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Abstract: Presents the front cover for this issue of the publication. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Tao Hong;
Pages: 587 - 588 Abstract: The papers in this special section focus on data analytics for energy, water, and environment. In 2008, the U.S. National Academy ofEngineering proposed 14 grand challenges for engineering, of which five were related to energy, water, and environment. The world population has exceeded 7.7 billion in 2020, up from 6.7 billion in 2008. A common goal of the human beings is to meet the global need of energy and water without sacrificing the environment. Agrowing interest in the recent decade is data analytics. Many business sectors have embarked analytics to extract actionable insights from the data and make informed business decisions. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Manzhi Liu;Liyuan Liu;Mengqian Shi;Gang He;Shiru Zhang;Mingzhu Shi;Yue Ren;Bowen Luan;
Pages: 589 - 601 Abstract: Comparative feedback is a focus on existing research, while studies on the psychological mechanisms of its effect on consumers’ energy-saving behavior remain lacking. This article considers that the social comparison orientation of feedback has a significant impact on consumers’ energy-saving behavior and intention through a mediated moderation mechanism. A field experiment of 2 (comparison orientation: upward comparison, downward comparison) × 2 (self-construal: independent self-construal, interdependent self-construal) between-subjects design with taking college student dormitories as the sample shows that consumers can experience higher psychological reactance when they receive energy consumption information through upward comparison than when they do so through downward comparison. The partly negative mediating role of psychological reactance is moderated by self-construal. In the context of independent self-construal, comparative feedback stimulates individual's psychological reactance to the intention to reduce energy-saving, while in the context of interdependent self-construal, it does not. This article has reference value for developing accurate feedback strategies for different types of energy consumers. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Chia-Yen Lee;Wei-Chun Sun;Yueh-Heng Li;
Pages: 602 - 615 Abstract: During the past decade, the demand for biofuel has rapidly increased because of fossil fuel scarcity and pollution, which has resulted in the accelerating commercialization of the global biofuel markets with a potential 500 exajoules per year by 2050. This paper evaluates the economic benefits of five biomass types and proposes a stochastic programming model for the optimal design of the global biodiesel supply chain under the demand and price uncertainty. The biomass is produced in Southeast Asia and oil is extracted. Then, the vegetable oil is shipped to Europe and North America for biodiesel conversion and fulfilling the local demand. The results show that palm tree is a cost-effective clean energy with high benefits and low-carbon emissions. Based on the economic evaluation of biomass, the optimal capital investment, allocation of cultivated area, and transportation paths are identified to maximize the expected global profit in this supply chain in 2008–2012 by the proposed supply chain optimization model. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Tao Zhang;Yingchi Qu;Gang He;
Pages: 616 - 627 Abstract: The energy consumption disparity is an important factor affecting product pricing. The risk and the cost of new energy products are generally higher than those of traditional energy products, which results in conflicting pricing strategies between manufacturers and retailers. Applying the concepts from dynamic cooperative game theory, such as improved Shapley value, in this paper, we have studied the optimal pricing strategies of manufacturers and retailers in a cooperative and a noncooperative game. Based on policies and market conditions in China, we have used a logistic regression model to determine the impact of product sales, devised pricing strategies affected by the relevant government tax, and subsidy policy. The results show that, first, under the basic condition in which the manufacturer obtains profit, the optimal pricing of the retailer in a noncooperative game is more than that in a cooperative game. Second, the differential coefficient of the energy consumption of the two products in the cooperative game is greater than that in the noncooperative game. Third, the cooperative game not only plays an incentivizing role for retailers in setting reasonable prices of new energy products but also encourages manufacturers, producing a greater difference of energy consumption between traditional energy products and new energy products, which further reduces the consumption of new energy products. Finally, the strategies of sharing the green marketing costs by manufacturers and retailers can improve the total profits of the supply chain in addition to their own. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Tony Castillo-Calzadilla;Cristina Martin Andonegui;Mikel Gómez-Goiri;Ana M. Macarulla;Cruz E. Borges;
