Utility-side versus customer-side business model.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Since the first years of the introduction of cocaine by Carl Koller in 1884, the evolution of regional anesthesia has been continuous, gradual and safe. Its development has been based on anatomy, the pharmacology of local anesthetics and adjuvant drugs, as well as advances in the various blocking techniques, with ultrasound guidance being the most recent advent. The use of ultrasound in regional anesthesia has shown the reduction of complications, which makes it mandatory to knowledge and acquire skills in all ultrasound-guided techniques.
\r\n\r\n\tUltrasound-guided regional blocks will be reviewed extensively, as well as intravenous regional anesthesia, thoracic spinal anesthesia. The role of regional anesthesia and analgesia in critically ill patients is of paramount importance. In addition, we will review the current role of regional techniques during the Covid-19 pandemic. Complications and malpractice is another topic that should be reviewed. Regional anesthesia procedures in some specialties such as pediatrics, orthopedics, cancer surgery, neurosurgery, acute and chronic pain will be discussed.
",isbn:"978-1-83969-570-4",printIsbn:"978-1-83969-569-8",pdfIsbn:"978-1-83969-571-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"264f7f37033b4867cace7912287fccaa",bookSignature:"Prof. Víctor M. Whizar-Lugo, Dr. José Ramón Saucillo-Osuna and Dr. Guillermo A. Castorena-Arellano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10708.jpg",keywords:"Regional Anesthesia, Ultrasound-Guided Regional Anesthesia, Local Anesthetics, Preventive Analgesia, Peripheral Blocks, Pediatric Regional Anesthesia, Intravenous Regional Anesthesia, Techniques, Complications, Adjuvants in Regional Anesthesia, Opioids, Alfa2 Agonists",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 25th 2021",dateEndSecondStepPublish:"March 25th 2021",dateEndThirdStepPublish:"May 24th 2021",dateEndFourthStepPublish:"August 12th 2021",dateEndFifthStepPublish:"October 11th 2021",remainingDaysToSecondStep:"23 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Whizar-Lugo has published more than 100 publications on Anesthesia, Pain, Critical Care, and Internal Medicine. He works as an anesthesiologist at Lotus Med Group and belongs to the Institutos Nacionales de Salud as an associated researcher.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"169249",title:"Prof.",name:"Víctor M.",middleName:null,surname:"Whizar-Lugo",slug:"victor-m.-whizar-lugo",fullName:"Víctor M. Whizar-Lugo",profilePictureURL:"https://mts.intechopen.com/storage/users/169249/images/system/169249.jpg",biography:"Víctor M. Whizar-Lugo graduated from Universidad Nacional Autónoma de México and completed residencies in Internal Medicine at Hospital General de México and Anaesthesiology and Critical Care Medicine at Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán in México City. He also completed a fellowship at the Anesthesia Department, Pain Clinic at University of California, Los Angeles, USA. Currently, Dr. Whizar-Lugo works as anesthesiologist at Lotus Med Group, and belongs to the Institutos Nacionales de Salud as associated researcher. He has published many works on anesthesia, pain, internal medicine, and critical care, edited four books, and given countless conferences in congresses and meetings around the world. He has been a member of various editorial committees for anesthesiology journals, is past chief editor of the journal Anestesia en México, and is currently editor-in-chief of the Journal of Anesthesia and Critical Care. 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Dr. Saucillo-Osuna has lectured at multiple national and international congresses and is an adjunct professor at the Federación Mexicana de Colegios de Anestesiología, AC, former president of the Asociación Mexicana de Anestesia Regional, and active member of the Asociación Latinoamericana de Anestesia Regional.",institutionString:"Asociación Latinoamericana de Anestesia Regional",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"346513",title:"Dr.",name:"Guillermo A.",middleName:null,surname:"Castorena-Arellano",slug:"guillermo-a.-castorena-arellano",fullName:"Guillermo A. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"61197",title:"Diversifying Electricity Customer Choice: REVing Up the New York Energy Vision for Polycentric Innovation",doi:"10.5772/intechopen.76023",slug:"diversifying-electricity-customer-choice-reving-up-the-new-york-energy-vision-for-polycentric-innova",body:'\nThe electric utility landscape is experiencing rapid and unprecedented transformation. A powerful confluence of structural, technological, and socio-economic factors is driving this change. Distributed technologies (e.g., distributed generation, energy storage, flexible demand, and advanced power electronics) are competing in the emerging distributed utilities market and, as a result, putting pressure on investors and regulators to consider utility choice management (UCM) opportunities that promote more capital-efficient options for the provision of electricity services [1]. The second installment of the Quadrennial Energy Review (QER), released in the winter of 2017, recommends spending $300–$500 billion in grid modernization, noting that it “is the platform for the twenty-first century electricity system, bringing significant value associated with lower electricity bills due to fuel and efficiency savings, more electricity choices, and fewer and shorter outages” [2]. The QER also recommends that utilities deploy a “wide range of new, capital-intensive technologies” to modernize their aging infrastructure, and to “support increased reliability, security, value creation, consumer preferences, and system optimization and integration at the distribution level.” At the distribution utility level, the electric utility faces a fundamental challenge. Besides investments needed for grid modernization, the emergent role of the consumer as prosumer coupled with new priorities, such as enhancing electricity reliability, affordability, resilience, environmental protection, and grid security, are driving the current evolution in the industry and destabilizing the century-old government-regulated, vertically integrated, monopoly business model that is the energy utility.
\nThe pressure to revamp the electric utility landscape is evident not only in the contiguous United States—for example, New York, California, Illinois, Massachusetts, and North Carolina—but also in Hawaii and Alaska [3]. The dominating trend of fast-flexing renewable energy sources, mostly solar and wind power, continues to underpin early retirement of baseload power-generating sources such as nuclear, coal, and natural gas steam generator [4]. The growth of solar and wind power, flat or declining electricity demand, and cheap natural gas have been cited as the reasons for the decline in electricity prices and economic viability of baseload energy generation sources such as nuclear energy [5, 6] and thus declining revenues for utility generators. As a result, strategic improvement of utility structure and planning to create new choices for customers requires explicit recognition and response to these challenges as well as local and regional idiosyncratic design and operational obstacles. For instance, utilities across the country face distinctive characterizations of the so-called ‘death spiral’ - the cycle of eroding market share to distributed energy prosumers that raises costs on remaining utility customers, leading to accelerated market losses [7, 8]. Nationwide, the ‘death spiral’ debate is substantial. According to Accenture, estimated utility sector revenue erosion in the United States resulting from increased distributed generation and gains in energy efficiency could be between $18 and $48 billion by 2025, depending on status quo, demand disruption, or perfect storm assumptions [9] (Figures 1 and 2). However, this debate continues with varied levels of concerns across states and regional electricity markets like PJM Interconnection, Midcontinent (MISO), Texas (ERCOT), California (CAISO), New England (ISO-NE), and New York (NYISO). The effect of the dreaded ‘death spiral,’ if it materializes, will be felt differently across the nation’s utilities. Similarly, aging infrastructure concern due to long periods of low investments in grid modernization, changing supply and demand profiles, and investments in research and development (R&D) commitments are not geographically ubiquitous [2, 6, 10].
