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Locational Approach is Redefining Energy Efficiency Programs

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This item is part of the Predictions & Trends - Special Issue - 01/2020, click here for more

As energy efficiency programs have matured, utilities have shifted from solely focusing on program performance to how these programs can better integrate into broader organization-wide strategies. Many programs are reaching the required savings and demand reduction goals – but are they fully taking into account other long-term objectives important to the utility? The days of operating within organizational silos are gone ─ instead replaced with a renewed emphasis on creating synergies across the different business units of the utility to deliver increased value and impact. A fast-changing energy landscape is forcing utilities to quickly adapt to evolving customer energy needs and build more innovative business practices.

Traditional energy efficiency programs often miss opportunities to target areas of the distribution system that truly need demand side management (DSM) projects, which can help the utility defer capital infrastructure projects and invest in other distributed energy resources. Rather than exclusively focusing on generating the most energy efficiency projects, utilities are leveraging an emerging industry trend to target program investments in areas with high energy demand and current or forecasted grid capacity constraints. This locational approach is helping energy efficiency programs maximize the impacts of their program investments while supporting enterprise-wide objectives associated with distribution planning and infrastructure projects.

An additional benefit of the integrated, locational approach is taking the overwhelming amount of utility data points associated with customers and the distribution system and assembling it into actionable insights. New technology that leverages machine learning can analyze large amounts of data ─ including historical load information, customer usage, and demographic data ─ in a short period of time. When combined with artificial intelligence, this process can use probabilistic forecasting to predict possible future outcomes, automate the load-flow analysis to determine grid deficiencies, and perform economic simulations to determine the best capital strategies for the utility. This comprehensive analysis results in spatial models that capture current or forecasted grid issues and identify customers that can be targeted with various demand reduction tactics – including energy efficiency.

For the past several years, Leidos has seen this industry trend emerge and has worked with utilities to develop more integrated strategies for energy efficiency program delivery and infrastructure improvements. Additionally, we have focused on how other non-wires alternatives such as demand response, renewable projects and energy storage ─ can influence these strategies and solve current and future challenges. Utilities that take a more comprehensive methodology to energy efficiency program implementation ─ one that integrates distribution planning with energy management operations ─ can realize significant long-term cost reductions, generate greater returns from existing energy efficiency budgets, and build a roadmap for future energy options. Most importantly, the utility will enhance grid reliability and meet their customers’ changing energy needs.

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Josh Wepman's picture

Thank Josh for the Post!

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Matt Chester's picture
Matt Chester on Feb 10, 2020 3:15 pm GMT

An additional benefit of the integrated, locational approach is taking the overwhelming amount of utility data points associated with customers and the distribution system and assembling it into actionable insights. New technology that leverages machine learning can analyze large amounts of data ─ including historical load information, customer usage, and demographic data ─ in a short period of time. When combined with artificial intelligence, this process can use probabilistic forecasting to predict possible future outcomes, automate the load-flow analysis to determine grid deficiencies, and perform economic simulations to determine the best capital strategies for the utility

Are there energy efficiency behaviors or tips that can be particularly tapped into using hyperlocational data, such as on a neighborhood by neighborhood basis rather than a city by city basis, for example?

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