Pages: 628 - 638 Abstract: The methodology presented in this article tries to make a water-energy auditing so as to design the same water network characteristics but being supplied by photovoltaic (PV). We identify which data would be required to define the most effective process to follow in order to reach better water and energy consumption efficiency rates. An analytical approach that deals with the optimal sizing of a solar pumping system in direct current microgrid is proposed. This means the combination among a solar PVs system, a storage system, and a converter system that deals with the energy harvesting and charging the batteries used for supporting the pumping water system. The methodology has been validated by two real pumping systems of Zarautz distribution network (Basque Country region). It has been modeled and tested under four scenarios. The results of the simulations carried out have clearly demonstrated both the feasibility and the savings achieved, in terms of the environmental and economic assessment. This will help the water distribution managers to take more informed decisions and design cost-effective strategies while contributing with more sustainable cities. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Scott Zuloaga;Puneet Khatavkar;Larry Mays;Vijay Vittal;
Pages: 639 - 655 Abstract: New metrics and a new methodology for determining system infrastructural–operational resilience are presented for the optimal real-time operation of two critical interdependent infrastructure systems—the water distribution systems (WDS) and the electric power system (EPS) under critical conditions of limited water and/or limited electrical energy resulting from extreme drought or electric grid failure, respectively. Operational resilience (OR) and infrastructural resilience are defined. The integrated resilience computation method is presented and utilizes results from a combined optimization–simulation framework, which involves capturing the interactions and associated dynamics between the EPS and WDS. A realistic example of the WDS and EPS is used to demonstrate the application of the resilience concepts to assess the interdependent systems’ performance. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Chunguang Bai;Joseph Sarkis;
Pages: 656 - 670 Abstract: The water, energy, and food (WEF) nexus has gained particular attention in the sustainable development community. Making effective decisions in this environment is difficult. Entwinement from multiple sustainability dimensions and various stakeholder perspectives contribute to this difficulty. Stakeholders have differing goals, interests, and preferences for potential technological and developmental solutions that address WEF nexus concerns. A holistic sustainable management approach with supporting decision support can address these concerns. This article introduces such a holistic framework to address WEF-sustainability (WEFS) concerns. Using a joint neighborhood rough set, interval-valued hesitant fuzzy set, and regret theory technique, this article introduces a multistakeholder transdisciplinary method to support WEFS nexus decisions. An illustrative example integrates the multiple stakeholder and WEFS nexus factors. The illustrative example provides insights into the modeling effort and data requirements. Research, practical implications, along with limitations of the study—which are all discussed—provide a foundation for future research directions in this socio-environmentally important field. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Junjie Lin;Jie Song;Chao Lu;
Pages: 671 - 681 Abstract: Transmission line parameters arefundamental parameters of power system models that influence the safety, stability, and economy of power utility grids. Transmission line parameter estimation is a basic and necessary function and module of a power grid energy management system. However, traditional offline parameter estimation methods cannot cope well with parameter uncertainties caused by transmission line aging, environmental factors, and modern power grid developments. The widespread deployment of phasor measurement units (PMUs) in power networks has paved the way for accurate real-time-measurement-based transmission line parameter tracking. This article proposes PMU-based parameter estimation models considering series and shunt compensators. An online parameter estimation method is developed based on recursive least squares with a variable forgetting factor, which can adapt to various parameter mutations and has strong robustness to outlier measurements. This article also analyzes the sensitivity of parameters with respect to synchrophasor data. The simulation results under different scenarios verify the effectiveness, robustness, and adaptivity of the proposed method, which exhibits significant advantages over other existing parameter estimation methods. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Arijit Sen;Yueming Qiu;
