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Journal articles on the topic 'Electric utilities Victoria Planning'

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Published: 10 December 2022
Last updated: 29 January 2023

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1

Hirst, Eric. "Integrated resource planning at electric utilities: The planning process." Evaluation and Program Planning 12, no. 3 (January 1989): 213–23. http://dx.doi.org/10.1016/0149-7189(89)90032-3.

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2

Ammons, J. C., and L. F. McGinnis. "A Generation Expansion Planning Model for Electric Utilities." Engineering Economist 30, no. 3 (January 1985): 205–26. http://dx.doi.org/10.1080/00137918508902908.

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3

Hirst, E., and C. Goldman. "Creating the Future: Integrated Resource Planning for Electric Utilities." Annual Review of Energy and the Environment 16, no. 1 (November 1991): 91–121. http://dx.doi.org/10.1146/annurev.eg.16.110191.000515.

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4

Hirst, E., and C. Goldman. "Key issues in integrated resource planning for electric utilities." IEEE Transactions on Power Systems 5, no. 4 (1990): 1105–11. http://dx.doi.org/10.1109/59.99359.

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5

Mehta, Vishal K., Omar Aslam, Larry Dale, Norman Miller, and David R. Purkey. "Scenario-based water resources planning for utilities in the Lake Victoria region." Physics and Chemistry of the Earth, Parts A/B/C 61-62 (January 2013): 22–31. http://dx.doi.org/10.1016/j.pce.2013.02.007.

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6

Hirst, Eric. "Reforming Electric Utility Regulation: The Engineer as Anthropologist." Practicing Anthropology 16, no. 2 (April 1, 1994): 27–30. http://dx.doi.org/10.17730/praa.16.2.v17026m775810l51.

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Trained as an engineer and employed at a national laboratory, I have been working on new forms of planning for electrical utilities. To learn how environmental groups have influenced utility company decisions, I spent a year (July 1992 through June 1993) working with the Energy Project of the Land and Water Fund of the Rockies (LAW Fund). The LAW Fund provides legal support to local environmental groups throughout the Rocky Mountain region. The LAW Fund's Energy Project focuses on the use of demand-side management programs, renewable resources, and integrated resource planning as ways for utilities to deliver desired energy services to their customers at minimal environmental cost. (Demand-side management programs are those that affect the amount and timing of customer electricity use, such as energy audits.)
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7

Hadley, Stan, and Eric Hirst. "How integrated resource planning for US electric utilities affects shareholder interests." Utilities Policy 5, no. 1 (January 1995): 37–45. http://dx.doi.org/10.1016/0957-1787(95)00012-o.

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8

Pratt, Bonnie Wylie, Jon D. Erickson, Jane Kolodinsky, Erik Monsen, and William J. Wales. "Shades of Green: Modelling Differences in Thought and Action among Electric Utility Regime Actors in the Energy System Transition." Sustainability 14, no. 20 (October 16, 2022): 13287. http://dx.doi.org/10.3390/su142013287.

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There is wide variability in how organizations approach sustainability and the energy system transition toward using more renewables. In the electric power industry, while some distribution utilities have leaned into the transition, others have taken a more conservative approach. Grounded in an institutional resource-based perspective, this multi-level study examines key intra-firm, firm, and individual leadership factors that impact an organization’s commitment to renewables. Sustainability orientation in the power industry is assessed as the percent of renewable energy in a utility’s fuel mix compared with their expressed commitment to renewables and energy efficiency within planning documents. Through computer-aided text analysis, characteristics of 170 electric utilities in the United States were analyzed to predict sustainability orientation. Results indicate that rurality, deregulation, and the entrepreneurial orientation of a utility, as expressed within their Integrated Resource Plans, explain a significant amount of variability in the sustainability orientation of electric utilities.
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9

J. Osborne, Richard. "The effect of alternative cost recovery methods on planning by electric utilities." Resources and Energy 7, no. 1 (March 1985): 49–58. http://dx.doi.org/10.1016/0165-0572(85)90005-2.

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10

Cavanagh, Ralph. "The future of America's electric utilities: Reconciling deregulation and least-cost planning." Electricity Journal 4, no. 4 (May 1991): 20–29. http://dx.doi.org/10.1016/1040-6190(91)90151-i.

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11

Carvallo, Juan Pablo, Alan H. Sanstad, and Peter H. Larsen. "Exploring the relationship between planning and procurement in western U.S. electric utilities." Energy 183 (September 2019): 4–15. http://dx.doi.org/10.1016/j.energy.2019.06.122.

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12

Murugan, Raji, and Raju Ramasamy. "Failure analysis of power transformer for effective maintenance planning in electric utilities." Engineering Failure Analysis 55 (September 2015): 182–92. http://dx.doi.org/10.1016/j.engfailanal.2015.06.002.

