The Next Chapter in DER – Scenario-based Impact Analysis

Oct 26, 2016

Energy is no longer solely supplied by utilities through large generation systems but also through a combination of large renewable and conventional sources alongside smaller, localized generation systems within distribution networks. It is no surprise that the grid is undergoing a rapid and substantial transformation, most notably with the increased installation of Distributed Energy Resources (DER) such as solar panels, wind turbines, energy storage systems, and electric vehicles. With the introduction of DERs and microgrids, the grid no longer operates with a unidirectional power flow - bidirectional flows are becoming more prevalent as local generation surpasses load.

Signals & Sign-Posts:

Many companies engage in detailed scenario planning as part of their organizational best practices.. Part of that practice includes the identification of signals and sign-posts.  A well-known strategy used by resource planners and scenario strategists.   As events unfold they can signal or indicate market changes that can be used to validate scenarios allowing the organization to be better able to adapt to the future.  Recently we have seen some unprecedented (but perhaps not unforeseen) events. For example, Southern California Edison proposes $2.1B capex for DER integration:

“SCE is now looking at spending capital to facilitate DER integration. Specifically, the utility is seeking regulatory approval on funding for updating automation systems on the worst performing distribution circuits, providing adequate communications equipment to support these automation upgrades, and deploying analytics tools to advance system planning and grid operations.” GreenTech Media

This underscores that now, more than ever, utilities must provide safe, dependable and efficient power to customers while managing increasingly complex needs and variability. Due to increased DER prevalence, peak load times are shifting away from historic norms. Utility companies must plan for and manage this transition. But how do you evaluate the multiple technologies, adoptions, and datastreams and make a sound judgment on how optimize continued delivery of the reliable service electric utility customers have come to expect, in the face of newly adopted grid edge generation?

One example is a utility in China who is experiencing increased challenges in managing their network due to the rapid and sustained emergence of electric vehicles (EV). In response, Nexant and the utility used advanced planning and distribution analytics to model and mitigate these difficulties to better manage the heightened energy demand from electric vehicle charging stations. In the study, the software used provided an analysis of both voltage and load flow problems in modeling different percentages of EV penetration. In looking at various penetration points on the grid, the project team assessed charging stations of three typical sizes: Level 1 (1.8KW@120V, ~10 hour charge time); Level 2 (12KW@240V, ~5 hour charge time); and Level 3 (42KW@480V, ~2 hour charge time). In modeling and assessing this information, the team gained an in-depth perspective on their expected load growth in order to find different ways to best solve this challenge. In order to best optimize a solution, Nexant worked with the utility to model, forecast, and assess these scenarios and impacts. A variety of solutions were presented and analyzed including new solar installations, new generation stations, upgraded feeders, and supplemental generation stations at key locations. It was critical that the assessment provide the utility with improved visibility into EV and DER, and deliver advanced comprehensive analytics to model the effect of all possible grid influencers. This provided the utility with options for new methods to improve operational goals such as shaved peaks, minimized losses, improved reliability, targeted maintenance, and deferred system upgrades. In other words, it was important that the software utilized was able to analyze any condition that could occur on the grid by modelling all grid influencers and estimating and evaluating their impact on the network.

New Solutions for New Problems:

The software used was unique and proved highly effective in its ability to model multifaceted capabilities, including impacts to energy and economics, especially in regards to this utility’s key use case regarding electric vehicles. The results of the software analysis allowed the utility to evaluate the impacts on their network and model various scenarios to help the utility manage EV plus better plan for future load growth in combination with other DER effects.

Similarly, many utility companies regardless of size or location have encountered challenges due to the increased installation of distributed generation such as photovoltaic panels upon the grid. With solar generating energy back into the grid, in some cases utilities are struggling to manage reverse power flows originating on the customer side. As a consequence, due to underutilized solar generation, the potential for reverse thermal violations is becoming more and more of a risk.

Maximize the ROI on distribution investments and deliver on the promise of a Smart Grid:

Utilities are facing several key challenges: What are the best technologies to add to improve the reliability and operation of your distribution network? How do you maximize the Return on Investment (ROI) on these new assets in conjunction with existing assets? The best analytical software includes modeling of both energy and economic impacts, and includes methods to easily establish and compare multiple scenarios involving combinations of grid influencers including demand response and energy efficiency programs, solar and wind generation (including increasingly prevalent smart inverters), energy storage, electric vehicles, and microgrids, in combination with Volt/VAR Optimization and Conservation Voltage Reduction programs.

It is important to consider the timelines for analyzing these scenarios to determine the specific locational impacts with respect to load carrying capacity factor and locational DR/DER valuation.

A key consideration for any utility dealing with DER and grid management is to define the time horizons. Is it a “snapshot” analysis or is it “time domain” analysis?  Ideally, the utility should model the time- dependent (i.e. temporal) constraints over the operational planning horizon - say the next 1-3 hours, next 24-48 hours, next 1 week and so on.  Here are a few examples of assets to be modeled:

a)   Constraints on how/when to move the transformer taps, toggle the reactive power sources, and dispatch commands to other smart grid devices over a longer time horizon to minimize the wear and tear on the equipment.  As an example, on a longer time horizon the utility may want to minimize the transformer tap movements: i.e. no more than 5 steps in a given day;  no more than 2 steps in a single operation, etc.

b)   Optimization of batteries/storage devices: time of charge and discharge, based on storage capacity and other storage characteristics as well as goals for peak shaving, peak shifting, or modulation of variable renewables and other factors.

c)    Options for demand response program dispatch based on operational goals.

d)   Constraints or incentives for timing of charging at electric vehicle charging stations.

e)   Settings for smart inverters, whether the inverter is intended for voltage control, power factor control, or reactive power control.

Fortunately, with recent advancements in distribution analytics, a utility can now model various scenarios taking place within their distribution networks, better evaluate new technology and its impacts over various timeframes, and maximize a utility’s return on investment.

It is critical to determine the specific locational and temporal impacts with respect to load carrying capacity factor and locational DR/DER valuation. In addition, utilities must rapidly evaluate their capacity for increased customer demand for photovoltaics and electric vehicles. Fortunately, with recent advancements in distribution analytics, a utility can now model various scenarios taking place within their distribution networks, better evaluate new technology and its impacts, and maximize their return on investment.

We are all watching state policy, RPS goals, the election, and in turn the clean power plan. There are many sign-posts. Technology companies are paying close attention to projects around the world while utilities are looking for new ways to analyze the impact of these technologies on the reliability of their network. Scenarios are good for understanding the big picture as Shell introduced to the world in the 70s, and specifically are providing utilities with the much needed ability to model and simulate DR, DER, microgrids, and other grid influencers and their resultant economic and operational impacts.

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