EVOLVING COMPETITIVE ELECTRICITY MARKETS: …iaeme.com/MasterAdmin/uploadfolder/IJCIET_08_09_015/...Nitin Desai specifies (Pradeep S Mehta, 2007) that the competition is the one that

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International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 9, September 2017, pp. 116–134, Article ID: IJCIET_08_09_015 Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=8&IType=9 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 © IAEME Publication Scopus Indexed

EVOLVING COMPETITIVE ELECTRICITY

MARKETS: A STUDY OF ENABLEMENT

THROUGH TECHNOLOGY DEVELOPMENTS

Jayaprakash Ponraj

Research Scholar, Management Studies, VELS University, Chennai, India

Dr. A. Chandra Mohan

Registrar, Rajiv Gandhi National Institute of Youth Development, Sriperumpudur, India

ABSTRACT

Background / Objectives: This paper discusses an approach to achieve the

autonomous market objective through the effective use advanced analytics and

cognitive solutions powered by internet of things and mobility. This paper includes

details on conducted survey on electricity consumers in Tamil Nadu, India along with

their inference on status of impact of technology towards progress of competitive

electricity markets.

Methods: Worldwide landscape of electricity industry such as business, grid and

asset management alongside customer experience and market models are transforming

quite rapidly towards achievement of autonomous electricity markets. Governments,

their regulatory bodies and utility organizations are focusing on establishing new

business models and thus help stage-manage their people, organization and its

governance structure towards achievement of their objectives. The fact that there are

enormous differences in the outcome achieved by worldwide utilities is explained

through this study of internal and external factors influencing the progress of movement

to autonomous state.

Findings: This study of global utilities brings out three key internal influences

depictions such as people, organization behaviors as well as key technology readiness.

In this paper various influences such as shrinking grid businesses due to various

challenges such as increasing micro grids or distributed generation resources,

regulatory pressures on cost reduction with the ageing infrastructure and diminishing

skills are discussed. Specific focus has been given on grid organizations with the

emphasis to prevent them from death spiral due to shrinking grid businesses. This paper

explains why these technologies are imperatives to energy and utility organizations and

how they will empower every stakeholder with digital experience and equal opportunity

to perform their businesses while in the challenging environment.

Applications / Improvements: This paper does not include discussion around other

influences such as people, organization & social behaviors, traditional applications and

Jayaprakash Ponraj and Dr. A. Chandra Mohan


solutions that help manage the business as usual. Hence it is proposed to continue the

research work to cover the detailed aspects and publish in the further proceedings.

Keywords: electricity markets, cognitive solutions, advanced analytics, technology influences, business model.

JEL Classification: O33, D40, F15, F63, M00

Cite this Article: Jayaprakash Ponraj and Dr. A. Chandra Mohan and Evolving Competitive Electricity Markets: A Study of Enablement through Technology Developments, International Journal of Civil Engineering and Technology, 8(9), 2017, pp. 116–134. http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=9

1. INTRODUCTION

Worldwide landscape of electricity industry (Kenneth, 2008) such as business, grid (transmission and distribution power network) and asset management alongside customer experience and market models (Yamzaki, 2015) are quite rapidly towards achievement of autonomous electricity markets and the state is beyond competitiveness. This study focuses on establishing an approach and requirements to achieve the autonomous market objective through the effective use advanced analytics and cognitive solutions powered by internet of things and mobility and are discussed in the following sections. It also provide perspective of why these technologies are imperatives to the industry organizations in order to tackle their challenges and how they will empower every stakeholders with digital experience and equal opportunity to perform their businesses while in the challenging environment is discussed. A study was conducted on electricity consumers of various categories such as residential, commercial and industrial in Tamil Nadu to deliberate on the areas of focus that support the said transformation objectives. This paper includes the analysis and interpretation from the study and its linkage to these discussions. This paper does not include discussion around other influences such as people, legal & regulatory framework, organization & social behaviors, prevailing applications, standards and solutions that help manage the business as usual.

2. STUDY OF MATURITY TOWARDS COMPETITIVE MARKETS

Competition, the process of challenge between organizations striving to gain sales and make profits, is the driving force behind markets. Former Undersecretary General of United Nations, Nitin Desai specifies (Pradeep S Mehta, 2007) that the competition is the one that promotes efficiency and accountability, ensures access for the citizen-consumer and widens his / her choices. Pradeep discussed elaborately about prospectus and challenges of electricity and telecommunication markets in India. He also focused mainly on existing state of infrastructure and provides a way forward in building the competitive electricity markets in India. He also stressed the need for detailed methodological study focusing on electricity competitive markets due to its inherent slow path of transformation. While comparing competitiveness and progress of telecommunication industry with electricity, the report termed the progress as failure and describes various aspects to focus on success path.

In order to bring out the focus on approach to realize end objective of establishment of competitive markets and enabling autonomy in the electricity utility segment, worldwide various studies in combination with practices are being evaluated and trialed. This study includes review of publications and reports (Ria Langheim, June 2014, Vol. 27, Issue 5) from various entities such as governments, regulators, utility organizations viz., generation including renewable, trading segment and on natural monopolies such as transmission and distribution

Evolving Competitive Electricity Markets: A Study of Enablement through Technology Developments


grids. The Figure 1 represents various steps taken by worldwide utilities to realize end maturity of competitive benefits.