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13

Lo, E. O., R. Campo, and F. Ma. "Decision Framework for New Technologies: A Tool for Strategic Planning of Electric Utilities." IEEE Power Engineering Review PER-7, no. 11 (November 1987): 39. http://dx.doi.org/10.1109/mper.1987.5526900.

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14

Lo, E. O., R. Campo, and F. Ma. "Decision Framework for New Technologies: A Tool for Strategic Planning of Electric Utilities." IEEE Transactions on Power Systems 2, no. 4 (1987): 959–67. http://dx.doi.org/10.1109/tpwrs.1987.4335286.

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15

Çağnan, Zehra, Rachel A. Davidson, and Seth D. Guikema. "Post-Earthquake Restoration Planning for Los Angeles Electric Power." Earthquake Spectra 22, no. 3 (August 2006): 589–608. http://dx.doi.org/10.1193/1.2222400.

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This paper describes the application of a new discrete-event-simulation model of the post-earthquake electric power restoration process in Los Angeles. The findings are that (1) Los Angeles residents may experience power outages lasting up to 10 days; (2) what we call the power rapidity risk (the joint probability distribution of restoration of a specified number of customers in a specified amount of time) varies throughout the area; (3) there is a relatively high likelihood that more repair materials than are currently available will be required if a large earthquake occurs; and (4) there are ways to reduce the expected duration of earthquake-initiated power outages and they should be subjected to cost-benefit analysis. These results should be useful to utilities and emergency planners in Los Angeles. The new simulation modeling approach could be used in other seismically active cities to gain insights into the restoration process that other modeling approaches cannot provide.
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16

Turner, Matthew, Yuan Liao, and Yan Du. "Comprehensive Smart Grid Planning in a Regulated Utility Environment." International Journal of Emerging Electric Power Systems 16, no. 3 (June 1, 2015): 265–79. http://dx.doi.org/10.1515/ijeeps-2014-0099.

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Abstract This paper presents the tools and exercises used during the Kentucky Smart Grid Roadmap Initiative in a collaborative electric grid planning process involving state regulators, public utilities, academic institutions, and private interest groups. The mandate of the initiative was to assess the existing condition of smart grid deployments in Kentucky, to enhance understanding of smart grid concepts by stakeholders, and to develop a roadmap for the deployment of smart grid technologies by the jurisdictional utilities of Kentucky. Through involvement of many important stakeholder groups, the resultant Smart Grid Deployment Roadmap proposes an aggressive yet achievable strategy and timetable designed to promote enhanced availability, security, efficiency, reliability, affordability, sustainability and safety of the electricity supply throughout the state while maintaining Kentucky’s nationally competitive electricity rates. The models and methods developed for this exercise can be utilized as a systematic process for the planning of coordinated smart grid deployments.
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17

Hill, Lawrence J., Eric Hirst, and Martin Schweitzer. "From DSM technologies to DSM programs: Issues in demand-side planning for electric utilities." Energy 17, no. 2 (February 1992): 151–60. http://dx.doi.org/10.1016/0360-5442(92)90064-7.

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18

Herod, J. Steven. "Comments on The effect of alternative cost recovery methods on planning by electric utilities." Resources and Energy 7, no. 1 (March 1985): 59–61. http://dx.doi.org/10.1016/0165-0572(85)90006-4.

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19

Saravanan, V., K. M. Venkatachalam, M. Arumugam, M. A. K. Borelessa, and K. T. M. U. Hemapala. "Impact of renewable energy in Indian electric power system." International Journal of Advances in Applied Sciences 10, no. 4 (December 1, 2021): 297. http://dx.doi.org/10.11591/ijaas.v10.i4.pp297-309.

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<p>This paper addresses the impact of renewable power generation such as photovoltaic and wind energy in the existing power system operations. Various modeling approaches and power quality/reliability analysis of these renewable energy sources in the electric power system by researchers and research organisations and utilities are outlined and their impacts are assessed. Challenges and protection schemes of renewable power integration into the existing grid are discussed through a detailed literature review and study of renewable integration into the Indian power system are outlined including potential planning and policy actions to support renewable energy integration in India.</p>
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20

Murugan, Raji, and Raju Ramasamy. "Understanding the power transformer component failures for health index-based maintenance planning in electric utilities." Engineering Failure Analysis 96 (February 2019): 274–88. http://dx.doi.org/10.1016/j.engfailanal.2018.10.011.

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21

Iskin, Ibrahim, and Tugrul U. Daim. "An assessment model for energy efficiency program planning in electric utilities: Case of Northwest U.S." Sustainable Energy Technologies and Assessments 15 (June 2016): 42–59. http://dx.doi.org/10.1016/j.seta.2016.03.002.

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22

Svendsen, Gert Tinggaard. "Towards a CO2 market in the EU: the case of electric utilities." European Environment 8, no. 4 (July 1998): 121–28. http://dx.doi.org/10.1002/(sici)1099-0976(199807/08)8:4<121::aid-eet158>3.0.co;2-t.