\nEstimated erosion of utility revenue.
How the adoption of energy demand-disrupting technologies could erode energy demand and utilities’ revenues through 2040.
Recent studies by McKinsey & Company conclude that energy storage is already economical for many commercial customers [11]. Rapidly falling solar photovoltaics (PV) prices coupled with low-cost storage will create an increasing number of residential and commercial customers who will meet their electric service needs through distributed generation. Falling storage prices have the potential to transform the power landscape by smoothing out the variations in power associated with variable electricity power, such as solar and wind, and achieve 24/7 reliability. Frew et al. review pathways to a highly renewable U.S. electricity future and observe that design of policies such as renewable portfolio standard (RPS) targets, Federal Energy Regulatory Commission (FERC) orders, emission regulations, greater regional coordination and geographic aggregation, and energy storage is critical to the emergent distributed electricity market [12]. While there is disagreement on the structure of electricity market design, regional coordination planning, flexibility mechanisms required to help mitigate the variability and uncertainty challenges arising from a high penetration of intermittent electricity generation, and how soon and how fast a highly renewable electricity future can occur, the trend is similar for many parts of the United States.
\nSeveral response strategies have emerged shaped by policy, market, public oversight, and financing support. These include utility-as-platform models like the New York Public Service Commission’s (NYPSC) grid and market modernization initiative called Reforming the Energy Vision (REV), utility as a smart integrator, and electric services operator model [13]. The New York’s REV vision recognizes that the path for a distributed utility model which promotes a highly renewable electricity future in the state will not be linear. Hence, the vision lays out multiple sets of solutions to various aspects of electricity market design and operations, taking into consideration utility market composition and regulatory structures. This paper evaluates a typology of policy, regulatory, and business model constructs for diversifying energy mix and utility choices, arguing for a polycentric approach to carry out utility business-model innovation and electric power market design that might allow this suggested future to play out in the real world. Section 2 discusses challenges, limitations, and opportunities of utility-side and customer-side business models. Section 3 evaluates the Hamel framework, and Section 4 applies this framework to the New York’s REV. Section 5 concludes the paper.
\nThe business model concept offers a valuable unit for evaluating new market ventures and business practice [14, 15, 16]. There is no universally accepted definition of a business model. However, authors in different industries have proposed a litany of definitions. Ref. [17] defines a business model as “the rationale of how an organization creates, delivers, and captures value” while [18] describes a business model as “the heuristic logic that connects technical potential with the realization of economic value.” Ref. [19] defines a business model as “a representation of the underlining core logic and strategic choices for creating and capturing value within a value network.” As an analytic tool, the concept has been widely used in studying investors’ preference for service-driven business models [15], energy service company (ESCO) [16], micro-generation solutions [20], the distributed electricity generation market [21], energy efficiency programs [22], evolution of energy utilities [23], and the ongoing expansion of distributed electricity generation market [24]. As a result, the business model concept has been widely tested in practice in the energy sector. Common components of the business model include the value chain, value propositions, target markets, competitive strategy, revenue-generation models, customer interface, value network, and infrastructure service [18, 25].
\nBusiness-model innovation as a term remains largely vague. Reference [25] notes that business-model innovation is less a matter of superior foresight, but more of trial and error and ex-post adaptation. Reference [26] suggests that it entails business model experimentation, while [27] views it as a strategic renewal mechanism for organizations undergoing through periods of transformation in their external environment [28]. In this chapter, business-model innovation refers to the development of new organizational forms to create, deliver, and capture value for realizing a distributed utilities future. Electric utilities in New York and elsewhere have different starting points, value propositions, customer expectations (across customer classes), and priorities, and they vary significantly with respect to electricity revenues, electricity sales, and customer-base. How can utilities meet these demanding business expectations in an uncertain environment? Fox-Penner (2010) offers a solution through a “two-and-a-half-business model” innovation as an alternative [13, 28]. The half refers to a smart integrator scenario in which the utility operating the power grid does not own or sell the power delivered by the grid. Consequently, power generation and grid infrastructure development including its information and control systems are community-owned (e.g., a community micro-grid). The advantage of a community-owned distributed generation is its potential for economies of scale. Hundreds to thousands of customers join the network participating as both consumers and producers (or prosumers) of renewable electricity from sources like solar PV and wind turbines. These prosumers use the set operational standards, but the financing and administration side of the business model is handled separately by the utility.
\nWith that in mind, our research shows that aligning core business incentives of electricity distribution utilities with cost-effective integration of DERs into power systems is a prerequisite for achieving DMS and UCM business model constructs that might allow this future to come about, arguing for a ‘polycentric’ approach in the near term. As a preliminary matter, it is commonly noted that the smart integrator model has well-developed analytic capabilities to ensure the electric grid can meet electricity demand at all times. The smart integrator model also has a green dispatch mechanism that enables utilities to determine when and how to switch to low-carbon energy sources such as solar, wind, and hydroelectric power. Therefore, the only key obligation of the utility is ensuring that the local grid meets power demanded in the system. Second, the smart integrator has a “highly secure but maximally open platform for information, price, and control signals” [13]. This feature ensures that it responds well to different regulatory regimes by integrating information for accounting, billing, and settlement systems to accommodate the more complicated functions such as managing pricing plans, payment, and billing. Related to the smart integrator model is the energy services utility (ESU), which is an extension of the smart integrator model. In the ESU model, the focus of the utility shifts from being a purely asset- and commodity-driven entity to a service and value-added enterprise in which profit achievement hinges on the services offered to consumers [13, 15, 28]. Examples of the ESU business model include programs offered by Arizona Public Service Electric Company (the largest electric utility in Arizona), including energy storage, demand response, and load management.