Figure 1 Evolving maturity state of Competitive Electricity Markets

2.1. Autonomous Markets

The term autonomous market is used in context of describing maturity level of electricity utility markets. At this state of maturity, regulatory environment would allow stakeholder organizations to define and run new business opportunities or models which are not conceding competitive environment themselves. Most utilities move towards achieving the autonomous state through various initiatives challenging the internal and external influences. One of the key characteristics which would allow existence of this environment is empowerment of every stakeholder organizations with digital experience to perform their role with economy. And it also should ensure allowance of disruptive innovation to be introduced, verified and validated through existing knowledge and information. Stimulating dynamic peripheral system in which a business competes is the key consternation for most countries to promote economic growth & development. In this environment more stakeholders (sellers of a similar product or service) competes, enjoy economic viability and does not get castigated due to missing data to make informed decisions to perform business.

2.2. Study of Internal and External Influences

Detailed study of global utilities on internal and external influences challenging movement towards autonomous market reveals that utilities have attained divergent level of maturity and experiences on their journey towards achieving autonomous markets. The following are some of key current trends such as unbundling or organizational alignment, exponential inclusion of renewable such as photo voltaic, wind energy in the grid, innovative new business models to include energy storage, increasing regulatory pressures vs. ageing infrastructure, diminishing resource skills, reducing oil & gas prices across the globe, liberalization of import restrictions etc., are posing major threat to utilities sustenance influencing the current status of the maturity. Some of the key trends are explained in the below section with some illustrations.

2.2.1. Rapidly Growing Micro-grids and Distributed Generation

The represents falling cost of solar PV generated electricity plotted for about three decades by Citibank in 2013. Renewable generation (Wiki, Growth of Photovoltaics, 2016) such as Solar Photovoltaic (PV), offshore wind plants are conquering exponential momentum generally due to attractive benefits such as declining cost per energy produced, enhanced plant load factors etc., In many countries PV prices have crossed grid parity even from retail customer perspective and thus set to go into competitive market space without any subsidies. However as these distributed generation have intermittency behavior, utilities need to look for satisfying major



obstacles to expanding consumption such as ramp events, spinning reserve etc., The variability of distributed generation spots a challenge to those who want to turn them into a profitable alternative energy sources. Hence it is obvious that better forecasting and optimized dispatch can alleviate these barriers. For case in point in India, weighted average bid price for solar PV were drastically reduced from INR 12.36 per kWh in 2010 to INR 5.36 per kWh now. Market insights including recent revised report from IEA on solar PVs suggests greater reduction in costs about 2cents per kWh. In many countries solar PV prices have crossed grid parity (Wiki, Grid Parity, 2016) even from retail customer perspective and thus set to go into competitive market space without any subsidies. This helps developing nations to declare multifold upsurge of renewable targets. For example recently Indian Leaders in energy sector envisioned target of about 1, 75,000 MW of solar PV capacity growth in 2022. To give an energy scale perspective Indian power grid met peak demand of 1, 41,180 MW which includes power from grid connected solar PV installed capacity of during the year 2014.

Figure 2 Falling cost of solar PV generated electricity

Other global examples include, E-ON (E.ON., 2016) which is privately-owned energy supplier implementing its new strategy, of focusing entirely on renewable, energy networks and customer solutions. The conventional generation, global energy trading and exploration and production businesses segments are being transferred to a new company. E-ON’s energy networks represent one of the three core businesses of its strategy; already deliver lots of green electricity to customers across Germany. E.ON networks in Germany are home to a total of 32 Giga watts (GW) of renewable capacity, about as much as 100 large offshore wind farms.

2.2.2. Robust Advent of Energy Storage Technologies

Concept of zero energy buildings or organizations was endured, with the robust advent of energy storage technologies and in fact many consumers are looking beyond towards powering the grid thanks to encouragement offered by utilities. In developing countries where challenge for high power network reliability is in existence, typically residential & commercial consumers depend on batteries as energy storage mechanisms. In fact distributed generation combined with energy storage has been proved more dependable in many countries. However more accurate demand and supply predictions are essential to manage demand vs. supply and ensure energy storage business is sustainable. The Indicates the costs of batteries are dropping in last decade. For case initiatives by North America utilities (such as San Diego Gas & Electric - SDGE) are encouraging customer owned batteries (Buy Your Own Battery - BYOB) to encounter other challenges such as peak demand can be referred. In Australia Reposit Power integrates solar, storage, and controllable loads into a single energy system (Luke, 2016).