Pages: 682 - 696 Abstract: Using nationally representative household survey data administered by the U.S. Energy Information Administration, in this article, we attempt to analyze the aggregate behavior of households in terms of usage of appliances with explicit temperature control mechanism and the adoption of energy-efficient variants of other appliances. A multivariate probit analysis suggests that the households with larger size, higher income, and higher level of education are more likely to use smart thermostat to control temperature and purchase energy-efficient appliances. To identify the broad classes of household behavior, latent class analysis specifications are used. The optimal specification indicates that there are four broad classes of households. Consistent with the results of the multivariate probit specification, we find that the increased odds of belonging to the smart thermostat/energy-efficient appliance owner category of households over the no control/no energy-efficient appliance owner are related to variables, such as household type, size, and income. Therefore, targeting renters, apartment dwellers, and lower income households through appropriate household incentives and residential regulations are likely to improve outcomes in the adoption of efficient appliances and temperature control strategies. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Yaze Li;Jingxian Wu;
Pages: 697 - 707 Abstract: This article discusses optimum designs of photovoltaic (PV) systems with battery energy storage system (BESS) by using real-world data. Specifically, we identify the optimum size of PV panels, the optimum capacity of BESS, and the optimum scheduling of BESS charging/discharging, such that the long-term overall cost, including both utility bills and the PV system, is minimized. The optimization is performed by considering a plethora of parameters, such as energy usage, energy cost, weather, geographic location, inflation, and the cost, efficiency, and aging effects of solar panels and BESS. To capture the impacts of long-term factors such as aging effects, inflation, and discounted economic returns, the problem is formulated as a mixed integer nonlinear programming (MINLP) problem over the time horizon covering the entire life cycles of solar panels and BESS of the order of ten years or longer, whereas almost all existing works on PV system designs consider much shorter time horizons of the order of days or weeks. The MINLP is transformed into mixed integer linear programming (MILP) and solved by branch-and-bound (B&B) algorithm. The complexity of MILP is high due to the long time horizon. A new low-complexity algorithm is then proposed by using dynamic programming, where it is shown that the MINLP problem can be transformed into one that satisfies Bellman’s principal of optimality. Applying the newly developed algorithms on real-world data from a commercial user in San Francisco reveals that the system achieves the break-even point at the 66th month and achieves a 29.3% reduction in total system cost. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Farzaneh Pourahmadi;Seyed Hamid Hosseini;Payman Dehghanian;Ekundayo Shittu;Mahmud Fotuhi-Firuzabad;
Pages: 708 - 719 Abstract: The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Jiawei Zhang;Yi Wang;Mingyang Sun;Ning Zhang;
Pages: 720 - 728 Abstract: The integration of distributed renewable energy and the implementation of the demand response complicate the change patterns of load profiles and present great uncertainties. Probabilistic load forecasting, an effective method for capturing the future load uncertainty, has been a hot issue. This article proposes a two-stage bootstrap sampling method for probabilistic load forecasting. In the first stage, alpha-bootstrap, which is a modification of traditional bootstrap methods, is applied to characterize the uncertainties from multiple forecasting models; in the second stage, the residual bootstrap method is used to formulate the regression errors. Finally, the probabilistic load forecasts can be obtained by integrating the uncertainties in both stages. Various off-the-shelf point load forecasting methods such as random forest (RF) and gradient boosting regression tree (GBRT) can be integrated into the proposed framework. We illustrate the effectiveness of our proposed method and superiority over direct quantile regression methods such as quantile RF and quantile GBRT using the case studies on open load datasets of eight zones in ISO New-England. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Michele R. B. Malinowski;Richard J. Povinelli;
Pages: 729 - 741 Abstract: This article addresses the need to divide a population of water utility customers into groups based on their similarities and differences, using only the measured flow data collected by water meters. After clustering, the groups represent customers with similar consumption behavior patterns and provide insight into “normal” and “unusual” customer behavior patterns for individually metered water utility customers within North America. The contributions of this work not only represent a novel work, but also solve a practical problem for the utility industry. This article introduces a method of agglomerative clustering using information theoretic distance measures on Gaussian mixture models within a reconstructed phase space, designed to accommodate a utility's limited human, financial, computational, and environmental resources. The proposed weighted variation of information distance measure for comparing Gaussian mixture models emphasizes those behaviors whose statistical distributions are more compact over those behaviors with large variation and contributes a novel addition to existing comparison options. We conduct several experiments with both synthetic and real data to show the reasonableness of the clustering results and their consistency. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Yuan Zhou;Rong Zhou;Luyi Chen;Yun Zhao;Qintian Zhang;