\nUnder a smart integrator, utilities must consider creating different triads of structure, regulation, and revenue models to facilitate transformation to a distributed utilities future. This process requires a variety of innovations, including joint construction and developments of electricity generation and delivery of electricity services such as financing and building related assets, ownership, and operations; growth of diversified independent transmission companies; diversified of generation mix with high composition of low-carbon resources mostly from natural gas and renewables such as hybrid solar PV systems, polygeneration energy systems, or zero-net energy systems; use of subsidiaries to speed up clean energy diversification; and use of utility consortia that expand member utilities’ service offerings beyond the provision of electricity service (e.g., to cater to cooperative customers).
\nTwo principal factors concern utilities. First, electricity must get to the customer reliably and safely. Second, power must be delivered efficiently to maximize profit margins. These factors put pressure on struggling utilities to minimize electric grid system losses. Utility-side business models, concepts, components, and technologies therefore ought to take these factors into consideration. With the growth of prosumers, the challenge then becomes: which key policy, market, and business concerns should utilities prioritize? Other salient challenges include optimal deployment of expensive assets, need for diversification of generation, demand response management, grid stability, and tariff implementation. Some of these challenges can be addressed by deploying ‘smart’ technologies at the utility-side to monitor operations and improve billing and tariff management. In states with fast changing electric utility landscapes such as New York, however, regulators need to identify and deconstructed elements of innovations in a contextually-appropriate manner to assure scalable solutions.
\nRef. [29] examines a suite of wholesale power market design currently in use on the customer-side to improve electricity reliability, security, and flexibility. It also assesses feasibility of wholesale market design with high penetration of DERs considering the role of technological innovations such as demand response, distributed generation, and energy storage. These technologies support the infrastructure needed to provide electricity services and address critical challenges such as climate change, energy security, and revenue erosion [2]. The revenue erosion concern can also be addressed through customer-side renewable electricity business models. In this chapter, distributed generation systems refers to small-scale generation systems (e.g., for private customers and small- to medium-sized businesses) in the range of a few kilowatts to about 5 MW from sources such as solar PV, micro-wind turbines, and micro-combined heat and gas-power systems. Accordingly, customer-side and utility-side business models follow a very different logic in the value chain: the former is based on many small projects while the latter focuses on a small number of large projects. Table 1 summarizes the differences of the two models [30, 31].
\n\n | Customer-side business model | \nUtility-side business model | \n
---|---|---|
Customer interface | \n\n
| \n\n
| \n
Value proposition | \n\n
| \n\n
| \n
Infrastructure | \n\n
| \n\n
| \n
Revenue model | \n\n
| \n\n
| \n
Utility-side versus customer-side business model.
Unlocking greater value of distributed utilities requires new business models that improves ownership, asset management, and monetization of utility assets. In the utility-controlled and utility-owned value arrangement, utilities continue to execute their core competency functions, for example, asset ownership and operation. For instance, New York State’s (NYS), clean energy standard (CES) provides for a “50 by 30” goal, which commits the state to procure 50% of its electricity from renewable resources by 2030. Each load-serving entity is required to procure for their retail customers renewable energy credits (RECs) linked to DERs listed in Tier 1 (e.g., solar, wind, biomass, and pumped storage hydroelectric) [32]. Likewise, the customer-side structure provides a context in which to situate the RECs’ management; utilities can bundle these RECs into service programs, such as utility green pricing plans, and sell them to other parties.
\nA fundamental challenge facing New York today is how to generate richer innovations at all levels, including products, business models, and management systems that transform a centralized power system into a high-performing distributed utility sector. The critical challenge in this endeavor, however, entails fashioning a comprehensive analytical framework that captures components of business model across the entirety of the market spectrum. To avoid the pitfall of ambiguous strategy in such a framework, a service-based business model approach should be adopted. Ref. [33] identifies six key functions of business model strategy as value proposition, revenue generation mechanism(s), value chain, value network, target market, and a competitive strategy, while [19] lists the four often-cited business model components: strategic resources, value creation, value capture, and value network. Hamel business model [34], which is applied in this chapter, incorporates these fundamental features, providing a robust framework (Figure 3) for analyzing the REV vision. It appears that REV is based on a polycentric paradigm as the main pathway with which utility market reorganization will be navigated. Several studies have already explored UCM governance approaches with polycentric characteristics, e.g., [35, 36, 37, 38, 39]. These contributions largely focus on bending reality, business model constructs, and institutional and near-term governance as an impetus for polycentric innovation. We argue here that so long as utility regulation and governance lag behind technology innovation, institutional innovations needed to support the industry to “become more adept at generating richer innovations at other levels, including products, services, business models, and management systems,” will continue to play catch up thus impeding the full participation of DER resources [40].
\nComponents of Hamel business model framework.
Hamel’s business model is comprised of four major components (i.e., core strategy, strategic resources, customer interface, and value network), three bridge components (customer benefits, configuration, and company boundaries), and sub-elements that determine the profit potential (efficiency, uniqueness, fit, and profit boosters). The first component, a
The product/market scope defines where the firm competes (i.e., the firm’s competitive arena). For instance, the scope determines the customers, geographies, and product segments [38]. In this regard, the definition of product/market scope can be a source of business concept innovation for a firm—especially when it is entirely different from that of traditional competitors [34]. Finally, basis for differentiation captures how the firm or organization competes differently from its competitors. For instance, a firm differentiates itself from competitors by seeking answers to questions such as: how do opponents differentiate themselves in the electricity market (e.g., in designing utility revenue models such as platform service revenues, rate design, and customer energy data usage)? Are there other dimensions of market-oriented revenue model differentiations that could be explored? In what aspects of the energy service (e.g., rate design) has there been the least differentiation? How could differentiation be increased in some of these dimensions (e.g., by implementing opt-in rate initiatives such as time-of-use rates or smart home rates)? And have differentiation opportunities been diligently sought in every dimension of the business model?
\nHamel’s second major component,
The third major component of the Hamel framework is
The fourth component is the
At the base of the framework are four factors that define the utility of the Hamel business model.