Figure 3 Declining Battery Costs in $/kWh



2.2.3. Exponential Upsurge of Data

Globally utilities have been implementing various solutions in order to manage their business effectively. The sourced from Electric Power Research Institute, Greentech Media research (John, 2013) indicates the annual rate of data intake in Terabytes would increase exponentially. And these increases further steep when futuristic First of a Kind solution such as autonomous protection (including Wide Area Monitoring Solutions (WAMS) for transmission, Wide Area Control Solutions (WACS) such as setting less protection for distribution) are implemented. Typically Phasor Measurement Units (PMU) is sampled at a rate of 48 samples per second. In such cases considering large number of PMUs on a distribution system to perform use cases like setting less protection, huge exponential data upsurge would occur. And the study validated that many of innovating utilities are already on path of selecting, trailing and implementing appropriate technologies in place to manage the business, grid and assets better. For example, Power Grid Corporation in India is performing trials on Wide Area Measurement Solutions since 2008. Comparison can be had with reference to the existing level of data rate in many utilities. For example, energy consumption units stored once in two months vs. data rate @48-144 samples per day to implement advanced demand response solution and manage peak demand. Another key example includes data reads from PMU @48 – 96 samples per sec depending the application and latency of features.

Figure 4 Exponential Data Growth for Utilities Organizations

2.3. Transformation towards Autonomous Markets

In many countries grid business is regulated non-competitive segment due to its inherent nature. Regulators are playing major role in perceiving policies, tariff structure. Study reveals that in order to achieve the goals of autonomous markets, focus of Governments and their regulatory bodies were to identify, establish new services, and business models in order to sustain or improve their revenues and to bring in more system and operational efficiency. This involves stage-managing of their people, legal & regulatory framework, organization & social behaviors and inclusion of appropriate technologies and solutions towards achievement of their autonomous market objectives. The Table 1 represents high level challenges or factors under



three broad categories such as people, framework and technology. In the following sections various influences such as shrinking grid businesses, ageing infrastructure & increasing regulatory pressures, etc., are explained. Specific focus has been given on grid organizations with the emphasis to prevent them from death spiral due to shrinking grid businesses.

People Organization Technology

Awareness Government Policies & Legal

Framework Energy & Power System Infrastructure

Consumer Interest Organizational design Operation Technology

Supplier Motivation Benefits & Threats Information Technology to manage

Skills & Training Influences a) Enterprise

Advertisement & Promotion b) Asset & Grid

Performance Metrics c) Markets

d) Customers

Table 1 Prospects and Challenges for Establishment of Competitive Electricity Markets

2.3.1. Shrinking Grid Business

Review of the influences discussed in the above sections reveals that due to increased dependence of renewable and energy storages, Prosumer or consumers have falling dependency on grid and thus energy transferred through the grid are likely to shrink. Due to diminishing nature of energy transferred through the grid utilities expects strong shift in price ratios towards providing reliable backup power source or towards high availability for ensuring selling of excess energy to the grid. This means expectations of consumers on grid companies is likely to increase and metrics based (for example, availability based) open access pricing is foreseeable in future. It would require analyzing grid availability or power quality at more frequent intervals and applying dynamic metrics based tariffs than practiced now. Also the tough regulatory pressures along with trend of journey towards zero energy structures and micro grids would soar larger challenge to sustain grid companies’ business.

2.3.2. Ageing Infrastructure & Increasing Regulatory Pressures

Utilities struggle to cope up with huge reduction in regulated revenues while there is a huge investments allocated towards renewable. Also with cost reduction as major objective, regulators enforce grid operators to identify other opportunities for bringing down capital investments and operational expenditures (CAPEX & OPEX). In addition to this utilities are also pushed towards investing on renewable. This means existing preventive maintenance practices will not scale up and hence utilities need to look for practices like deferred network augmentation and however are compelled to maintain existing service levels. It becomes imperative for utilities to apply advanced asset performance innovations to establish stringent year-on-year cost reduction targets.

2.3.3. Ageing and Diminishing Skilled Manpower

The Depicts Center for Energy Development’s Survey results. It clearly indicates that rate at which highly skilled resources will diminish is high, which means as a whole, almost 55% of the workforce may need to be replaced in the next 10 years. And it is imperative that utilities need to engage and act faster to meet this challenge to avoid knowledge attrition. For illustration survey in Japan suggests that as of 2013 a quarter of the nation’s population was age 65 or older (CEWD, 2013). This demographic (Jonathan, 2015) is projected to expand to 36 percent by 2040, and will reach 40 percent by 2060. This means government and or regulators needs to



plan to tackle this critical scenario to maintain metrics such as grid reliability, system & operational efficiency, safety etc.

Figure 5 Diminishing Skilled Manpower: A Case with Japan Utilities

2.3.4. Enablers of Competitive Environment

It should be noted that the transformation is only be possible subsequent to empowerment of each stakeholders with appropriate information that will help them make informed decisions to perform their business with economy as prime focus. Thus government has key imperative to ensure that none of the stakeholders are vulnerable to economic non-viability prior to unbundling due to unavailability of competitive environment especially lack of governed timely & right perspective information. This transformation involves series of actions by utilities under various domain such as people, legal & regulatory framework, social & organization behavior and technology are picked up to achieve the required maturity (The Climate Group, 2008). In this paper specific focus has been given related to few key technologies to empower stakeholders with digital experience in the following sections.

