The economics of solar energy continue to pose issues for the energy industry at large. As the levelized cost of energy (LCOE) for solar declines, distributed solar continues to gain adoption. Whether on rooftops, in community solar settings or grid-scale plants, Solar's midday peak production has produced many challenges for system operators. One of the biggest is the "duck curve" dilemma — in which utilities find themselves with more energy produced than needed.
As utilities have struggled to respond to solar's daily output peaks, most reacted by ramping up or down their legacy generation resources. However, these baseload resources (often coal or nuclear plants) were not originally designed for this new use. Shutting down and restarting these resources several times a day often proved difficult, inefficient and costly.
Energy storage has helped address some issues as costs have dropped and storage technologies have improved, enabling utilities to begin utilizing it to extend existing PV generation into off-peak hours. While it is a promising approach, storage adds another asset to the resource mix that utility system operators must manage and optimize.
Utilities are at the center of this industry transformation adjustment, and each enterprise must balance its own economic and operational equations to find the right mix of resources to deploy at the right time. The traditional LCOE which measures the energy production cost of a single generating asset now faces challenges. Although the traditional LCOE allows for a comparison of different generating assets, it considers generation in isolation. Utilities can no longer evaluate new generation resources solely on the cost of electricity produced, but instead must make decisions between distributed energy resources (DER) that affect each other and are affected by market signals and operational data. A new, system-wide metric is needed; one that provides a more accurate assessment of the resources within the environment and use-case it must serve.
In the US DOE-funded Austin SHINES program, such a new yardstick has been developed by Austin Energy and Doosan GridTech. The "System Levelized Cost of Energy" metric – called SLCOE – enables resources to be evaluated based on their ability to support an efficient and low-cost integrated grid ecosystem. The SLCOE metric is defined as the combined costs of all assets working together within the defined system boundary to reliably serve a unit of load demand.
In Austin SHINES, SLCOE is specifically used to calculate the differential value of integrated DER management as compared to the value of uncoordinated DERs. Three control strategies with different levels of intelligence and complexity were defined:
No controls which involved very rudimentary controls essential for basic operation of the asset;
Autonomous control which introduced local intelligence to the assets and enabled them to make locally-optimal decisions; and,
Holistic controls that involve a DER management platform which optimizes both operational performance and economics, aiming to create value in five distinct use cases. Three use cases were economic – energy arbitrage, real-time price dispatch and peak load reduction. Two use cases were operational – voltage support and congestion management.
The diverse set of integrated DERs (around 4.5 MW) is typical of the distributed resources scenario that many utilities encounter -- utility-owned energy storage systems (ESS), community solar, and distributed residential and commercial resources including rooftop solar, electric vehicles and energy storage on two separate distribution circuits.
These resources are managed and optimized by Doosan GridTech's DER Optimizer (DERO®), an overarching intelligent system that acts on data from multiple sources to work out the SLCOE. DERO sits in the utility control room and acts to manage and optimize the entire fleet of DERs across the utility enterprise based on its analysis of data from all sources, resulting in the ability to optimize the economic value. It determines which resource to deploy at which time – not only the best operational choice but the best economic one.
To fulfill this role, DERO needs to gather data, analyze it and act on it. The system architecture includes technical operational data coming from DER including from energy storage systems, customer meters, solar inverters and aggregators. It also incorporates enterprise data coming from the utility's SCADA/ADMS and gathers economic data from multiple sources including ERCOT market signals and forecasting. The system uses open standards for both controls and communications, so it can integrate and add more elements as needed.
Austin SHINES calculates value with information from the ERCOT market. That's an approach unique to this specific regional market today. Across the country, approaches to regulation of markets and the role of utilities in DER management vary widely and are still evolving. While value is measured differently in each, the concept of value is universal.
The lessons being learned at Austin SHINES on how to maximize value in high-penetration DER systems can act as an operational template to utilities in any market structure or region. By establishing a system-wide way to measure value, the SLCOE offers a key tool to advancing the holistic view of how DERs can provide value to utility systems – and to the grid as it continues to evolve.
This is Doosan GridTech's final article of a 3-part series on the Austin SHINES program. Visit us at Distributech 2020 to explore ways that our field-proven DERMS platform can support your needs to balance customer value with utility value. And be sure to register for our January 23 webinaron the Austin SHINES program hosted by Utility Dive.