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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
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Primary distribution systems deliver electrical power from substations to consumers through various voltage classes, with 15-kV class voltages being predominant among U.S. utilities. Older 2.5- and 5-kV classes are being replaced by 15-kV primaries, while higher 25- to 34.5-kV classes are used in high-density urban areas and rural regions with long feeders. Three-phase, four-wire multigrounded systems are widely employed for balanced power delivery, using the neutral wire as a grounding point.
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Forecasting distributed energy resources adoption for power systems.

Nicholas Willems1, Ashok Sekar2, Benjamin Sigrin2

  • 1Walker Department of Mechanical Engineering, The University of Texas at Austin, 204 E. Dean Keeton St., Austin, TX 78712, USA.

Iscience
|May 27, 2022
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Summary

Accurate distributed energy resource forecasts are crucial for grid planning. An advanced Bass model improves rooftop solar adoption predictions, reducing errors significantly for better energy system reliability.

Keywords:
Energy ModelingEnergy SystemsEnergy managementEnergy policyEnergy resources

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Area of Science:

  • Energy Systems Analysis
  • Technology Adoption Modeling
  • Renewable Energy Forecasting

Background:

  • Accurate forecasting of distributed energy resources (DERs) is essential for effective long-term resource and transmission planning.
  • Inaccurate predictions can result in significant cost inefficiencies and potential grid failures.
  • Existing models often lack the granularity to capture complex adoption dynamics.

Purpose of the Study:

  • To develop and validate an open-source tool for forecasting technology adoption using an advanced Bass specification.
  • To improve the accuracy of distributed energy resource penetration forecasts at the U.S. county-level.
  • To demonstrate the benefits of incorporating geographic clustering, market size, and dynamic time steps in adoption models.

Main Methods:

  • Developed an open-source forecasting tool based on an advanced Bass specification.
  • Incorporated geographic clustering, exogenously estimated market size, and dynamic time steps into the model.
  • Trained the model on historical rooftop photovoltaic adoption data at the U.S. county-level with techno-economic estimates.

Main Results:

  • Achieved a two-year average mean-absolute-percentage-error of 19% in predicting county-level system counts, weighted by population.
  • Demonstrated a negative correlation between model error and market maturity; error was 12% in mature markets.
  • Significantly reduced unweighted forecasting percent error compared to a conventional Bass specification: 25% for capacity (vs. 196%) and 22% for system count (vs. 226%).

Conclusions:

  • The advanced Bass specification provides a more accurate method for forecasting distributed energy resource adoption.
  • The developed open-source tool can enhance the reliability and cost-efficiency of energy planning.
  • Improved forecasting accuracy is particularly notable in more mature renewable energy markets.