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23

Petersen, H. Craig. "Rates and tariff structures: Investor-owned utilities vs. rural electric cooperatives." Agribusiness 7, no. 6 (November 1991): 597–601. http://dx.doi.org/10.1002/1520-6297(199111)7:6<597::aid-agr2720070609>3.0.co;2-h.

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24

Palmer, Karen, and Hadi Dowlatabadi. "Implementing Social Costing in the Electric Utility Industry1." Energy & Environment 4, no. 3 (September 1993): 197–220. http://dx.doi.org/10.1177/0958305x9300400301.

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Social costing refers to the regulatory practice of requiring electric utilities to incorporate external costs into utility decision making. This practice is being adopted by a growing number of state public utility commissions (PUCs). The effectiveness of this new regulatory approach in reducing the social costs of supplying electricity will depend on the range of utility decisions covered. We use a utility planning model and illustrative estimtes of environmental costs to analyze the implications of different social costing regimes for generation technology choice, social and private costs of electricity supply and electricity price. Due to large differences in private costs across technologies and fuel types, social costing regulation has little or no effect on the utility's investment decisions, dispatch of generators or output price for many of the external cost estimates considered. Applying social costing exclusively to new generating units could result in increased use of existing units and higher social cost electricity production.
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25

Strezoski, Vladimir, Dragan Popovic, Dusko Bekut, and Goran Svenda. "DMS - basis for increasing of green distributed generation penetration in distribution networks." Thermal Science 16, suppl. 1 (2012): 189–203. http://dx.doi.org/10.2298/tsci120119071s.

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Modern (electric power) distribution utilities are faced with high penetration of distributed (electric) generation. Renewable generation is of prime interest. Within this generation, the green one incorporating solar (photovoltaic) and wind generation is the most important. Consequently, the following two imperatives are established in modern distribution utilities: 1) absorption of as much of available (connected to network) green generation as possible and 2) increasing of the limit of green distributed generation penetration. This generation is a significant basis of Smart Distribution Grid Concept. Distributed generation transfers passive distribution network into active one. The active distribution network analysis, control, operation management and planning become significantly complex. This complexity radically hinders the achievement of two above stated imperatives referring to the distributed generation penetration. This paper proves that Distribution Management System is a unique powerful system that integrates all tools necessary for surpassing main difficulties in the achievement of the both imperatives. The proof is obtained by the elaboration of a set of power applications (mathematical calculations) integrated in the Distribution Management System. The most important power applications, which deal with voltage / reactive power control, are specially stressed.
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26

Zielke, Megan, Adria Brooks, and Gregory Nemet. "The Impacts of Electric Vehicle Growth on Wholesale Electricity Prices in Wisconsin." World Electric Vehicle Journal 11, no. 2 (April 3, 2020): 32. http://dx.doi.org/10.3390/wevj11020032.

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This work explores the impact of the rapid growth of plug-in electric vehicles on wholesale electricity pricing. Understanding electric vehicle impacts on the grid is important for the mid- and long-range planning of transmission owners, distribution utilities, and regional system operators. Current research in electric vehicles considers technology adoption projections and the infrastructure needed to support electric vehicle growth. This work considers how projected electric vehicle growth in the State of Wisconsin would impact the transmission congestion and wholesale electricity pricing in the year 2030. We find minimal impacts on electricity prices (<2%) even under rapid growth assumptions, in which EVs comprise 5% of all vehicles in 2030. The increases seen in hourly locational marginal prices (LMPs) due to projected electric vehicle growth are, on average, less than those seen in annual changes of historic electricity prices in Wisconsin. We do find moderate, relative increases in congestion prices (+16–32%), which could provide an opportunity to align electric vehicle charging schedules with times of low transmission congestion.
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27

Pereira, João Adalberto, Osíris Canciglieri, André Eugênio Lazzaretti, and Paulo Moreira de Souza. "Application of Integrated Product Development Model Oriented to R&D Projects of the Brazilian Electricity Sector." Advanced Materials Research 945-949 (June 2014): 401–9. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.401.

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The R&D Programof the Brazilian Electricity Sector has recently begun to encourage the newly developed technologies transference to market through R&D projects. However, the lack of adequate methodological references for project planning led the authors to propose a model to assist the energy utilities and research teams in the elaboration of R&D projects proposals for the electricity sector. In this context, this paper presents a case study of the Integrated Product Development Model Oriented for R&D Projects of the Brazilian Electricity Sector (MOR&D) application to elaborate a project of pioneer lot for a newly developed of a safety equipment with the incentives of the R&D Program, showing that, as in industry, a well-structured model may be the way to generate innovative projects and products, meeting completely the guidelines that all electric utilities are subject.
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28

Teera-achariyakul, Noppada, and Dulpichet Rerkpreedapong. "Optimal Preventive Maintenance Planning for Electric Power Distribution Systems Using Failure Rates and Game Theory." Energies 15, no. 14 (July 17, 2022): 5172. http://dx.doi.org/10.3390/en15145172.

