growth of published papers in global literature.

There is a possibility of SG becoming just a fad. To understand if it is indeed a fad or if it is becoming a fashion or a concept that will persist for a long time we will use the methodology used by Ponzi and Koening to analyze the lifecycle and diffusion of new concepts. Specifically, we will utilize bibliometric techniques (Ponzi & Koening, 2002). The lifecycle of a fad is shown as a quickly increasing concept in popularity that peaks and disappears very quickly, while a fashion grows more slowly, matures and stays atop for a while and begins to come down slowly (Abrahamson, 1991). It is our expectation that SG has not reached the maturity to begin declining, so we propose our second hypothesis:

H1b: Smart Grid Distribution has not reached the maturity stage of its global lifecycle.

As presented in the paper’s introduction, there are many different technologies that are correlated to the main concept of SG. Entities are pushing technologies according to their strategic plans and needs; therefore, if they have a different technological need, their definition and interpretation of SG is going to vary as well. Thus our third hypothesis proposes that:

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As we conduct bibliometric studies, we shall propose a null hypothesis that the number of articles mentioning every technology is going to be equal for all countries and states. So our final hypothesis claims that:

H1d: All technologies under the SG umbrella are equally mentioned in the literature 2.3 METHODOLOGY FOR TAXONOMY RESEARCH

We counted the number of articles containing the words “smart grid”, eliminating those papers with citations only to ensure the selected articles are referring to the concept and are not only references. The research was conducted using Google Scholar for all papers published from January 1st. 2001 until December 31st. 2010. Once the information was retrieved, we developed charts with the annual counts of articles over this period on a yearly basis to see if the shape of the chart shows a fad, a fashion or a growing concept and so support hypotheses 1a and 1b.

In order to correlate the main areas of study of SG, we only considered the following disciplines: engineering, business, physics, chemistry, environmental and social sciences. It is our expectation that the engineering, physics and chemistry study areas represent the technical aspect behind SG, while the business, environmental and social sciences provide the planning aspect—as they refer to goals, strategies, and non-technical implementation plans.

Technologies under the concept of SG are also separated to support hypotheses 1c and 1d, because these technologies will show if there is a different perception in regards to SG at the different jurisdictions. We used only some of the technologies, DA, DR, EV/PHEV, AMI along with smart meters, DS, etc. We will finally probe countries and even states to correlate technology to every jurisdiction’s perception.

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2.3.1 Google Scholar Research Results

Researching Google Scholar for worldwide articles containing SG and the above mentioned technologies, we were able to find 5,125 articles written in the ten year period. The trend chart shows exponential growth that can be better seen using a log scale— as shown in Figure 2.2. The number of articles written about “smart grids” has been increasing exponentially, and there are no signs of decrement. Based upon these findings we can support both hypothesis 1a and 1b, as we see a rapid growth of worldwide articles without showing signs of stagnation or decrement that may signal the “smart grid” concept being a fad. The trend is still going up or it is about to reach its maturity level before stabilizing and then going down. To better understand the study areas that are promoting this growth, we categorized of articles based on their area of specialization.

Figure 2.2: Log trend line of SG articles

The area with the highest number of mentions is engineering, as the know-how about technical achievement of SG is being developed. Far in second place is the business area which

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Figure 2.3: Selected articles by categories

To have a better view of the growth, and determine if the technologies are beginning to slow down and become stagnant, we calculated the percentage increments on a yearly basis per category. The chart shows that the social sciences, environmental and business areas are growing at slower rates than the other sciences. This might be a sign of them getting closer to reaching maturity. In other words, we might say that the foundation for SG in the social, environmental and business areas has been set and the maturity phase will begin with smaller growth and even decrease in the upcoming years. This can be seen in Figure 2.4, as social and environmental sciences are decreasing in growth. The previous charts support hypothesis 1a and 1b in that the overall trend in worldwide literature is still growing. The SG concept has not reached maturity, as there are no signs of stagnation or decrements in growth. In the business, environmental and social sciences, the trend seems to be slowing down and even reaching maturity, but this is still to be shown. On the other hand, engineering, physics and chemistry disciplines are still growing rapidly in literature about the concept and details on how to build and improve SG.

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Figure 2.4: Growth of SG by discipline

Figure 2.5: Time series for SG technologies (Cardenas et al., 2011)

Focusing on the technologies under the SG umbrella, we did the time study shown in Figure 2.5. This study introduces distribution automation as the first technology that was

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technologies are showing important increments in the past years, and none of them are giving signs of slowing down yet. The lines are showing exponential growth in most instances, such that we can conclude, looking at Figure 2.5, that Demand Response (DR) is the term more commonly associated with SG in the reviewed literature. This is not peculiar, as the smartness concept is linked to responding intelligently to the demand and supply energy levels. The 2nd technology in number of mentions was AMI, better known as smart metering, which has been highly accelerated in the past 2 years.

