Presented By:
Sherif Abdelrazek, Ph.D., PE
Duke Energy, LLC
TechCon 2023
Abstract
Energy Storage Systems (ESSs) can accommodate an extensive variety of functions, rendering them a viable solution to enhance grid resiliency, reliability and efficiency. They can also enable high penetration of renewable energy resources on our grids. However, the current capital cost, life cycle, and efficiency of storage technologies, although improving, make single application use cases economically challenging. At this point, only stacked use-cases can offer economic feasibility. Stacked benefits can be achieved through manipulation of active and reactive power capabilities of ESSs to achieve multiple value streams and maximize total benefit while maintaining operational efficiency such that battery cycle life is not compromised. While control techniques are progressing in strides, the practice of identification of points on the electrical system, where the need for grid-support is the greatest, remains immature. This paper provides an overview of methodologies deduced by Duke Energy’s Regulated Renewables team to optimally identify locations on the grid where grid tied ESSs can solve problems at lower costs than wires solutions.
Introduction
Use cases that ES systems can execute on the grid hold considerable value to energy producers, grid operators and in turn, utility rate payers. Reference [1] Discuss different applications where different technologies of energy storage can be used. These applications include electric energy time shift, electric supply capacity, peak load shaving, regulation, electric supply reserve capacity, voltage support, transmission support, transmission congestion relief, transmission and distribution upgrade deferral, substation on-site power, time of use energy cost management, demand change management, electric service reliability, electric service power quality, renewable energy time shift, renewable capacity firming [2,3] and wind generation grid integration. [2] Also discusses the value of using energy storage systems in flexible AC systems (FACS) and high voltage DC transmission (HVDC).
It has been identified that there is a concurrent need to quantify the “value” of ES systems in the various services it provides to the grid, individually and in multiple or “stacked” services, where a single storage system has the potential to capture several revenue streams to achieve economic viability. This is important now and as the cost of storage systems decline to economically attractive levels [4]. Reference [5] discusses tools for evaluating BESS multiple functions value based on various applications and battery technologies. The ability to evaluate applications and technologies provides greater value for grid level energy storage. However, it is important to develop multiple control functions for storage management system considering grid level value and economic benefits for a given storage technology. Such control architecture should interactwith grid and provide command signals to storage management systems for the appropriate set points and applications that provide maximum benefit at a given time.
The problem to which an energy storage system is aimed to solve, dictates the energy storage technology to be applied. Applications that require power sources that are able to provide a wide range of power levels for a relatively short period of time (in the order of seconds or minutes) use different storage technologies than applications that require energy sources that are able to supply limited power levels for a considerably long period of time (in the order of hours). Applications that require power sources use storage technologies that can bare relatively low amounts of energy per rated power output. These storage technologies include flywheels, capacitors, super conducting magnetic energy storage systems (SCMES) and some electrochemical battery types. Applications that require energy sources use storage technologies with high energy baring capability per rated power output. These storage technologies include thermal energy storage, pumped hydro, compressed air energy storage and most battery types [1].
Further, to gain the most value from a grid tied ESS, the optimization cycle starts during the identification phase. In other words, one should start by identifying where on the grid is the need for an ES system the greatest. This should take into account the previously mentioned capability and duration of the ES technology in consideration.
Energy Storage Necessity Identification (ESNI) Framework:
The ESNI framework aims to provide a clear method for utility distribution planners to identify locations on the utility grid where Battery Energy Storage Systems (BESSs) are needed to provide value. The value that grid tied BESSs can provide range from improving reliability for customers to deferring system upgrades. Given the multitude of applications/use cases that grid tied BESSs can wield, identifying electrical locations on the utility system where most value can be extracted can be challenging. This creates a need and necessity to create a standardized method for screening system constraints for the capability of BESSs to be considered. This is exactly what the ESNI framework aims to provide.
Distribution planners are typically responsible for certain geographic areas. Within these areas, they are fully aware of present and near-term constraints and issues. These issues and constraints present potential opportunities for BESSs to provide value through solving these grid constraints in a more economic fashion than classical methods. For example, an area may suffer from a high number of outages due to a long radial feeder that provides power to a limited number of customers at the end of said feeder. In this case an energy storage system can be potentially utilized to improve reliability for these customers while deferring the cost of upgrade of the long radial feeder. BESS can stack benefits by performing multiple functions. As a result, opportunities where a BESS is needed to perform multiple functions and thus, wield multiple value streams becomes the most appealing. To measure the potential of different opportunities to satisfy multiple values streams, distribution planners are requested to provide an Energy Storage Opportunity Score (ESOS) for each system opportunity and each grid issue (ESOS A, ESOS B & ESOS C), as shown in the necessity identification flow chart. This will help the energy storage project development team to prioritize further vetting of these opportunities. Additionally, for each opportunity, different constraints prompt the knowledge for different sets of data for further vetting. Therefore, within each opportunity, distribution planners are asked to provide a certain dataset.