Pages: 742 - 754 Abstract: Policy mixes integrate distinct policy instruments into one coherent policy package as a measure to avoid the potentially counterproductive impacts of any given policy on another. In China, local governments recently have pursued policy mix approaches to resolve the existing policy conflicts, which are associated with the debates over the development priorities between manufacturing productivity growth and environmental mitigations. However, there has been little research that has empirically examined the effects of policy mixes, especially those concerned with the dual-faceted requirements that involve both productivity and the control of emissions. The study presented here employs a model designed to examine firm-level total factor productivity (TFP) and green TFP as performance measures. This article assesses the impacts of municipal-level environmental policy mixes on the textile industry in 13 Chinese cities by using the difference-in-differences technique to analyze the microfirm panel data of 12 133 Chinese textile firms during the period from 1998 to 2012. The results show that these environmental policy mixes could significantly promote green performance without compromising productivity growth. The results also indicate that the impacts varied in different cities. This article offers insight into policy mixes and industrial policy literature as well as practical guides for policymakers and industrialists. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Ruiyang Jin;Chao Lu;Jie Song;
Pages: 755 - 764 Abstract: The stable, efficient and low-cost operation of the grid is the basis for the economic development. The amount of power generation and power consumption must be balanced in real time. Traditionally the grid needs to quickly detect the electrical load of users in real time and adjust the power generation to maintain the balance between electrical supply and demand, which brings great cost from the output modulation of the generator set. This article focuses on the distributed battery energy storage systems (BESSs) and the power dispatch between the generators and distributed BESSs to supply electricity and reduce electrical supply costs. The cost analysis of electrical supply from the generators and BESSs is proposed. Then, this article introduces a consensus control algorithm (CCA) to dispatch the power output and track the load in a decentralized manner. A nonuniform CCA (NCCA) is proposed to improve the convergence speed especially when the power of BESSs reach upper/lower bounds. Finally, the case study implemented in a 5-bus system with the real load data of a district in Beijing verifies the effectiveness and efficiency of the proposed method. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Tao Hong;Alex Hofmann;
Pages: 765 - 772 Abstract: In developed countries, the electric grid and the Internet are societal necessities. In developing countries, both have grown rapidly during the past few decades. The operational benefits from adding digital and communications overlays to the power grid and the resulting coevolution of the two technologies have expose the power systems to potential cyberattacks. The outage management system (OMS) is a crucial component of outage restoration and reliability planning. In this article, we propose a new research topic on data integrity attacks to OMSs. We discuss the scenarios of such attacks, their consequences, and the means to detect and mitigate the attacks. We relate this proposed research topic to the recent progress in three areas, state estimation, load forecasting, and outage prediction. Finally, we discuss the practical challenges and future research directions. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Kim-Kwang Raymond Choo;Sercan Ozcan;Ali Dehghantanha;Reza M. Parizi;
Pages: 773 - 775 Abstract: Presents the introductory editorial for this issue of the publication. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Henry M. Kim;Hjalmar Turesson;Marek Laskowski;Amir Fard Bahreini;