Initiated in 2014, New York’s REV program is a comprehensive effort to reform the state’s energy system in order to align ownership, management, and operation of its utility industry [43, 44]. REV is led by NYPSC and seeks to fundamentally transform the electric power sector of New York State from a primarily centralized generation system to distributed utilities model [45]. The REV docket has two tracks. Track 1 focuses on the development of DER markets and the utility-as-platform model known as distributed-system platform (DSP) providers, while Track 2 focuses on reforming utility-ratemaking practices and revenue streams to accommodate the proposed DSP model. Implementation of REV will take several years and will involve the mutual efforts of industry, customers, non-profit organization, and regulatory partners. The initiative encourages regulatory changes that promote energy efficiency, demand response, increase storage capacity, and increase renewable energy resources. These reforms empower end-users by providing more choices through diversification of energy resources, and by fostering improvement in the performance of the power sector across policy objectives such as system-wide efficiency, system reliability and resiliency, enhanced customer billing system, market animation and leverage of customer contributions, fuel and resource diversity, and reduction of carbon emissions [44].
\nRichard Kauffman, chair of the state’s Energy Research Development Authority (NYSERDA) and former NYPSC Chair Audrey Zibelman explain that the REV program is “removing market barriers and bridging market gaps that have historically impeded the clean energy sector from benefiting from technological innovations” [46]. Its major impact on the industry so far has been increased integration of solar- and wind -energy generations. Therefore, this evaluation focuses on the regulations and directives specified by the NYPSC, and other guidelines released by key power utilities in the state [e.g., Consolidated Edition, Long Island Power Authority, Niagara Mohawk Power Corporation, New York Power Authority (NYPA), New York State Electric and Gas Corporation (NYSEG), Central Hudson Gas and Electric Corporation (CHGEC), Orange and Rockland Utility Inc., and Rochester Gas and Electric Corp (RG&E)] to explore the characteristics, nuances, structure, and approaches applied.
\nRetail peak electricity demand in NYS is approximately 75% greater than the average system load, and nearly 9% of power generated in the state is lost in transmission [47]. Essential investment needed through 2025 to replace the state’s aging infrastructure to meet projected energy demand is estimated at $30 billion [43]. REV is thus a ‘polycentric’ strategy intended to make distribution planning more transparent and better integrated. For instance, it seeks to transform electric distribution companies into DSP providers with responsibility for active coordination of DERs. It fosters “transactive energy” ecosystem in which “consumers and other parties can take full advantage of every type of energy resource—on both sides of the meter” [45]. Key to this ambitious goal is reorienting the traditional regulatory model by aligning utility and consumer interests so that both groups benefit from (scalable) improved market efficiency and scalable organizational learning.
\nTwo pricing mechanisms offer a critical role in this regard. First, REV establishes benefit–cost analyses as a foundational procurement tool to determine renewable electricity deployment [48]. Chosen due to its regulatory familiarity and apparent simplicity [49], the multi-year distribution system integration plans (DSIPs) to be developed by utilities seeks to foster a fair, open and value-based decision-making environment for utilities to build out their own competitive advantage in the DER market [45]. The benefit–cost approach will be applied in DSP investments, procurement of DERs through competitive selection and tariffs, and energy efficiency programs. Second, REV proposes using locational marginal pricing (LMP) principles to optimize the value of distributed utilities. Application of LMP principles can help distinguish which configuration of distributed resources enhances system flexibility and yield overall best value to consumers [44]. In terms of a repurposed DER policy, market development, innovation in designing value strategy and benefit–cost of DSIPs, and investment in community-choice aggregation programs, the REV model shares some of these characteristics with other ambitious and successful initiatives, particularly the German Energiewende initiative [50]. New York is not alone in its efforts to improve its utility regulation market and optimal system efficiencies. Parallel regulatory actions have been proposed in California, Hawaii, Massachusetts, Minnesota, and Illinois through its proposed utility of the future study known as “NextGrid” [51]. However, REV represents the most promising utility-as-platform business model as it challenges two fundamental components of the conventional utility model: the assumption that electricity demand is inelastic, and the notion that economies of scale make a centralized generating model the most economical way for electricity services provision [52] and market development. Table 2 summarizes the main policy, regulatory, and technological solutions that utilities and planners have proposed to improve DMS and UCM strategies based on polycentric approach to business-model innovations.
\nPolicy, regulatory, and technological solutions for advancing polycentric innovation | \nAuthor(s) | \n
---|---|
Information asymmetry, capital expenditure bias, and time-varying rates. | \n[53, 54] | \n
Distribution utilities and their place in an integrated grid model to provide infrastructure services, enhance personalization, and value creation. | \n[1] | \n
Energy performance contracting, regulation of retail energy markets, and innovation of revenue and pricing models. | \n[16, 55] | \n
DERs, DSPs, benefit–cost analysis framework, and net energy metering. | \n[3, 55, 56] | \n
Institutionalized polycentric innovations in energy governance, and sociotechnical co-evolution of energy planning and policymaking. | \n[10, 38, 39] | \n
Marginal-cost-based dynamic pricing and time-varying electricity rates. | \n[47] | \n
Utility financial incentives, investments, utility of the future roadmaps: (smart grid development, DERs, and customer utility service model). | \n[45, 57] | \n
Electric grid modernization and polycentric governance (democratized energy paradigm). | \n[45, 46] | \n
Policy, regulatory, and actions for polycentric innovation.
Table 3 offers a four-part, multi-dimensional, Hamel analytical framework and application of the key dimensions to REV. These dimensions extend beyond business-model innovation in the utility industry. These dimensions attempt to account for the increasing focus on performance-based utility operation, the relationship dynamics that accompany such a shift [58] and the required transition to a servitization system—as mandated by system reliability and resiliency, system-wide efficiency, and the climate change challenge [3].
\nComponent | \nDefinition | \nREV features | \n
---|---|---|
Strategic resources | \nDepicts the architecture of the utility value creation. Includes strategic assets, know-how, core processes and competencies. | \nAn estimated $30 billions of investment in the state’s aging grid infrastructure is required by 2025. NYSERDA’s Clean Energy Fund provides $5B investment in new green energy over 10 years, starting in 2016. | \n
Customer interface | \nGreater customer interactions, including customer relationship, segmentation, fulfillment support, and revenue structure. | \nREV promotes greater consumer choice. Emphasizes enhanced customer-centric paradigm (e.g., billing solutions for effective management). Nonlinear transactions. | \n
Value network | \nIncludes utility added values or business offerings to resource providers, suppliers, and partners. | \nRemoves market barriers and promotes distributed utilities. Promotes greater interaction among DSPs to create a market pricing platform, and service monetization. | \n
Core strategy | \nThe utility’s capacity to change course in the face of potential existential business model risks. This capacity is influenced by the flexibility and complexity of both the business model but also the infrastructure it operates. | \nDistribution utilities act as DSPs. Energy efficiency savings are part of utility revenue not dedicated surcharge. Earning impact mechanisms (EIM) replace platform service revenues (PSR) and market based earnings (MBE). Includes modified clawback mechanisms to attract third parties. Encourages time of use (TOU) rates. Each utility submit benefit–cost-analysis plan. | \n
Application of Hamel business model to conventional energy utility.