2.4. Survey Conducted on Electricity Consumers and Suppliers

A survey was conducted on electricity suppliers and consumers. Respondent profile of consumers was grouped as Consumer Categories viz., Residential, Commercial and Industrial. Similarly this was also grouped into various Organization Categories viz., Individuals, Private and Public. The following questions related to Infrastructure & Technology were raised.

1. Energy & Power System (EPS) Infrastructure

a) Do you consider that the existing power system infrastructure is sufficient to support the requirements of open access?

b) Do you consider that the infrastructure to manage distributed generation is sufficient to support the requirements of open access?

c) Do you consider that the existing energy storage infrastructure is sufficient to support the requirements of open access?

d) Do you consider that the existing demand response infrastructure is sufficient to support the requirements of open access?

e) Do you consider that there is not much need for major investment to improve existing Energy & Power System Infrastructure to meet open access requirements?

f) Do you consider that step by step investment in Energy & Power System Infrastructure could help meeting the open access targets?

2. Information Technology (IT)

a) Do you consider that the existing Information Technology infrastructure and applications is sufficient to support the open access transformation?



b) Do you consider that there is not much need for major investment to improve existing IT infrastructure and applications that enable open access requirements?

c) Do you think that step by step investment in IT infrastructure and applications could help meeting the target transformation?

3. Operation Technology (OT)

a) Do you consider that the existing Operation Technology infrastructure and applications is sufficient to support the pre-requisite of open access?

b) Do you consider that there is not much need for major investment to improve existing Operation Technology infrastructure and applications to meet open access requirements?

c) Do you think that that step by step investment in Operation Technology infrastructure and applications could help meeting the target transformation?

4. Market Systems (MS)

a) Do you consider that existing Market operation and management systems is sufficient to support the pre-requisite of open access?

b) Do you consider that there is not much need for major investment to improve existing Market Systems infrastructure and applications to meet open access requirements?

c) Do you consider that step by step investment in Market and enterprise systems and applications could help meeting the targets?

5. Customer Systems (CS)

a) Do you consider that electricity suppliers are doing well to manage customer acquisition and retention?

b) Do you consider that there is not much need for major investment to improve existing Customer Systems and applications to meet open access requirements?

c) Do you consider that step by step investment in Customer Systems and applications could help meeting the targets?

3. RESULTS AND DISCUSSION

This section divided into two subsections. First subsection covers detailed interpretation from survey results and the latter includes how advanced analytics and cognitive solutions enable achieving the desired outcome.

3.1. Enablement Solutions Areas - Interpretation through Survey

This subsection focuses on the analysis of the survey results conducted on consumers related to Information & Technology factors. The detailed summary results are expected to be presented in future articles.

3.1.1. Tests on Consumer Category and Infrastructure & Technology Factors

MANOVA is used to explore taking Consumer Category as independent variable and Infrastructure & Technology factors like EPS Infrastructure, IT, OT, MS and CS as dependent variables to find the interactions among the dependent variable and also among independent



variable. Table 2 represents Multivariate test on Consumer Category and Infrastructure and Technology factors to determine significance of the following hypothesis.

Table 2 Multivariate Tests on Consumer Category and Infrastructure & Technology Factors

Effect Value F Hypothesis df Error df Sig.

Intercept

Pillai's Trace .990 7565.068b 4.000 318.000 .000

Wilks' Lambda .010 7565.068b 4.000 318.000 .000

Hotelling's Trace 95.158 7565.068b 4.000 318.000 .000

Roy's Largest Root 95.158 7565.068b 4.000 318.000 .000

Consumer Category

Pillai's Trace .526 17.007 12.000 960.000 .000

Wilks' Lambda .517 19.878 12.000 841.640 .000

Hotelling's Trace .853 22.522 12.000 950.000 .000

Roy's Largest Root .747 59.783c 4.000 320.000 .000

a. Design: Intercept + Consumer Category

b. Exact statistic

c. The statistic is an upper bound on F that yields a lower bound on the significance level.

Hypothesis:

Ho: There is no significant effect across the Consumer Category and Infrastructure & Technology factors

The hypothesis is tested using the Consumer Category as independent measure (Fixed Factor) and Infrastructure & Technology factors as dependent variables. MANOVA procedure is applied to the data. The table of multivariate tests table displays four tests of significance for each model effect. The entire four tests show significant difference. The significance value of the main effect is less than .01, indicate that the effect Consumer Category contribute to the model.