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Current electric utilities must achieve reliability enhancement of considerable distribution feeders with an economical budget. Thus, optimal preventive maintenance planning is required to balance the benefits and costs of maintenance programs. In this research, the proposed method determines the time-varying failure rate of each feeder to evaluate the likelihood of future interruptions. Meanwhile, the consequences of feeder interruptions are estimated using interruption energy rates, customer-minutes of interruption, and total kVA of service areas. Then, the risk is assessed and later treated as an opportunity for mitigating the customer interruption costs by planned preventive maintenance tasks. Subsequently, cooperative game theory is exploited in the proposed method to locate a decent balance between the benefits of reliability enhancement and the costs required for preventive maintenance programs. The effectiveness of the proposed method is illustrated through case studies of large power distribution networks of 12 service regions, including 3558 medium-voltage distribution feeders. The preventive maintenance plans resulting from the proposed method present the best compromise of benefits and costs compared with the conventional approach that requires a pre-specified maintenance budget.
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29

Shrestha, Tej Krishna, Rajesh Karki, and Prasanna Piya. "Development of an Operational Adequacy Evaluation Framework for Operational Planning of Bulk Electric Power Systems." International Journal of Reliability, Quality and Safety Engineering 27, no. 05 (March 10, 2020): 2040010. http://dx.doi.org/10.1142/s0218539320400100.

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Proper long-term planning for investment in resources, timely operational planning to prepare resources and to decide on operational strategies, and proper operating decisions to respond to disturbances during real-time system operation are important to supply reliable power to customers as economically as possible. However, existing utility procedures are insufficient to comprehend uncertainties of modern renewable-integrated power systems and to provide suitable quatitative indicators to assist in operational planning. Independent system operators and utilities around the globe are developing new and unique approaches to operational planning to manage rising uncertainties in power generation from renewable sources like wind and PV. It is desirable to establish uniformity in operational adequacy evaluation methods and quantitative metrics applicable to all power systems in the operational planning horizon of days, weeks, or even months. This will help standardize the operational planning methodology and metrics, and simplify implementation of operational strategies. To address this need, this paper presents a probabilistic analytical methodology for operational adequacy evaluation of a bulk power system integrating the concepts of state enumeration and a novel Dynamic System State Probability Evaluation (DSSPE) approach in time series analysis to accommodate the operational as well as network characteristics. The proposed methodology is implemented on a test system to demonstrate operational adequacy-based operational planning, and to analyze the impact of factors such as unit commitment decisions, locational distribution of load, and generation on the operational adequacy of the system.
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30

Murugan, Raji, and Ramasamy Raju. "Evaluation of in-service power transformer health condition for Inspection, Repair, and Replacement (IRR) maintenance planning in electric utilities." International Journal of System Assurance Engineering and Management 12, no. 2 (March 13, 2021): 318–36. http://dx.doi.org/10.1007/s13198-021-01083-1.

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31

Sun, Kaiqi, Huangqing Xiao, Shengyuan Liu, Shutang You, Fan Yang, Yuqing Dong, Weikang Wang, and Yilu Liu. "A Review of Clean Electricity Policies—From Countries to Utilities." Sustainability 12, no. 19 (September 25, 2020): 7946. http://dx.doi.org/10.3390/su12197946.

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Due to the heavy stress on environmental deterioration and the excessive consumption of fossil resources, the transition of global energy from fossil fuel energy to clean energy has significantly accelerated in recent years. The power industry and policymakers in almost all countries are focusing on clean energy development. Thanks to progressive clean energy policies, significant progress in clean energy integration and greenhouse gas reduction has been achieved around the world. However, due to the differences in economic structures, clean energy distributions, and development models, clean energy policy scope, focus, and coverage vary between different countries, states, and utilities. This paper aims at providing a policy review for readers to easily obtain clean energy policy information on various clean energies in the U.S. and some other countries. Firstly, this paper reviews and compares some countries’ clean energy policies on electricity. Then, taking the U.S. as an example, this paper introduces the clean energy policies of some representative states and utilities in the U.S in perspectives of renewable energies, electric vehicles, and energy storage.
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32

Veeramsetty, Venkataramana, Modem Sai Pavan Kumar, and Surender Reddy Salkuti. "Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree." Computers 11, no. 8 (July 29, 2022): 119. http://dx.doi.org/10.3390/computers11080119.