The following grid issues are addressed in the flowcharts:
- Poor Reliability
- High Peak Load Growth
- Critical Infrastructure/Load
Poor Reliability
High Peak Load Growth
Critical Infrastructure / Load
For each grid issue, a flow chart is provided to guide the distribution planner to calculate the ESOS for each opportunity. Also, each flow chart directs the planner to which set of data to provide to a specialized energy storage development team to further vet the opportunity.
Automated Identification of Energy Storage Necessity
While ESNI did produce considerable value in terms of identifying valuable ES opportunities, it did have some drawbacks relating to resource requirements for evaluation and throughput. Therefore, Duke Energy’s Energy Storage Engineering and digital transformation teams developed automated methods to identify ES opportunities. Here we will go over identification method for reliability improvement use cases.
It became apparent that we need to develop metrics that measure where grid-tied ES systems are needed most. One of these metrics is the Ar index. The purpose of this index is aide in the identification of MV circuits with high probability of outages of concentrated customer loads. Concentrated customer loads at the end of radial feeders are ones most susceptible to outages due to the high probability of OH line damage or vegetation encroachment. These are scenarios in which ES systems can be best positioned to improve reliability as it becomes easy to isolate customer load from locations of faults. Conversely, in scenarios where customer load is distributed throughout a certain feeder, isolating between areas of high probability of faults and customer load becomes challenging and unrewarding. As such this index is intended to be representative of how concentrated and far, loads are from substations, and as such, how likely an energy storage system can improve customer reliability if presented as a solution.
Formulation
For a certain medium voltage feeder (f), assume each node (n) is assigned the following two
factors:
- Distance from Substation (Du(n)): The feeder line distance in circuit miles from node (n) to the substation in miles
- Name Plate Apparent Power (Sd(n): The summation of connected name plate distribution transformer capacity downstream of node n on all three phases in MVA
Evaluation
In analyzing the feeders shown in Figures 2 and 3, it was found that Feeder B, which represents an ideal case for the type of targeted feeders, had an Ar index value equal to 134.7 MVA.mile whereas a Feeder A shown in Figure 2, which represents a typical feeder where customer load is dispersed throughout the feeder, had an Ar index value of 16.1 MVA.mile. Accordingly, the factor between a good and a bad case is 134.7/16.1=8.4. This level of differentiation has proved effective in identifying the locations with the direst need for reliability improvement ESSs Duke Energy implemented this methodology within Cyme to identify its reliability improvement ES projects pipeline. As can be gleamed, Duke Energy started with cases with the highest Ar index.
Conclusions
The science to optimally identify the best locations for grid-tied ES systems is constantly evolving as the capabilities and specifications of ES systems evolve. However, at this point, Duke Energy has found great value in implementing both methods explained in this white paper to identify locations with the greatest need for ES systems.
References
[1] Eyer, J. (2010). Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide Study for the DOE Energy Storage Systems Program. Sandia Laboratories, 321, 232. doi: SAND2010-0815
[2] Ribeiro, P. F., Johnson, B. K., Crow, M. L., Arsoy, A., & Liu, Y. (2001). Energy storage systems for advanced power applications. In Proceedings of the IEEE (Vol. 89, pp. 1744–1756). doi:10.1109/5.975900
[3] Teleke, S., Baran, M. E., Huang, a Q., Bhattacharya, S., & Anderson, L. (2009). Control Strategies for Battery Energy Storage for Wind Farm Dispatching. IEEE Transactions on Energy Conversion, 24, 725–732. doi:10.1109/tec.2009.2016000
[4] Grid Energy Storage, US Department of Energy Report, December 2013.
[5] Akhil, A.A., Huff, G, Currier, A.B., Kaun, B.C, Rastler, D.M., Chen, S.B., Gauntlett, W.D., DOE/EPRI 2013 Electricity Storage Handbook in Collaboration with NRECA. Sandia National Laboratories Report, SAND2013-5131.