Table 3 Descriptive Statistics across the Consumer Category and Infrastructure & Technology Factors

Infrastructure &

Technology factors

Consumer

Category Mean Std. Deviation N

EPS Infrastructure

Domestic 9.0923 1.13848 63

Industries 10.7507 1.92881 160

Commercial 9.4197 2.20458 95

Public use 7.4550 .00000 7

Total 9.4742 1.99037 325

IT

Domestic 6.4547 .26649 63

Industries 7.4388 .85896 160

Commercial 6.1125 1.03027 95

Public use 5.6030 .00000 7

Total 6.5271 .95744 325

OT

Domestic 6.4547 .26649 63

Industries 7.4388 .85896 160

Commercial 6.1125 1.03027 95

Public use 5.6030 .00000 7

Total 6.5271 .95744 325

MS Domestic 4.6940 .29299 63

Industries 4.5071 .79655 160



Infrastructure &

Technology factors

Consumer

Category Mean Std. Deviation N

Commercial 5.2026 .59037 95

Public use 4.5780 .00000 7

Total 4.6981 .68615 325

CS

Domestic 4.8720 .48304 63

Industries 4.3209 .65800 160

Commercial 3.5584 .33287 95

Public use 4.3257 .00000 7

Total 4.1864 .63470 325

Summarizes Descriptive Statistics across the Consumer Category and Infrastructure & Technology factors. The Descriptive Statistics table provides the summary of the analysis and means score of various dependent measures across the Consumer Category. There is a difference in opinion between consumer categories and Infrastructure & Technology factors at 1% level of significance. Hence the Ho is rejected. Further it is observed that mean score shows that EPS Infrastructure, IT and OT are found higher among the industries, MS is found higher among commercial category and CS is found higher among domestic category of consumers.#

Table 4 Tests of Between-Subjects Effects on Consumer Category and Infrastructure & Technology Factors

Source Dependent Variable Type III Sum of

Squares df Mean Square F Sig.

Corrected

Model

EPS Infrastructure 154.811a 3 51.604 14.675 .000

IT 75.517b 3 25.172 36.481 .000

OT 75.517b 3 25.172 36.481 .000

MS 21.979c 3 7.326 18.012 .000

CS 32.875d 3 10.958 36.025 .000

a. R Squared = .121 (Adjusted R Squared = .112)

b. R Squared = .254 (Adjusted R Squared = .247)

c. R Squared = .144 (Adjusted R Squared = .136)

d. R Squared = .252 (Adjusted R Squared = .245)

Provides summary of results of Tests Between-Subjects Effects on Consumer Category and Infrastructure & Technology factors.

3.1.2. Tests on Organization Category and Infrastructure & Technology Factors

MANOVA is used to explore taking organization as independent variable and Infrastructure & Technology factors like EPS Infrastructure, IT, OT, MS and CS as dependent variables to find the interactions among the dependent variable and also among independent variable. represents Multivariate Test on Organization Category and Infrastructure & Technology factors to determine significance of the following hypothesis.

Ho: There is no significant effect across the organization and Infrastructure & Technology factors



Table 5 Multivariate Tests on Organization Category and Infrastructure & Technology Factors

Effect Value F Hypothesis df Error df Sig.

Intercept

Pillai's Trace .995 17122.773b 4.000 319.000 .000

Wilks' Lambda .005 17122.773b 4.000 319.000 .000

Hotelling's Trace 214.706 17122.773b 4.000 319.000 .000

Roy's Largest Root 214.706 17122.773b 4.000 319.000 .000

Organization

Category

Pillai's Trace .576 32.373 8.000 640.000 .000

Wilks' Lambda .468 36.869b 8.000 638.000 .000

Hotelling's Trace 1.045 41.524 8.000 636.000 .000

Roy's Largest Root .946 75.640c 4.000 320.000 .000

a. Design: Intercept + Organization Category

b. Exact statistic

c. The statistic is an upper bound on F that yields a lower bound on the significance level.

Table 6 Descriptive Statistics across the Organization Category and Infrastructure & Technology Factors

Infrastructure &

Technology factors Organization Mean Std. Deviation N

EPS Infrastructure

Individual 8.1567 1.13848 63

Private 10.7507 2.14141 220

Public 9.3602 .70763 42

Total 9.4742 1.99037 325

IT

Individual 6.4909 .26649 63

Private 7.4388 .85770 220

Public 5.3487 .70755 42

Total 6.5271 .95744 325

OT

Individual 6.4909 .26649 63

Private 7.4388 .85770 220

Public 5.3487 .70755 42

Total 6.5271 .95744 325

MS

Individual 4.5765 .59037 63

Private 5.2026 .71205 220

Public 4.5780 .00000 42

Total 4.6981 .68615 325

CS

Individual 4.6976 .33287 63

Private 4.2686 .61466 220

Public 3.5584 .29637 42

Total 4.1864 .63470 325

Table 6 summarizes Descriptive Statistics across the Organization Category and Infrastructure & Technology factors. The hypothesis is tested using the organization as independent measure (Fixed Factor) and Infrastructure & Technology factors as dependent variables. MANOVA procedure is applied to the data. The table of multivariate tests table displays four tests of significance for each model effect. The entire four tests show significant difference. The significance value of the main effect is less than .01, indicate that the effect organization contribute to the model. The Descriptive Statistics table provides the summary of the analysis and means score of various dependent measures across the organization. There is



a difference in opinion between organizations and Infrastructure & Technology factors at 1% level of significance. Hence the Ho is rejected. Further it is observed that mean score shows that EPS Infrastructure, IT and OT and MS are found higher among private and CS is found higher among individuals.