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Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution system point of view, accurate load forecasting is essential for proper planning and operation. In order to build most robust machine learning model to forecast the load with a good accuracy irrespective of weather condition and type of day, features such as the season, temperature, humidity and day-status are incorporated into the data. In this paper, a machine learning model, namely a regression tree, is used to forecast the active power load an hour and one day ahead. Real-time active power load data to train and test the machine learning models are collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that the regression tree model is able to forecast the load with less error.
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33

Sultan, Vivian, and Brian Hilton. "How May Location Analytics Be Used to Enhance the Reliability of the Smart Grid?" Inventions 4, no. 3 (August 2, 2019): 39. http://dx.doi.org/10.3390/inventions4030039.

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The United States (U.S.) electric infrastructure urgently needs renovation, as highlighted by recent major outages in California, New York, Texas, and Florida. The media has discussed the aging power infrastructure, and the Public Utilities Commission called for a comprehensive review of the causes of recent power outages. Without optimization algorithms to account for the many operating parameters and outage scenarios, planning engineers may inadvertently choose non-optimal locations for new components such as automated distribution switches, thereby impacting circuit reliability. This study aims to address this problem by answering the research question: “How may location analytics should be used to enhance the reliability of the smart grid?” To address this problem, Insights for ArcGIS was used to build worksheets using a geographic information systems (GIS)-based approach to resolve the challenges faced by utilities to reduce power outage frequency. Three case studies demonstrate various risk scenarios wherein a utility can prepare for the unexpected. An artefact in Insights for ArcGIS was created using a design science research methodology. This research proposes a solution to facilitate storm/disaster planning and vegetation management, identify optimal grid locations needing inspection or work, and detect regions where new distribution switches may provide benefits, considering the many operating parameters and outage scenarios. The artefact demonstrates that GIS can play an integral role in resolving this problem.
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34

O’Neill-Carrillo, Efrain, Matthew Lave, and Thad Haines. "Systemwide Considerations for Electrification of Transportation in Islands and Remote Locations." Vehicles 3, no. 3 (August 6, 2021): 498–511. http://dx.doi.org/10.3390/vehicles3030030.

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Electric vehicles (EVs) represent an important socio-economic development opportunity for islands and remote locations because they can lead to reduced fuel imports, electricity storage, grid services, and environmental and health benefits. This paper presents an overview of opportunities, challenges, and examples of EVs in islands and remote power systems, and is meant to provide background to researchers, utilities, energy offices, and other stakeholders interested in the impacts of electrification of transportation. The impact of uncontrolled EV charging on the electric grid operation is discussed, as well as several mitigation strategies. Of particular importance in many islands and remote systems is taking advantage of local resources by combining renewable energy and EV charging. Policy and economic issues are presented, with emphasis on the need for an overarching energy policy to guide the strategies for EVs growth. The key conclusion of this paper is that an orderly transition to EVs, one that maximizes benefits while addressing the challenges, requires careful analysis and comprehensive planning.
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35

Pawsey, Nicholas, Jayanath Ananda, and Zahirul Hoque. "Rationality, accounting and benchmarking water businesses." International Journal of Public Sector Management 31, no. 3 (April 9, 2018): 290–315. http://dx.doi.org/10.1108/ijpsm-04-2017-0124.

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Purpose The purpose of this paper is to explore the sensitivity of economic efficiency rankings of water businesses to the choice of alternative physical and accounting capital input measures. Design/methodology/approach Data envelopment analysis (DEA) was used to compute efficiency rankings for government-owned water businesses from the state of Victoria, Australia, over the period 2005/2006 through 2012/2013. Differences between DEA models when capital inputs were measured using either: statutory accounting values (historic cost and fair value), physical measures, or regulatory accounting values, were scrutinised. Findings Depending on the choice of capital input, significant variation in efficiency scores and the ranking of the top (worst) performing firms was observed. Research limitations/implications Future research may explore the generalisability of findings to a wider sample of water utilities globally. Future work can also consider the most reliable treatment of capital inputs in efficiency analysis. Practical implications Regulators should be cautious when using economic efficiency data in benchmarking exercises. A consistent approach to account for the capital stock is needed in the determination of price caps and designing incentives for poor performers. Originality/value DEA has been widely used to explore the role of ownership structure, firm size and regulation on water utility efficiency. This is the first study of its kind to explore the sensitivity of DEA to alternative physical and accounting capital input measures. This research also improves the conventional performance measurement in water utilities by using a bootstrap procedure to address the deterministic nature of the DEA approach.
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36

Singh, Shweta. "Artificial Neural Network Based Load Forecasting." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 1071–76. http://dx.doi.org/10.22214/ijraset.2022.40467.