There are four main types of electric utilities in NYS, namely investor-owned private utilities, retail-power marketers, state-owned public authorities, and municipal utilities. These utilities can be grouped into two service types: bundled and delivery. Several organizations have institutional capabilities, mandates, and responsibilities for managing utility customer choice archetypes in New York (Figure 4). Eventually, NYSERDA may emerge as the hub of such polycentric activities. However, a more polycentric governance approach could potentially emerge across and between several bodies as institutional innovation takes root, with organizations such as the NYPSC and FERC providing oversight mechanisms for greater transparency in utility rate design, wholesale market regulations, and DER integration, and organizations like the North American Electric Reliability Corporation (NERC) and New York State Reliability Council (NYSRC), establishing greater degrees of reliability standards. This polycentric innovation development could help minimize information asymmetries and strategic behavior such as disguising true expected future costs to the regulator to increase allowed revenues or returns. As the NYPSC contends, “asymmetry regarding system information if continued will result in a barrier to new market entry by third parties and ultimately impede innovation and customer choice” [44]. On the other hand, New York Independent System Operator (NYISO)—a non-profit organization set up by NYS—could emerge as the central open platform for procuring DERs from suppliers. NYISO currently administers wholesale electricity markets in the state and provides reliability planning for bulk-electricity power, but this function could expand with the growth of DERs especially bulk power generation. Ultimately, NYISO would continue to oversee the wholesale electricity markets in NYS while FERC regulates wholesale electricity rates, licenses hydroelectric projects, and sets policies for interstate electricity sales. Under FERC Order 745, FERC regulates wholesale product tariffs by independent system operators (ISO) such as NYISO—including integration of DERs into wholesale markets [45].
\nNYS electric industry participants and institutions.
The state’s strategic resources and utility assets are owned, operated, and regulated by a variety of private and public entities (Figure 4). The functions provided by this complex electricity infrastructure create a path dependency in which existing business models either enable or constrain energy market development. The resulting utility landscape that manages the flows of all these energy resources has experienced consolidation to the point at which, in 2015, a “baker’s dozen” of three holding companies (namely Consolidated Edition, Long Island Power Authority, and Niagara Mohawk Power Corporation) representing 2.4% of all integrated utilities controlled 49% of utility revenues [4]. The REV model fully addresses the subcomponents of strategic resources (core competencies, strategic assets, and core processes) of the utility industry such as the aging infrastructure challenge. It supports what Reference [59] refers to as “infrastructure to services transition”, or the “evolution of infrastructure for commodity delivery” to support greater personalization of value—new purposes, new platforms, enabled new infrastructure, and new applications (services).
\nREV empowers customers with meaningful level of choice and reduces cost-of-service of electricity consumption. For instance, it improves electricity billing system and knowledge of customer analytics, and animates the market with substantial choice offering about the consumption and provision of electricity services (e.g., from whom to procure electricity services and from what resources) [45, 46]. Conventional electric utilities compete by establishing utility-consumer relationship characterized by billing-based interactions that are impersonal, distant, and standardized. This distant aspect arises partly due to primary fiduciary obligation to the owners and shareholders of the company. Additionally, conventional utilities are characterized by less customer interactions as they do not go “beyond-the-meter.”
\nFundamental to optimizing behind-the-meter storage assets and DERs like rooftop solar is sharing of distribution-level data of the utility grid and common understanding of its distribution system. In 2015, a total of 124 utilities operated in New York with investor-owned utilities accounting for 12% of the total market share, representing 71% of customers (Figure 5). Behind the meter, cooperative, municipal, retail power marketer, and state utilities accounted for 9.7, 0.8, 9.7, 65.3, and 2.4% of the total market ownership, respectively. Investor-owned utilities operate under conditions of a guaranteed rate of return that is set by NYPSC. In the conventional business model, utilities invest in large-scale asset, economies of scale, and long-term infrastructural commitments that determine the form of the revenue/cost structure. These features still influence portfolio of electricity sales, revenues, and customer numbers of certain utilities in New York, even as the implementation of the REV model is ongoing. Behind-the-meter recorded the fastest growth in electricity revenues, sales, and customer count of 89.4, 78.6, and 68.7% in 2015, respectively. Under REV, DSP providers “create markets, tariffs, and operational systems to enable behind the meter resource providers to monetize products and services that will provide value to the utility system and thus to all customers” [43].
\nNumber of utilities, by ownership from 2008 to 2015.
The business model of the traditional utility pursues expansion in asset-based and, through its commodity-focused strategy, increases shareholders value. The goal of the conventional utility, as such, can be conceptually positioned at one end of a profit-motivation spectrum: the “motivation to build incremental assets for the primary purpose of expanding its rate-base” [60]. Because regulators reward or chastise utilities for decisions to achieve certain public-policy goals and to maintain “just and reasonable revenues,” this model faces mounting challenges—especially in a DER framework. So-called “incentive regulation,” however, establishes the working conditions of the utility. Within these conditions, “[g]iven any set of regulations, utilities participate in actions which most benefit their principal constituencies—shareholders and management—while meeting the requirements of the regulations” [61]. Because the principal constituency of the investor-owned utility is its shareholder base, REV seeks to expand utility customer-base through
All the major distribution utilities in New York support the REV vision for long-term innovation in the industry and have submitted proposals for pilot projects. Additionally, a number of utilities have began implementing “flexibility products and services” such as distributed solar PV inverters, real-time transactions, demand response, and pricing of reserves that would enable them to obtain electricity from the most flexible resources. Response to these market changes, however, depends on adaptations in the utility regulatory landscape. Nevertheless, the dependence of the modern society on a stable and reliable electricity system require that these innovations should be ongoing throughout the lifetime of the electricity grid infrastructure.