Figure 2.6: SG technologies per country

We asked ourselves if the SG concept, technologies and trends are the same all around the world; so we took literature and data mined the definition concepts from Figure 2.5 and related them to the countries that are implementing it. The results show that the United States is number one in SG mentions in literature. The second place is almost a tie between the United

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metering while China’s main interest revolves around electric vehicles. Germany and Canada are close in fourth and fifth place with smart metering as their key interest after DR. Many other important points can be noted by looking at Figure 2.6.

We support hypothesis 1c and reject hypothesis 1d because we can see that every country has a different perception of what SG is, and not all the concepts are mentioned equally. Because the country with the most articles related to SG is the United States, we further researched and subdivided the papers into the different technologies under the SG umbrella per state. There have been some successful implementations of SG technologies at some states, so data mining by states we found some interesting facts shown in Figure 2.7. California and Pennsylvania are the leaders in regards to technical papers about smart grid implementation, followed closely by Texas and Illinois. It is interesting to note that New Jersey is paying more attention to smart metering than demand response as all other states do.

In the US, we can also support hypothesis 1c for every state’s perception in regard to SG, which are different and their levels are not equal—thus rejecting hypothesis 1d. The technologies mentioned for the US and the world follow the same path: demand response is number one at the selected countries and even states in America. Second and third place are also the same for smart metering and electric vehicles. For a better idea on the perceptions at every state, we normalized the results and calculated the percentage of written articles per state and technology to confirm the interest of every state in regard to the SG technologies. The results are shown in Figure 2.8.

For demand response, the average percentage of mentions was 36%, with California and New Jersey being the least interested in this area with 23 and 22% of articles—although their interest was biased toward smart metering. Smart metering and electric vehicles are tied in

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receiving an important push from Colorado while smart meters have spread support throughout the states. On distribution generation Texas and Florida are the least interested states while New Jersey is the one showing the most interest.

Figure 2.7: SG technologies by state

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2.3.2 Preliminary Conclusions of Google Research

Based on the research conducted using Google Scholar, in the period from 2001 to 2010, we can conclude that the SG concept has been growing rapidly around the world. The concept has not reached the expected maturity level, but because the bibliometric study shows that SG has experienced growth in the past but is beginning to slow down now, we doubt that it is a fad.

Until the number of SG papers stabilizes or decreases in number, we might be able to conclude if it is a fashion, as we expect the number of articles to reduce slowly until it goes to a minimal level; a fad would grow quickly, reach its peak, and then diminish rapidly. Thus we do not expect SG to be a fad (Cardenas et al., 2011). Dissecting the SG concept into the different technologies related to the SG concept, we are able to see that the individual components are also growing and none of them has reached the maturity stage yet; although the growth has taken over 7 years, so we do not expect them to be fads either.

With the business, social science and environmental disciplines showing increases of around 100% in the past year, we can infer that these study areas seem to be slowing down compared to the engineering, physics and chemistry areas with yearly growths from 271 to 715%.

Worldwide, we found that demand response is the concept more closely related to SG followed very closely by smart meters and electric vehicles. This is surprising given that distribution was expected to be more related than electric vehicles, showing a global perspective of energy conservation, enhanced distribution, optimized generation, and intelligent consumption breaking away from the current energy schemes towards a greener environment and smarter

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Table 2.1: Results of Hypotheses H1

2.4 LITERATURE SURVEY USING ISIWEB OF SCIENCE

In order to use a more rigorous method for paper publication, we analyze and count the peer-reviewed articles published containing the words: “Smart Grid” & “Distribution”. Two different publishing sources were used: conference and journal papers. This research was conducted using the ISI Web of Science for serious academic peer-reviewed papers published from January 1st. 2008 until December 31st. 2013. It is important to emphasize that the first mention of SG happened in 2008, for that reason we are analyzing up to 2013, to include more than five years of information, an earlier version of this literature review was published by the Journal of Cleaner Production (Cardenas et al., 2014). In a period of one year, from the time when the original paper was written to the time of the conclusion of this dissertation, over 150 papers were added to ISI listing. Therefore, we expect the same to happen in 2013, thus we are not worried about the lower number of conference papers in the past year.

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2.4.1 Hypotheses H2

While the field holds many publications, in this section we are going to focus exclusively on peer-reviewed papers. We expect that the behavior of academic reviewed articles is going to be the same as the more general Internet listing site. Thus, we develop the following hypothesis:

H2a: Peer-reviewed conference and journal papers about SG are still showing growth.

Smart Grid literature has been mostly related to engineering, specifically focusing on its technical aspects. It is our expectation that most serious literature will be devoted to developing theories for its Smart Grid implementation. Because SG is such a new concept, new technologies and new operating philosophies have to be developed to achieve the expected results (Klein et al. 2011). Thus, through the peer-review literature, we will analyze if the technology has matured enough to be at the theory building, testing or implementation stage.

H2b: Smart Grid Distribution literature is more focused on theory building and testing than empirical implementation.

If the diffusion of Smart Grid Distribution literature is reaching at least the early majority, there is going to be enough examples from case or field studies to be analyzed in the articles and develop prognoses for future implementations. As technology evolves, the need for accurate operating information becomes paramount (Bank, 2012.) Due to the newness of SGD, it is our expectation that there are more case studies than sources of data at this time:

H2c: The majority of peer-reviewed papers collect data from case studies rather than other research sources.