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Abstract: In this report our point is to figure the power costs as precisely as conceivable by planning a half and half model and utilizing different blunder capacities to really take a look at the exactness of our outcome. Before liberation come to presence years and years back, the electric power enterprises have been overwhelmed by utilities that had full command over movements of every kind nearby. In any case, after its first endeavor in Latin America, the business has been on the move in many nations all over the planet. In a liberated market, end-use clients have the decision to choose their power provider. Keywords: Load Forecasting, ,Load forecasting models, Electricity pricing forecasting, AI Based Load forecasting, Neural network model
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37

Alshammari, A. S., B. M. Alshammari, T. Guesmi, and R. Abbassi. "Evaluation Framework of the Deficit and Reliability Quality Measures of the Transmission System." Engineering, Technology & Applied Science Research 11, no. 2 (April 11, 2021): 6930–34. http://dx.doi.org/10.48084/etasr.4074.

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Power system planning faces various issues related to reliability and quality evaluation. The power system network planning is by nature a complex, huge-scale, and mixed-objective optimization problem, especially when concerning its non-linear behavior and the requirements of future unknown loads. In this regard, the electric power utilities attempt to maintain a balance between the generation energy, the transmission capacity, and the needed demand. The main purpose of the current paper is to utilize modern modeling techniques and computational procedures, including the advanced deficit transmission system evaluation method and sparse-matrix network analysis algorithms, in order to evaluate, with sufficient accuracy, the deficit and reliability levels in practical real-life large-scale power systems. The new evaluation methodology is based on three quantities representing the relationship between the generation push in the grid, the maximum limitation of the transmission capacity, and the needed load. The main contribution of the paper is assessing the deficit transmission system index with novel formulas.
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38

Mouaad, Boulakhbar, Markos Farag, Benabdelaziz Kawtar, Kousksou Tarik, and Zazi Malika. "A Deep Learning Approach for Electric Vehicle Charging Duration Prediction at Public Charging Stations: The Case of Morocco." ITM Web of Conferences 43 (2022): 01024. http://dx.doi.org/10.1051/itmconf/20224301024.

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The adoption of electric vehicles (EVs) is increasing worldwide as it may help reduce reliance on fossil fuels and greenhouse gas emissions. However, the large-scale use of charging stations for electric vehicles poses some challenges to the power grid and public infrastructure. To overcome the problem of extended charging time, the simple solution of increasing the charging station and increasing the charging capacity does not work due to the load and space limitation of the power grid. Therefore, researchers focused on developing intelligent planning algorithms to manage the demand for public charging based on predicting the charging time of electric vehicles. As a result, this paper proposes a deep learning approach for predicting the duration of charging sessions. These approaches are validated using a real-world dataset of charging processes collected at public charging stations in Morocco. Numerical results show that the gated recurrent units (GRU) regression method slightly outperforms the other methods in predicting the charging sessions duration. Accurate prediction of electric vehicles charging duration has many potential applications for utilities and charging operators, including grid reliability, scheduling, and smart grid integration. In the case of Morocco, the massive deployment of EVs can cause a variety of problems to the electrical system due to the considerable charging power and stochastic charging behaviors of electric vehicle drivers. Thanks to this study's results, we can assess the expected impact of additional EVs on the grid, considering specific characteristics of the Moroccan power system.
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39

Son, Namrye, Seunghak Yang, and Jeongseung Na. "Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting." Applied Sciences 10, no. 18 (September 17, 2020): 6489. http://dx.doi.org/10.3390/app10186489.

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Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.
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Motwakel, Abdelwahed, Eatedal Alabdulkreem, Abdulbaset Gaddah, Radwa Marzouk, Nermin M. Salem, Abu Sarwar Zamani, Amgad Atta Abdelmageed, and Mohamed I. Eldesouki. "Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids." Sustainability 15, no. 2 (January 12, 2023): 1524. http://dx.doi.org/10.3390/su15021524.

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Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.
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Kumar, Pawan, Srete Nikolovski, Ikbal Ali, Mini S. Thomas, and Hemant Ahuja. "Impact of Electric Vehicles on Energy Efficiency with Energy Boosters in Coordination for Sustainable Energy in Smart Cities." Processes 10, no. 8 (August 12, 2022): 1593. http://dx.doi.org/10.3390/pr10081593.

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The use of electric vehicles (EVs) has recently increased in a smart city environment. With this, the optimal location of the charging station is a great challenge and, hence, the energy efficiency performance (EEP) of an electrical system is important. Ideally, the EEP is realized through passive energy boosters (PEBs) and active energy boosters (AEBs). PEBs require no external resources, and EEP is achieved through altering the network topology and loading patterns, whereas, in AEBs, integrating external energy resources is a must. The EEP has also become dynamic with the integration of an energy storage system (ESS) in a deregulated environment. Customer energy requirement varies daily, weekly, and seasonally. In this scenario, the frequent change in network topology requires modifying the size and location of AEBs. It alters the customers’ voltage profile, loadability margin, and supply reliability when the EV works differently as a load or source. Therefore, a comprehensive EEP analysis with different probabilistic loading patterns, including ESS, must be performed at the planning stage. This work uses a harmony search algorithm to evaluate EEP for AEBs and PEBs, in coordination, when ESS works as a load or source, at four locations, for customers’ and utilities’ benefits.
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Saeidian, B., A. Rajabifard, B. Atazadeh, and M. Kalantari. "EXTENDING CITYGML 3.0 TO SUPPORT 3D UNDERGROUND LAND ADMINISTRATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W4-2022 (October 14, 2022): 125–32. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w4-2022-125-2022.