\nThe transition from centralized to decentralized renewable electricity governance animates business-model innovations to address “death spiral” concerns and inefficient resource allocation. REV’s core strategy addresses market risks in New York by increasing DER deployment, increasing transparency in utility ownership, incentivizing low-carbon electricity generation, and aligning utility profits with DER deployment [45]. However, as [36, 62] caution, these innovations must not be construed as attempts at regime preservation rather than market adaptations for fostering ‘polycentric’ business-model innovation. In other words, the REV docket’s core strategy positions political and economic innovations of the utility landscape to optimize customer-focused operations and return on environment. For instance, the role of the ESCOs which currently provide only commodity services (e.g., energy efficiency investments) are expanded to include more classes of electricity services including consulting and analytic services to help consumers dynamically manage their energy bills.
\nThe key objective of this chapter was to evaluate the viability of the Hamel business model and its application to evaluating the New York’s REV vision and the state’s path for optimizing distributed energy future and customer choice. The Hamel framework proved to be a valuable analytical business model methodology in this context. The chapter reveals that residential and commercial rooftop solar electricity generation systems is expanding in New York led by behind-the-meter facilities producing power intended for on-site consumption in homes, office facilities, and commercial buildings. Our findings show that New York utilities are increasingly investing in behind-the-meter renewable energy projects. Utilities favor these customer-side projects which recorded the fastest growth in electricity revenues, sales, and customers in 2016 of 89.4, 78.6, and 68.7%, respectively.
\nThe chapter sheds lights on the growing influence of business-model innovations and the New York’s REV docket in optimizing utility customer choice management and distribute system planning of electricity services. This research shows that implementation of the REV vision in a polycentric fashion offers significant benefits to all customers, not just those that subscribe to them, by generating richer innovations in pricing plans, consumer choice management, and customer analytics to improve utility operations and customer satisfaction. The expansion of renewable electricity market in New York would be impossible without support from state and federal policymakers. Although key polices and market regulations including community choice aggregation, net metering, clean energy fund, dynamic load management, low income affordability, and utility energy efficiency proposals have been proposed and even in some cases implemented in NYS to improve the development of distributed utilities and services, significant improvement in regulatory and market reforms is still required to eliminate market, financial, and economic barriers and skewed incentives that presently impede the efficient evolution of the utility sector. One of the key market development needs is thus to emphasize heavily improvement in the utilities’ business-model innovation through external partnerships and suitable organizational structures that promotes an integrated renewable electricity utility market statewide.
\nThis work was supported by the Utility of the Future project that is co-sponsored by the Center for Energy and Environmental Policy (CEEP) at the University of Delaware and the Foundation for Renewable Energy and Environment (FREE).
\nNo potential conflict of interest was reported by the authors.
The extradosed bridge is thought to be a special form of cable-stayed bridge because both bridges use inclined stay cables for supporting the girder load elastically at points along its length in order to increase the span of girder without intermediate piers [1]. The dead and live loads on girders are transferred to towers by axial action of stay cables. Thus, the safety of these kinds of flexible structures is mainly dependent on the safety of stay cables, which is usually assured by introducing a safety factor to provide a margin between theoretical strengths (R) and load effects (S). For instance, the allowable stress (
In this paper, a parametric study is carried out to evaluate the safety factors of stay cables of cable-stayed and extradosed bridges by employing the deterministic and nondeterministic methods at limit states. The effects of various parameters, i.e., cable loss and deterioration of cables due to corrosion, on demand to capacity ratio (DCR) of stay cables are also considered in this study. Finally, it is found that the safety factors in the range of 2.3–2.5 and 1.67 are essential for the safe design of cable-stayed and extradosed bridges, respectively to satisfy the conditions of limit states and target reliability index.
A 3D FE model of a cable-stayed bridge, with a main span length of 460 m, is developed using a FEM software (Midas Civil). The structural configuration of the bridge model is shown in Figure 1. The bridge model is cambered linearly by 2%. The steel box girder is used for this model. The total width and depth of girder are 21.75 m and 3.5 m, respectively with four design lanes of each 3.5 m wide as shown in Figure 2. The configuration of tower is an H-shape composed of steel legs. The total height of tower is 140 m and pylon height (110 m) is taken as 1/4th of the main span length. Moreover, cable-stayed bridge model consists of 144 stay cables (Cs), arranged in a modified-fan style. The anchorage points of stay cables at the bridge deck are located at an interval of 12 m. Tower and girder are modeled as elastic beam elements (168 beams) whereas stay cables are modeled as truss elements (only tension). Fishbone modeling technique is adopted to connect the stay cables with deck spine through rigid links. Moreover, the model is supported by roller supports provided on each end of bridge and piers are assumed to be fixed into firm foundation. All bearings of main girder are movable in longitudinal direction of bridge, i.e., there is no connection between tower and girder at their intersection. The attachments of the cables to tower are pinned. Elastomeric rubber bearings are installed to connect the girder with lower transverse beam through elastic links.
Configuration of cable-stayed bridge model.
Configuration of traffic lanes.
Similar to cable-stayed bridge, a 3D FE model of extradosed bridge, with a main span length of 208 m and two side spans of each 100 m, is developed. The structural configuration of bridge model is shown in Figure 3. The total width and depth of concrete bridge girder are 21.75 m and 4.5 m, respectively with four lanes as already shown in Figure 2. The depth of girder is kept same at the pylon locations as well as at mid-span. The total height of the concrete tower is 40 m and pylon height (20 m) is taken as 1/10th of the main span length. The bridge girder is supported by the piers and a system of 88 stay cables (EDCs) arranged in a modified-fan style. The anchorage points of stay cables (EDCs) at the bridge deck are located at the intervals of 5 m and 6 m on side and main spans, respectively. The connection between tower and girder is assumed to be fixed and monolithic because stress range due to live load in the cables is affected by the girder stiffness and fixity of support on the piers. When the girder is stiff, the stress range in cables due to live load will be small in comparison with permanent loads. To reduce the magnitude of this stress range, girder should be fixed at the piers.
Configuration of extradosed bridge model.
Bridge design loads are referred to Japanese specifications for highway bridges [4] as shown in Table 1. Dead loads are applied uniformly on entire spans whereas B-live loads (concentrated live load: P1 and uniformly distributed load: P2) are applied only on main spans of both bridges. The material and sectional properties of bridge components are also shown in Tables 2 and 3, respectively.