In the early 70’s Akao and others developed the Quality Function Deployment (Chan and Wu, 2002) This technique presents a sequence for planning the product or service all the way to

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implementation process, while control will be the last part. Because SGD is a new technology, we expect most papers to be related to strategy instead of process controls, or quality focus. The next hypothesis proposes that:

H2d: Strategic papers have a leadership role over quality focus on efficiency and control.

With the “green revolution” push in the new millennium and an increase on energy demand, the new energy has to be generated from renewable wind, solar and tidal resources (Ramchurn et al., 2012). If renewable energy is correlated to the SG concept, we expect that the generation of energy using renewable resources will be correlated to the number of papers written on SGD. The name for the renewable generation of energy is coined under distributed generation (DG) or distributed energy resources (DER). Although the necessary investment is high at first, the benefits are being studied throughout the world, such that we can expect that:

H2e: Distributed Generation is steadily growing in importance throughout the world.

The United States has been the leader on some technologies in the past. Recently, the US has been the creator of programs such as Intelli-Grid, Gridwise, Modern Grid Initiative, and Smart 2030, which have been propelled by the stimulus plan (ARRA). With this investment in Smart Grids, we expect the US to be the leader in the generation of literature focusing on planning and strategies for the future.

H2f: The US is the leader in peer-reviewed SGD literature

Considering the country of origin of the writers, it is our expectation that most papers for both conferences and journals would be from the US, but what about the economic blocks? Is America a leader in peer-reviewed conference and journal papers when compared against the European or Asian blocks? Because America is very much represented by the US and Canada,

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we expect that the leadership position might be challenged by the European Union or the Tigers of Asia, but America may still be the leader.

H2g: America is the leader on SG conference papers in the world H2h: America is the leader on SG journal papers in the world

Conference papers are preliminary journal papers in nature, so we expect that the number of conference papers will predict the number of journal papers in that subject matter. Because the number of peer-reviewed conference papers is much higher than journal papers, that relationship might be linear:

H2i: Journal papers follow a linear regression to the number of conference papers in the SG subject.

2.4.2 Research Purposes

Following the method used by Cardenas et al. (2014) and Gupta et al. (2006) the selected published papers are also classified into the three categories of research: theory building, theory verifying, and theory application. Theory building is going to include the published papers that present new theories or formulate existing ones; theory verifying include the research that prove previously presented theory, while theory application are practical papers that present how the technologies are implemented in the field.

In the category of theory-building authors develop new relationships, algorithms, or hypotheses to be confirmed in future research papers (Kleinberg et al., 2009). Although this literature survey is written from the social science perspective of Information and Decision Sciences, these papers are required to be conceptual and also contain empirical data to support the author(s)’ hypotheses. For example, Tom Jauch (2009) proposes the coordination of

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automatically adjust to changing loads, circuit changes, reverse power applications, and circuit switching and reconstruction. Sanz et al (2009) also researched the introduction of electronic circuitry in the control of distribution to optimize the process. More recently, Dukpa and Venkatesh (2010) proposed a new model for distribution systems using fuzzy charts.

For theory-verification, the authors conduct tests to prove those hypotheses previously proposed in earlier research (Saleem et al., 2009; Vokony and Dan, 2009). For examples, Tenti et al. (2010) introduced the token ring approach to minimize the lost energy where a voltage control algorithm sets a progressive reduction of the voltage at the other end down to zero. Moreover, Li et al. (2008) examined the system configuration and control methods at the typical radial distribution network, where locations of DEs are affecting the grouping of the buses; therefore, they affect the reduced network structure and the appropriate range of the controller parameter.

The most commonly observed type of empirical research for Smart Grid Distribution is the application of existing theories, models, or frameworks. For example, Bushby (2009) focused on the Building Automation and Control Networks (BACnet) to provide a standard, network visible interface to configure and manage facility load shedding operations. Son et al. (2009) applied the principles of Smart Grid in Korea under the name K-grid established there since 2004; and Kirkham (2009) presented research using four different innovative methods to measure current efforts to address the issues of transduction and isolation from the circuits measured.

Smart Grid Distribution (SGD) is a recent development, the focus in the past years was on energy transmission, and hence there are not many available sources for data collection. However, many of the papers on SGD refer to simulations and pilot runs before implementation. These simulations and pilot runs could help prevent catastrophic events if the proposed models

smart grid

SMART GRIDS: A PARADIGM SHIFT ON ENERGY GENERATION AND

DISTRIBUTION WITH THE EMERGENCE OF A NEW ENERGY

MANAGEMENT BUSINESS MODEL

SMART GRIDS: A PARADIGM SHIFT ON ENERGY GENERATION AND

DISTRIBUTION WITH THE EMERGENCE OF A NEW ENERGY

MANAGEMENT BUSINESS MODEL

ABSTRACT

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

CHAPTER 1: INTRODUCTION



CHAPTER 2: COMPREHENSIVE RESEARCH IN SMART GRID