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Abstract. Rapid development of underground space necessitates the efficient management of underground areas. Data modelling plays an underpinning role in integrating and managing underground physical and legal data. The physical data refers to semantic and spatial data of underground assets such as utilities, tunnels, and basements, while the legal data comprises the ownership information and the extent of underground legal spaces and the semantic and spatial relationships between legal spaces. Current Underground Land Administration (ULA) practices mainly focus on representing only either legal spaces or the physical reality of subsurface objects using fragmented and isolated 2D drawings, leading to ineffective ULA. A complete and accurate 3D representation of underground legal spaces integrated with the 3D model of their physical counterparts can support different use cases of ULA beyond underground land registration, such as planning, design and construction of underground assets (e.g. tunnels and train stations), utility management and excavation. CityGML is a prominent semantic data model to represent 3D urban objects at a city scale, making it a good choice for underground because underground assets such as tunnels and utilities are often modelled at city scales. However, CityGML, in its current version, does not support legal information. This research aims to develop an Application Domain Extension (ADE) for CityGML to support 3D ULA based on the requirements defined in the Victorian state of Australia. These requirements include primary underground parcels and secondary underground interests. This work extends CityGML 3.0, which is the new version of this model. In CityGML 3.0, UML conceptual models as platform-independent models are suggested to express ADEs. Thus, the ADE proposed in this study will be based on UML. The findings of this study show that extending CityGML to support legal information can be a viable solution to meet the requirements of a 3D integrated model for ULA. The CityGML ADE proposed in this study can potentially provide a new solution for 3D digital management of underground ownership rights in Victoria, and it can be used to implement an integrated 3D digital data environment for ULA.
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Celli, Gianni, Marco Galici, Fabrizio Pilo, Simona Ruggeri, and Gian Giuseppe Soma. "Uncertainty Reduction on Flexibility Services Provision from DER by Resorting to DSO Storage Devices." Applied Sciences 11, no. 8 (April 10, 2021): 3395. http://dx.doi.org/10.3390/app11083395.

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Current trends in electrification of the final energy consumption and towards a massive electricity production from renewables are leading a revolution in the electric distribution system. Indeed, the traditional “fit & forget” planning approach used by Distributors would entail a huge amount of network investment. Therefore, for making these trends economically sustainable, the concept of Smart Distribution Network has been proposed, based on active management of the system and the exploitation of flexibility services provided by Distributed Energy Resources. However, the uncertainties associated to this innovation are holding its acceptance by utilities. For increasing their confidence, new risk-based planning tools are necessary, able to estimate the residual risk connected with each choice and identify solutions that can gradually lead to a full Smart Distribution Network implementation. Battery energy storage systems, owned and operated by Distributors, represent one of these solutions, since they can support the use of local flexibility services by covering part of the associated uncertainties. The paper presents a robust approach for the optimal exploitation of these flexibility services with a simultaneous optimal allocation of storage devices. For each solution, the residual risk is estimated, making this tool ready for its integration within a risk-based planning procedure.
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Hsu, Cheng Ting, Chao Shun Chen, Chia Hung Lin, and Tsun Jen Cheng. "Impact of a Large Scale PV Generation System on the Distribution System." Applied Mechanics and Materials 479-480 (December 2013): 559–64. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.559.

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This paper investigates the impact of photovoltaic generation system (pvgs) on the distribution system. The installations of various types of distributed generators (dgs) on the distribution system have significantly changed the operating, planning and maintaining strategies of the utility. In this paper, one practical Taiwan Power Company (taipower) distribution system with a large-scale of pvgs is selected for study. Many power quality issues like steady state voltage variation, reverse power, voltage unbalance, short-circuit current and harmonic are analyzed by considering many different operation scenarios of the distribution system and pvgs. The simulation results are compared with the related standards and it is found that the pvgs impact on the concerned power quality and reliability issues are all within the limits. It is concluded that the simulation results of the paper are very helpful to the electric utilities and pv producers for providing better power quality as well as increasing photovoltaic (pv) penetration in distribution grids.
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Fang, Fang, Rajesh Karki, and Prasanna Piya. "Probabilistic Reliability Enhancement Strategies of Hydro Dominant Power Systems under Energy Uncertainty." Sustainability 12, no. 9 (May 1, 2020): 3663. http://dx.doi.org/10.3390/su12093663.