Table 7 Tests of Between-Subjects Effects on Organization Category and Infrastructure & Technology Factors

Source Dependent Variable Type III Sum of Squares Df Mean Square F Sig.

Corrected

Model

EPS Infrastructure 178.413a 2 89.207 25.992 .000

IT 110.974b 2 55.487 96.040 .000

OT 110.974b 2 55.487 96.040 .000

MS 19.895c 2 9.947 24.147 .000

CS 37.308d 2 18.654 64.441 .000

a. R squared = .139 (adjusted r squared = .134)

b. R squared = .374 (adjusted r squared = .370)

c. R squared = .130 (adjusted r squared = .125)

d. R squared = .286 (adjusted r squared = .281)

Table 7 provides summary of results of Tests Between-Subjects Effects on Organization Category and Infrastructure & Technology factors.

3.1.3. Association between Consumer Category and their Opinion about Infrastructure &

Technology Factors

Chi-square analysis is carried out in order to find the significant association between Consumer Category and their opinion about Infrastructure & Technology factors like EPS Infrastructure, IT, OT, MS and CS.

Hypothesis:

H0: There is no significant association between Consumer Category and their opinion about EPS Infrastructure.

H1: There is a significant association between the Consumer Category and their opinion about EPS Infrastructure.

Table 8 Association between the Consumer Category and their Opinion about EPS Infrastructure

Consumer

Category Less challenge More challenge Total

Statistical

inference

Domestic 15 48 63

Chi-Square Value =

47.386*** df = 3

7.7% 37.2% 19.4%

Industries 113 47 160

57.7% 36.4% 49.2%

Commercial 61 34 95

31.1% 26.4% 29.2%

Public use 7 0 7

3.6% 0.0% 2.2%

Total 196 129 325

*** Significant at 0.01 level

It is observed from the Table 8 that there is a significant association between Consumer Category and their opinion about EPS Infrastructure and hence the null hypothesis (Ho) is rejected. Further, it is noted that a majority of the respondents (57.7%) of industrial consumers and 31.1% of commercial consumers opined EPS Infrastructure is less challenge. 37.2% of



domestic consumers, 36.4% of industrial consumer and 26.4% of commercial consumer opined EPS Infrastructure is more challenge. Hence it is concluded that different Consumer Category has a significant association on the opinion of EPS Infrastructure.

Hypothesis:

H0: There is no significant association between Consumer Category and their opinion about IT.

H1: There is a significant association between the Consumer Category and their opinion about IT.

It is inferred from the Table 9 that there is a significant association between Consumer Category and their opinion about IT and hence the null hypothesis (Ho) is rejected. Further, it is noted that a majority of the respondents (51.1%) of industrial consumers and 30.6% of commercial consumers opined IT is less challenge. 36.8% of domestic consumers, 40.4% of industrial consumer and 22.8% of commercial consumer opined IT is more challenge. Hence it is concluded that different Consumer Category has a significant association on the opinion of IT.

Table 9 Association between the Consumer Category and their Opinion about IT

Consumer


Statistical

inference

Domestic 42 21 63

Chi-Square Value =

14.439***

df = 3

15.7% 36.8% 19.4%


51.1% 40.4% 49.2%

Commercial 82 13 95

30.6% 22.8% 29.2%

Public use 7 0 7

2.6% 0.0% 2.2%

Total 268 57 325


Hypothesis:

H0: There is no significant association between Consumer Category and their opinion about OT.

H1: There is a significant association between the Consumer Category and their opinion about OT.

Table 10 Association between the Consumer Category and their Opinion about OT

Consumer


Statistical

inference

Domestic 0 63 63

Chi-Square Value =

24.466*** df = 3

0.0% 22.2% 19.4%


41.5% 50.4% 49.2%

Commercial 24 71 95

58.5% 25.0% 29.2%

Public use 0 7 7

0.0% 2.5% 2.2%

Total 41 284 325




It is found from the Table 10 that there is a significant association between Consumer Category and their opinion about OT and hence the null hypothesis (Ho) is rejected. Further, it is noted that a majority of the respondents (51.1%) of industrial consumers and 30.6% of commercial consumers opined OT is less challenge. 36.8% of domestic consumers, 40.4% of industrial consumer and 22.8% of commercial consumer opined OT is more challenge. Hence it is concluded that different Consumer Category has a significant association on the opinion of OT.

Hypothesis:

H0: There is no significant association between Consumer Category and their opinion about MS.

H1: There is a significant association between the Consumer Category and their opinion about MS.

Table 11 Association between the Consumer Category and their Opinion about MS

Consumer


Statistical

inference

Domestic 8 55 63

Chi-Square Value =

114.596***

df = 3

3.6% 53.4% 19.4%


56.3% 34.0% 49.2%

Commercial 82 13 95

36.9% 12.6% 29.2%

Public use 7 0 7

3.2% 0.0% 2.2%

Total 222 103 325


Shows that there is a significant association between Consumer Category and their opinion about MS and hence the null hypothesis (Ho) is rejected. Further, it is noted that a majority of the respondents (56.3%) of industrial consumers and 36.9% of commercial consumers opined MS is less challenge. 53.4% of domestic consumers and 34% of industrial consumer opined MS is more challenge. Hence it is concluded that different Consumer Category has a significant association on the opinion of MS.