Dead load, DLCSB (kN/m) | Self-weight of deck | 48.5 | |
Pavement loads | 34.2 | ||
Additional loads | 4.85 | ||
Dead load, DLEDB (kN/m) | Girder self-weight | 335 | |
Pavement loads | 34.2 | ||
Additional loads | 4.85 | ||
Live load, LL (kN/m) | Concentrated load | P1 | 97.5 |
Uniformly dist. Load | P2 | 29.3 | |
Pedestrian load | PL | 10 |
Design loads.
Properties | Stay cables of CSB | Stay cables of EDB |
---|---|---|
1860 | 2000 | |
1302 | 1400 | |
744 | 1200 | |
195 | 195 | |
0.3 | 0.3 | |
77 | 77 |
Material properties of stay cables.
Members | Deck | Pylon | Pier | Transverse beam | |
---|---|---|---|---|---|
CSB | A (m2) | 0.59 | 1.11 | 1.11 | 0.55 |
Ixx (m4) | 14.73 | 7.96 | 7.96 | 2.61 | |
Iyy (m4) | 5.13 | 6.24 | 6.24 | 2.14 | |
Izz (m4) | 29.03 | 4.72 | 4.72 | 1.52 | |
EDB | A (m2) | 13.54 | 6 | 12 | 6 |
Ixx (m4) | 168.62 | 4.7 | 19.44 | 4.7 | |
Iyy (m4) | 54.22 | 4.5 | 16 | 4.5 | |
Izz (m4) | 683.84 | 2 | 9 | 2 |
Sectional properties of bridge components.
Preliminary design of stay cables of cable-stayed bridge (Cs) is carried out by assuming a safety factor of 2.5 against
Cross-sectional areas of stay cables of cable-stayed bridge.
Similar to cable-stayed bridge, the preliminary design of stay cables of extradosed bridge (EDCs) is also carried out by using a safety factor of 1.67. For the calculation of initial pretension forces (PS) of stay cables, the continuous beam method is applied. Hit and trial method is used to find the ideal and balanced state of extradosed bridge under dead loads. Many iterations are performed to optimize the bending moment and cable forces, and cross-sectional areas of stay cables are calculated accordingly as shown in Figure 5. In extradosed bridge, the prestress force (Pi) is also applied to the concrete girder. Full pre-stressing of the girder is not feasible. Since only concentric pre-stressing can be used locally in the girder (eccentric pre-stressing causes a secondary bending moment as large as the primary bending moment), a prestress force (Pi) of 200,000 kN is required at main span and some portion of side span to keep the girder un-cracked. Pi is required to minimize the deflection and to resist the bending moments due to long-term effects and live loads.
Cross-sectional areas of stay cables of extradosed bridge.
Nonlinearity effects including cable sag effect due to self-weight of stay cables and P-Delta effects due to interaction of deck and tower are also considered in the analysis of both bridge types. Reduced or equivalent modulus of elasticity of stay cables is determined by:
Eq. (1) is known as Ernst’ formula in which Eeq is equivalent modulus of elasticity, E is effective material modulus of elasticity, A is cross-sectional area of stay cable, w is cable weight per unit length, L is horizontal projected length and T is tensile force in stay cable.
For the evaluation of safety factor of stay cables at fatigue limit state, moving load analysis is performed by applying fatigue design load (T-load: 200 kN) to the cable-stayed and extradosed bridge models. Then, influence line diagrams (ILDs) of axial forces in stay cables are drawn by using Breslau Muller Principle and maximum and minimum design variables are calculated. Figure 6 shows the ILDs of axial forces of stay cables (C1 and EDC1) of cable-stayed and extradosed bridges, respectively. It is observed that the area under ILD of C1 is larger than that of EDC1 under the same fatigue load which indicates that extradosed bridge is less influenced by fatigue load as compared to cable-stayed bridge. Subsequently, cable reversal stresses and design stress range
ILDs of axial forces of stay cables C1 and EDC1.
where
where ∆σCE is the basic allowable stress range or cut off limit for constant amplitude stress which is taken as 270 MPa and 200 MPa for parallel wire strand type stay cables of cable-stayed and extradosed bridges, respectively at 2 million load cycles based on the standard SN or Wohler’s curves of cables and CR is correction factor for mean stress which can be calculated as:
which
Figures 7 and 8 compare the fatigue stress demand to capacity ratios (DCRs) of stay cables of cable-stayed and extradosed bridges, respectively. In case of cable-stayed bridge, stay cable C15 shows maximum DCR under fatigue design load and there is a hefty variation in DCR of stay cables depending on their locations with respect to tower-deck intersection. From Figure 7, it can be concluded that a minimum safety factor of 2.2 is necessary to satisfy the fatigue limit state.
Effect of fatigue load on DCR of stay cables of cable-stayed bridge.
Effect of fatigue load on DCR of stay cables of extradosed bridge.
In case of extradosed bridge, all stay cables (EDCs) exhibit almost same DCR irrespective of their locations with respect to tower-deck intersection. Figure 8 also shows that the safety factor of 1.67 satisfies the fatigue limit state. From probabilistic point of view, the safety of stay cables under the fatigue limit state is verified by satisfying the Palmgren-Miner hypothesis which states that fatigue failure of stay cables occurs when the accumulated damage exceeds one,
After evaluation of safety factor of stay cables at fatigue limit state, the safety factor is further evaluated and verified at ultimate limit state. For that, following equation should be verified [9]:
where
where the subscripts CSB and EDB are cable-stayed and extradosed bridges, respectively, DC is dead load (components and attachment), DW is dead load (wearing surface and utility), PS is pretension force, Pi is prestress force, LL is live load and IM is dynamic load allowance. In case of extradosed bridge,
In design viewpoint of long-span cable-supported bridges, PTI [12] suggests two methods. The first method consists of a simplified quasi-static analysis of cable-supported bridge with a missing cable under factored dead and live loads. These loads are combined with the static cable loss dynamic impact force (CLDF) resulting from the sudden breakage of a cable with the additional load factor of 1.1 on CLDF. In second method, PTI allows the usage of a dynamic analysis to compute the structural response more accurately due to an abrupt cable failure. However, little guidance is provided by PTI on how to conduct such a dynamic analysis. That is why, first method is selected in this paper for the sake of simplification.