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Climatic hydrological changes cause considerable seasonal and yearly energy variation in hydro dominant electric power systems. Extreme weather events are becoming more frequent in recent years causing dramatic impacts on energy availability in such systems. A significant amount of energy is often wasted due to reservoir overflow during wet seasons. By contrast, the scarcity of water in dry seasons results in inadequate power generation to meet the system demand, and therefore degrades overall system reliability. The high risks associated with an extreme dry hydrological condition should not be ignored in long term system adequacy planning of hydro dominant utilities. This paper presents a probabilistic method to incorporate diurnal, seasonal and yearly energy management strategies in run-of-river and storage type hydropower plant planning and operation in order to minimize the adverse impact of energy uncertainty and maintain long-term system adequacy. The impacts of reservoir capacity and demand side management on water utilization and system reliability are investigated with case studies illustrated using the IEEE Reliability Test System modified to create a hydro dominant system. The achieved benefits of reliability enhancement strategies are analyzed and compared in this paper.
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Aslam, Shahzad, Nasir Ayub, Umer Farooq, Muhammad Junaid Alvi, Fahad R. Albogamy, Gul Rukh, Syed Irtaza Haider, Ahmad Taher Azar, and Rasool Bukhsh. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid." Sustainability 13, no. 22 (November 16, 2021): 12653. http://dx.doi.org/10.3390/su132212653.

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Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.
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Nor, Shamsul Fahmi Mohd, Mohd Zainal Abidin Ab Kadir, Azrul Mohd Ariffin, Miszaina Osman, Muhammad Syahmi Abd Rahman, and Noorlina Mohd Zainuddin. "Issues and Challenges in Voltage Uprating for Sustainable Power Operation: A Case Study of a 132 kV Transmission Line System in Malaysia." Sustainability 13, no. 19 (September 28, 2021): 10776. http://dx.doi.org/10.3390/su131910776.

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Ageing grid infrastructure is a critical issue that is currently faced by electrical utilities worldwide, resulting in crucial decisions that must be made for the replacement, repair, or refurbishment of assets under constrained budgetary conditions, as well as other factors. As one of the fastest-developing countries globally, Malaysia is steadily growing in terms of its cumulative population, large industries, and advanced transportation sector, leading to an increasing electricity demand and, consequently, putting stress on electric utility providers to meet these tremendous demands. Significant concerns in the new development of transmission in Malaysia are the environmental issues, which involve exploration in gazetted areas and forest reserves. This paper describes the issues and challenges in developing a new transmission line system in Malaysia. In recent years, uprating existing transmission line systems has been one of the best asset management decisions for electric utility operators in order to meet such a demand for capacity. This paper assesses the technical issues and conventional methods of uprating the voltage of an existing transmission line system. The initial study found that the phase-to-Earth clearance does not fulfil the electrical clearance envelope. The study on the existing 132 kV transmission line system further analysed these issues and proposed an appropriate technique for uprating to a 275 kV transmission line system. Finally, the results indicate that the voltage uprating of the 132 kV transmission line system to 275 kV is able to satisfy the electrical clearance requirement envelope.
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Jang, Moonjong, Ho-Jin Choi, Chae-Gyun Lim, Byoungwoong An, and Jungsub Sim. "Optimization of ESS Scheduling for Cost Reduction in Commercial and Industry Customers in Korea." Sustainability 14, no. 6 (March 18, 2022): 3605. http://dx.doi.org/10.3390/su14063605.

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Various attempts have been made to reduce carbon emissions in the energy sector as part of global net zero emissions trends. Among them, interest in the use of energy storage systems (ESSs) for energy efficiency is growing. Utilities intend to improve the efficiency of investment and operating costs by reducing the maximum peak and leveling the load. Many ESS-scheduling optimization techniques have been studied to reduce the peak demand, balance the load, or reduce the cost corresponding to these two purposes from the customer’s point of view. In this paper, a method for cost minimization that simultaneously considers both the peak demand and load balancing is proposed, and the results and analysis of a case study in Korea Electric Power Corporation (KEPCO), Korea are presented. Through these results, we show that there is a priority among the objective functions of the ESS schedule, that demand charge is more important than energy charge, and that the ESS schedule problem for customers to reduce costs is also beneficial to power system operation by the utility’s rate policy.
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KINGSTONE, PETER. "Maria Victoria Murillo, Political Competition, Partisanship, and Policy-Making in Latin American Public Utilities (Cambridge and New York: Cambridge University Press, 2009), pp. xv+2929, £45.00, £17.99 pb; $85.00, $25.99 pb." Journal of Latin American Studies 42, no. 4 (November 2010): 904–6. http://dx.doi.org/10.1017/s0022216x10001720.

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Javed, Umar, Khalid Ijaz, Muhammad Jawad, Ejaz A. Ansari, Noman Shabbir, Lauri Kütt, and Oleksandr Husev. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis." Energies 14, no. 17 (September 3, 2021): 5510. http://dx.doi.org/10.3390/en14175510.

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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.
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