Hypothesis:

H0: There is no significant association between Consumer Category and their opinion about CS.

H1: There is a significant association between the Consumer Category and their opinion about CS.

Shows that there is a significant association between Consumer Category and their opinion about CS and hence the null hypothesis (Ho) is rejected. Further, it is noted that a majority of the respondents (39.3%) of industrial consumers, 30.6% of commercial consumers and 30.1% of commercial consumers opined CS is less challenge. 66.4 % of industrial consumers and 27.7% of commercial consumer opined CS is more challenge. Hence it is concluded that different Consumer Category have a significant association on the opinion of CS.



Table 12 Association between the Consumer Category and their Opinion about CS

Consumer


Statistical

inference

Domestic 63 0 63

Chi-Square Value =

114.596***

df = 3

30.6% 0.0% 19.4%


39.3% 66.4% 49.2%

Commercial 62 33 95

30.1% 27.7% 29.2%

Public use 0 7 7

0.0% 5.9% 2.2%

Total 206 119 325


3.1.4. Impact of infrastructure and Technology Factors on Open Access Electricity Market

Infrastructure and technology factors such as EPS Infrastructure, IT, OT, MS and CS are used as inputs in regression analysis to identify predictors of open access market. The method used to predict the open access market is Multiple Regression Analysis.

Hypothesis:

Ho: There is no significant impact of Infrastructure and Technology factors on open access market

H1: There is a significant impact of Infrastructure and Technology factors on open access market

Table 13 Multiple Regression Model for Open Access Market Based on Infrastructure and Technology factors

Independent

variables

Unstandardized

Coefficients Standardized Coefficients Statistical inference

B Std.

Error Beta t Sig F value

Constant 4.103 .454 9.042 .000 R = 0.330

R2 = 0.109

Adjusted R2 = 0.097

9.746***

X1 -.079 .015 -.331 -5.404 .000***

X3 .029 .040 .057 .705 .481

X4 .175 .039 .252 4.481 .000***

X5 -.040 .057 -.053 -.691 .490

***Significant at 1% level

In this study, open access market (Y) is dependent variable; EPS Infrastructure (X1), IT (X2), OT (X3), MS (X4) and CS (X5) are predictor variables. The Table 13 shows that the combination of four variables together contributed to 33% effect on open access market. The variable IT is excluded for the analysis because of co-linearity. The R2 for the overall study on the above four variables suggests that there is a less effect (10.9%) of these independent variables on open access market (dependent variable). However, based on the adjusted R square value of 0.097, the elements contribute 9% to dependent variable. The F value (9.746) is significant at 1% level which implies that the model is fit. From the table it is found that EPS Infrastructure and MS variables give significant impact to open access market. It is clear that independent variable with higher level of β has higher impact on dependent variable. In this



study result reveal that the variable MS (β =0.252, p<0.01) is the most influential variable is exerted a statistically significant and positive influence on open access. EPS Infrastructure (β = - 0.331, p<0.01) is exerted a statistically significant and negative influence on open access market. The standardized coefficients Beta column, gives the coefficients of independent variables in the regression equation.

Y = -0.331X1 + 0.057X3 + 0.252 X4 – 0.053X5

This would suggest that EPS Infrastructure and MS play a significant role on open access market.

3.2. Enablement through Advanced Analytics & Cognitive Solutions

In this section how each utility stakeholder across globe are progressing to attain their target goals through the use of advanced analytics and cognitive solutions powered by infrastructure solutions such as Cloud, Internet of Things and mobile are discussed (IBM, MIT Sloan Management, 2011). The Figure 6 depicts a functional overview of utilities solutions to enable various stakeholders such as generation, grid, customers, retailers, energy service, renewable providers, market exchanges etc. In this summary, key solutions under the category of advanced analytics and cognitive solutions with a use case example are discussed and it does not represent entire solution portfolio due to limitation of the current scope. Technology factors were studied with focus on factors such as Data Explosion, Diminishing Expertise, Disruptive Innovation, Digital experience, societal inclusion and Economic sustenance. It is evident that both advanced analytics and cognitive solutions are complement to each other and are becoming business imperatives. The requisite and nature of solutions are explained in below section with at least one specific use case of each solution. It is intended to elaborate on each solution, benefits and their integration with other business as usual applications in different publications in future.