The dynamic cable force is applied as an equivalent static force in the correct orientation on both anchorage points of cable by considering CLDF of 2.0 in the load combination. Following the aforementioned approach, the effects of cable loss on DCR of stay cables of cable-stayed and extradosed bridges are investigated thoroughly. Figure 9 compares the DCR of stay cables of the cable-stayed bridge with and without sudden loss of single and multiple stay cables at different safety factors. It can be observed from Figure 9 that loss of two cables (C35&36) yields maximum DCR in the adjacent stay cables. This multiple cable loss event can also trigger the progressive collapse of the entire cable-stayed bridge.
Effect of cable loss on DCR of stay cables of cable-stayed bridge. (a) Safety factor of 2.5, (b) safety factor of 2.3, and (c) safety factor of 2.2.
Moreover, Figure 9 also depicts that with the decrease of safety factor of stay cables, DCR increases accordingly and a minimum safety factor of 2.3 is essential to meet the requirements of ultimate limit state. Similarly, the effects of cable loss on DCR of EDCs are also investigated as shown in Figure 10. It is observed that the loss of two cables (EDC1&2) yields maximum DCR of EDCs and a safety factor of 1.67 is compulsory under normal loading condition which should be increased to achieve higher safety under extreme damaging condition.
Effect of cable loss on DCR of stay cables of extradosed bridge. (a) Safety factor of 1.67 and (b) safety factor of 1.75.
In addition to that, the effect of corrosion as well as the combined effect of corrosion and cable loss on DCR of C1 and EDC1 are also examined at different safety factors in this study. For that, a simple corrosion model is adopted by introducing the uniform corrosion of 10% throughout the cable length as a change in cable area. The effective modulus of elasticity of corroded cable is determined and static analyses are performed. Figure 11 shows that DCR of C1 is greater than 1.0 at a safety factor of 2.4 which indicates that the safety factor of 2.5 is the minimum factor required to avoid the rupture of C1. On the other hand, DCR of EDC1 is greater than 1.0 even at a safety factor of 1.67 which elucidates that a minimum safety factor of 1.75 is essential under extreme loading condition for the safe design of extradosed bridges.
Effect of corrosion and, combined effect of cable loss and corrosion on DCR of C1 and EDC1.
With the development of reliability-based methods, it has become evident that the traditional deterministic finite element method is not sufficient to properly design advanced structures or structural components subjected to a variety of complex loading conditions. Therefore, uncertainties in loads, material behavior and geometric configuration must be considered to provide rational reliability analysis and to describe the structural behavior with higher level of confidence.
In this paper, the safety factors of stay cables are also assessed by the nondeterministic method. For that, a probabilistic based reliability analysis code is prepared based on the mean value first order second moment (MVFOSM) reliability method. Basic random variables used for this program are material strength, dead loads and live loads. One million samples of normally distributed random variables are generated by using Monte Carlo simulation technique. The coefficient of variations (COV) of random variables are taken from the Ref. [13]. The program calculates the cable force (
For the acceptable values of probability of safety of structures, United States Army Corps of Engineers (USACE) suggests that the estimated reliability indices should be at least 3.0 (for above average performance) and 4.0 (for good performance) [14]. Based on it, the calculations of reliability index and failure probability for both bridge types are carried out and shown in Tables 4 and 5. These tables clarify that reliability index decreases when safety factor decreases from 2.5 to 2.2 in case of cable-stayed bridge. For instance, the safety factors of 2.5, 2.3 and 2.2 yield the reliability indices of 8.17, 5.04 and 2.91 for C1, respectively. Similarly, in case of extradosed bridge, the reliability index increases as safety factor increases from 1.60 to 1.85 for EDC1. The reliability analysis results also show that the safety factors of 2.3 and 1.67 yield the target reliability index greater than 4.0 for good performance of both bridge types. Based on these results, the optimum safety factors of C1 and EDC1 are calculated graphically as shown in Figures 12 and 13, respectively. It is observed that the safety factors of 2.25 and 1.66 yield the target reliability index of 4.0 and failure probability of 10−5 for stay cables C1 and EDC1, respectively. This also elucidates that the safety factor of 1.66 for extradosed bridges yields same reliability index as the safety factor of 2.25 for cable-stayed bridges.
Safety factor | ||
---|---|---|
2.5 | 8.17 | 1.48 × 10−16 |
2.4 | 6.79 | 5.31 × 10−12 |
2.3 | 5.04 | 2.36 × 10−7 |
2.2 | 2.91 | 1.8 × 10−3 |
Reliability analysis results of C1 of cable-stayed bridge.
Safety factor | ||
---|---|---|
1.60 | 1.9 | 2.84 × 10−2 |
1.67 | 4.37 | 6.03 × 10−6 |
1.75 | 6.81 | 4.66 × 10−12 |
1.85 | 9.32 | 5.76 × 10−21 |
Reliability analysis results of EDC1 of extradosed bridge.
Graphical evaluation of safety factor of C1 of cable-stayed bridge.
Graphical evaluation of safety factor of EDC1 of extradosed bridge.
In this paper, a parametric study on safety factor of stay cables of cable-stayed and extradosed bridges is carried out by using deterministic and nondeterministic methods. Following conclusions can be drawn from this study:
Finite element analysis results show that cable-stayed and extradosed bridges are sufficiently redundant at safety factors ranging from 2.3 to 2.5 and 1.67, respectively under normal loading conditions. For cable-stayed bridges, ultimate strengths of stay cables are more critical than their fatigue strengths and a minimum safety factor of 2.3 is essential to satisfy the fatigue and ultimate limit states. However, in case of extradosed bridges, the ultimate strengths of stay cables are even more critical than their fatigue strengths and a minimum safety factor of 1.67 is indispensable to meet the limit state design requirements under normal loading conditions and it should be increased under extreme damaging conditions.
The reliability analysis results elucidate that a minimum safety factor of 2.25 is necessary for stay cables of cable-stayed bridge to achieve the target reliability index of 4.0. Whereas, in case of extradosed bridge, a safety factor of 1.67 yields the reliability index greater than 4.0 and a minimum safety factor of 1.66 is essential for the safe design of extradosed bridges. Moreover, the safety factor of 1.66 for extradosed bridges yields same reliability index as the safety factor of 2.25 for cable-stayed bridges.
The optimum safety factors evaluated by nondeterministic method are close to those obtained by deterministic finite element method. These outcomes imply that the structural reliability solutions for stay cables are rational and correct.
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