Pages: 776 - 791 Abstract: Blockchains can be public, permissionless networks implementing novel cryptocurrency-based technology features or permissioned, interorganizational networks championed by industry consortia. Some ventures operationalize a hybrid of these two network types to enhance adoption of their blockchain platforms by broadening their base of stakeholders or facilitating interoperability between heterogeneous blockchains. In this article, we synthesize literature and industry writings to identify four hybrid blockchain architectures: hybrid blockchain approach, connected hybrid blockchain, interoperable blockchain architecture, and hard-forked blockchain for enterprise use. We then analyze these architectures along dimensions of semantic modeling support between private and public networks, data connectivity between networks, syntactic interoperability support between networks with heterogeneous codebases, governance model, and technical features. We find that hybrid blockchain ventures make trade-offs: support API's, tools, and customized development so that a codebase is useful for private and public networks or provide such support for interoperation between heterogeneous codebases. We then conduct a case study of an exemplar for a hybrid blockchain approach, the startup Insolar. We identify characteristics that have led Insolar to be idiosyncratically agile and effective in its blockchain development, which together with our architecture analysis may be timely and prescriptive as enterprises grow interested in addressing blockchain hybridity and interoperability. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Sercan Ozcan;Serhan Unalan;
Pages: 792 - 809 Abstract: Blockchain is considered to be a general-purpose technology (GPT) by many scholars. However, previous studies offer no proof that Blockchain is a GPT. Thus, approximately 2500 Blockchain-related patent data are investigated by deploying the mixed-method approach, using patentometrics with the support of semi-structured interviews conducted with Blockchain experts. This article investigates six main GPT indicators: pervasiveness, improvement, spawning, prevalence, reallocation of resources, and inclusive democratization. Overall, the results demonstrate that Blockchain has not yet become a GPT, though it already shows some GPT characteristics. There are six specific findings: 1) Blockchain shows pervasive characteristics; 2) Blockchain is capable of further improvement; 3) Blockchain facilitates and encourages the creation of innovations; 4) several countries with strong R&D capabilities, particularly China and the United States, are showing the prevalence of Blockchain technology; 5) the Blockchain landscape is witnessing greater participation of “younger” companies; and 6) Blockchain is strongly related to the Information and Communication Technology domain with the potential of inclusivity and democratization. China and the United States have the potential to influence the future development of Blockchain technology. This article is assumed to be of great interest to a broad spectrum of stakeholders, such as scholars and policymakers. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
João Martins;Manuel Parente;Mário Amorim-Lopes;Luís Amaral;Gonçalo Figueira;Pedro Rocha;Pedro Amorim;
Pages: 810 - 824 Abstract: Firms have available many forms of collaboration, including cooperatives or joint ventures, in this way leveraging their market power. Customers, however, are atomic agents with few mechanisms for collaborating, leading to an unbalanced buyer-supplier relationship and economic surpluses that shift to producers. Some group buying websites helped alleviate the problem by offering bulk discounts, but more advancements can be made with the emergence of technologies, such as the blockchain. In this article, we propose a customer-push e-marketplace built on top of Ethereum, where customers can aggregate their proposals, and suppliers try to outcompete each other in reverse auction bids to fulfil the order. Furthermore, smart contracts make it possible to automate many operational activities, such as payment escrows/release upon delivery confirmation, increasing the efficiency along the supply chain. The implementation of this network is expected to improve market efficiency by reducing transaction costs, time delays, and information asymmetry. Furthermore, concepts such as increased bargaining power and economies of scale, and their effects in buyer-supplier relationships, are also explored. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Authors:
Michael Wustmans;Thomas Haubold;Bennet Bruens;
Pages: 825 - 837 Abstract: The evaluation of various innovation fields of an emerging technology as well as their potential impact on a certain market, region, industry, or target group is a part of an innovation manager's day-to-day business. Such evaluations are usually based on a combination of information from a variety of data sources, which are used to decide whether to invest in the advancement or adoption of a technology. With the aim of supporting this decision-making process, we combine different data sources to identify and evaluate innovation fields by semantically bridging trend and patent data. We apply our method in the context of blockchain technology, show how trend data can be used and operationalized to identify innovation fields, and illustrate how patent data can be used to evaluate these innovation fields. Our data reveals that trend and patent data complement each other and that hybrid or multihybrid approaches to evaluate a technology's development lead to additional insights for the systemic anticipation of future perspectives as well as research pathways of innovation fields. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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Pages: 838 - 838 Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. PubDate:
June 2022
Issue No:Vol. 69, No. 3 (2022)
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