Figure 6 A Portfolios of Target Solutions for Various Electricity Market Stakeholders

3.2.1. Advanced Analytics

Worldwide due to varying business models, for many grid utilities complexity increases while managing their ageing infrastructure, diminishing skilled resources, they also have additional responsibility for managing their own distributed energy resources, provide an ability for market operations to perform with more efficiency and economy. Hence it is vital that the grid companies’ system architectures must be equipped to play a challenging role of energy and system integrators. Traditionally utilities are cautious adopters of technologies due to its inherent regulatory and public apprehensions. However most of global utilities are in the midst of smart grid or digital transformation. As business models evolving – some are privatized, many of vertically integrated utilities have started unbundling, now new franchisee models pop up, multiple suppliers in same region especially in metros, open access is on for large customers and being targeted for small scale consumers in the future. Customers are more engaging than ever before in utilities business and holds high expectations than those are currently realized.



All these challenges combined with market forces are compelling executives to think beyond the traditional approaches and embrace the new technologies. Thus thoughtful application of each solution in an innovative way is essential. Also convergence of Information & Operation technologies (IT & OT) (example, smart meters sending power quality & consumption data every 10-15 minutes increases volume by of meter read by approximately 2800 times compared to once a month read) which are causing exponential data growth. Although various sources are contributing to growth, it is essential to empower stakeholders leveraging of type and other attributes of data (IBM CAI, IBM IBV, 2012). Typical example of benefit areas for grid companies include a) Predict Asset performance and defer network investments b) increase operational efficiency c) Optimize Outage restoration d) Power portfolio optimization etc. It is important to note that new challenges being spanned due to additional analytics deployments. Here it is not just volume, but velocity & variety of the data are also increasing. As we discussed earlier, these new opportunities (IDC, 2011) should address the challenges caused by the three famous ‘V’ of big data being experienced by utility.

Volume: Smart meters, PMUs, sensors, integration with Supervisory Control and Data Acquisition (SCADA), Energy Management Solutions (EMS) and Distribution Management Solutions (DMS), are generating new data in a volume that utility staff or systems were not designed to handle

Velocity: Time scale of data arriving ranges from few milliseconds from PMUs and Advanced Metering Infrastructure (AMI) interval data to all the way to once a month from customer payment and all time intervals in between.

Variety: Sources of data ranges from utilities internal & trusted sources to blogs from customers, format of data can be a well-defined structure or free unstructured format text.

Use case from various grid companies’ represents predicting peak load which involves high complex customer energy models, segmentation models, demand forecasting based on various other parameters such as weather, cloud etc., and help optimize dispatch through demand response solutions.

3.2.2. Cognitive Solutions

Cognitive solutions use natural language processing and machine learning to reveal insights from large amounts of unstructured data. Typically for utility industry unstructured data set includes manuals and user guides, thermal images, videos, operational or transactional notes, social & weather interactions etc. A cognitive solution helps to observe, interpret, evaluate and make informed decisions. It self learns knowledge, converts into hypothesis and further facilitate stakeholders or employees to perform better. Through the study of effects of ageing workforce and data growth as explained above sections, it is evident that there is a need for a set of solutions with self-learning capabilities and as well that captures the human and machine (both structured and unstructured) knowledge through capabilities such as natural language processing, and help apply statistical & network analysis. It should also facilitate information exploration capabilities such as hypothesis generation and evidence expression. The Figure 7 explains the approach to encounter increasing customer expectation with ageing work force and increasing data (Jayaprakash, 2016). It suggests that upgrading or speed up the time to resolve per each worker is essential. This means implementation of cognitive solutions helps through knowledge capture, retention and reapplies into analytics to provide insights for the newly skilled or unskilled resources. It is intended to discuss detailed use cases in next publications including the topics such as micro grid (Effatnejad, 2015), WAMS/WACS including setting less protections, etc.



Figure 7 An Approach for Improve Speed to Resolve of Field Workers through Cognitive Solutions

4. CONCLUSION

The study enlightens that the advanced analytics and cognitive solutions play key role towards competitive objectives. It also indicates that Data is Key for bringing insights to enable informed decision making, three key requirements such as volume, variety and velocity needs to be accounted while designing analytics platform. The requisite solution shall not limit the ability to handle structured and unstructured data; it should also cater an ability to support cognition within and outside organization in order to manage lowering skills and customer expectations better.

4.1. Research Implications and Policy Implications

This Study combined with review of utilities stakeholders approaches globally suggests that for realization of autonomous market maturity requires coherence of appropriate strategy & roadmap followed by step by step implementation of various solutions which includes not just technology applications but also various initiatives to align their people, legal & regulatory framework, social & organizational behavior. It was envisaged to lever this publication by emphasizing on key facets of those technologies and present some of use case areas. Analysis and interpretation clearly highlights detailed list of the Infrastructure & Technology subject areas.

4.2. Limitations of the Study and Scope for Further Research

This paper did not discuss other enabling technologies such as internet of things (IOT), cloud, social and mobile to enable utility organization to perform better towards competitive journey. The point of view to empower every stakeholder with digital experience to manage challenges from people, framework and technology has not been covered in this publication. One should always focus on extracting maximum benefits from any targeted technology investments includes advanced analytics, cognitive solutions powered by cloud, IOT and mobile capabilities. It is proposed to discuss every challenges and do deep dive into design of utility system architecture and also to provide insight on data governance and management methods which would play vital role in shaping the utilities analytics and cognitive journey in future.

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