Developing Energy Storage Roadmap for Electric Utilities

By Dr. Ralph Masiello

Energy storage systems (ESS) are a new technology for electric utilities which offer great promise in assisting with renewable energy resources integration, and in reducing capital expenditures by increasing asset utilization. It also has potential for improving system reliability. Quanta Technology has developed a comprehensive tool set and the associated methodology for developing a strategy and roadmap for energy storage size/technology selection and deployment plans, including a quantitative business case analysis.


Many utilities have begun pilot projects as they seek to learn about the technology and its applications. In California, especially, ESS has moved beyond the pilot stage; thanks to the CPUC deployment mandate (Rulemaking 10-12-007 pursuant to CA AB 2514) to achieve certain penetration levels by 2020 and 2030. For instance, large scale energy storage systems are playing a key role in mitigating the effects of the Alviso Canyon gas leaks. There are numerous other examples from smaller behind the meter installations to large grid scale applications.


Turning ESS from the pilot phase to a technology that is at least considered in distribution planning on a routine basis is another thing altogether. And understanding just how much ESS a utility might plan to have system wide in a multi-year planning horizon is still more difficult. Quanta Technology has developed a roadmap approach that addresses these questions and is a big step forward in enabling distribution planners to exploit energy storage.


A key question to address: why should a utility today develop a “roadmap” for ESS, if it is not imminently faced with regulatory pressure or with high renewables penetration? In short, it is primarily to be proactively ahead of the curve and prepared for the day when the issue comes to the table. Most importantly, many state commissions are not fully up to speed on energy storage yet, and may be getting information from the perspective of the energy storage industry or from developers that want to see energy storage classified as a “competitive” resource and utilities excluded from participation. A well-developed roadmap should include: clearly detailed benefits to the ratepayers, economics of different regulatory approaches, various energy storage business models and value streams, and deployment approaches that enable the utility to make a well-reasoned, technical, and economically robust argument.


One important element in developing a roadmap is to understand the various ESS technologies commercially available in the market and/or under development in the laboratory today, as well as projections for their cost and performance for the next few years. Such information is readily available from multiple sources; but, they need to be processed and presented in the context of a utility environment. Quanta Technology has taken this common roadmap practice a step further by incorporating a detailed bottom-up probabilistic and futuristic cost model of energy storage turnkey costs for several leading electro-chemistries, based on the Argonne National Lab BatPac tools. (http://www.cse.anl.gov/batpac/). This novel approach allows the development and analysis of different planning scenarios as part of the roadmap based on possible future evolution paths.

This method also allows utility managers and decision makers to monitor key flags in the energy storage development costs as new solutions or technology breakthroughs emerge in the market. This tool is getting constantly updated and extended to include additional technologies. We believe it will be useful in understanding the range of possibilities – after all, if ESS were already inexpensive and robust, it could be in widespread service today to bring capacity factors from the 40%’s to 80% or higher (defined as the average loading as a percentage of peak load allowed for the distribution circuit).


One challenge for utility planners is that commonly used distribution planning software suites do not represent the ESS model properly, or fail to represent how ESS actually operates in various applications. Quanta Technology has developed a set of software that simulates the energy storage control algorithms and the physical storage system and also integrates with common planning software platforms such as CYME™. These control algorithms can work with “centralized” (deployed at the substation) or “distributed” (multiple storage systems along the feeder) projects. The enhanced planning models support analyses of major applications such as: capacity deferral, arbitrage, backfeed prevention, power factor control, production intermittency smoothing and voltage control applications. They can work on a balanced three phase basis or on a per phase basis.


These algorithms are more sophisticated than those found in many published works. For instance, the capacity deferral algorithm acts to peak shave MW loads, but it can also take advantage of the reactive power compensation to exploit near-perfect power factor correction. This receives the last few pecentages of capacity out of a conductor. The application can also work on a phase amperage basis, instead of individually managing active and reactive power flows. Integrated with common power flow tools, they provide time-series quasi-steady state-type analysis by performing minute-by-minute simulations of the controls and the energy storage simulation (or even higher time-based resolution studies for managing voltage fluctuations). These applications and simulations are what is needed to perform the detailed engineering validation of energy storage projects, such as the evaluation of system configuration and the locations of a given circuit for chosen applications.


The control algorithm for smoothing large and sudden voltage fluctuations (beyond the permissible ranges due to impact of high penetration of PV installations) on the grid is equally sophisticated. This modeling and simulation analysis can exploit “smart bi-directional inverter” capabilities to manage not only storage MW charge/discharge rates, but also to adjust inverter Mvar flows to regulate voltages at the ESS point of interconnection and on the nearby feeder sections. An example case of investigating the improving effect of the voltage smoothing application through an ESS for a circuit with large amount of solar PV systems is shown in Figure 1. The results of 60-minute time-series voltage analysis for the circuit for the case of no ESS (trend in blue), and after applying the ESS application (trend shown in red) are given in Figure 1. The before and after trends clearly show voltage spike mitigation and smooth transitions as the PV production changes due to cloud alternations.

 

 

But how do you know which circuit can benefit from an ESS, and how do you know what power and energy rating ESS to begin with when you perform the detailed engineering?
Performing detailed time series simulations across a large number of circuits is tedious and unworkable for most utilities. A reasonably accurate method to assess a large population of feeders, and to determine where there is a viable business case for energy storage, the size of the system, and so on, is required. Quanta Technology has developed MATLAB analytics that can process a large population of feeders and to determine, for instance, the best configuration of energy storage for capacity deferral as well as the best deferral period economics compared to normal capacity upgrades. The economics are evaluated in terms of customary utility revenue requirements as well as cash flow.

A key element in developing viable energy storage business cases is how the energy storage resource can passively or actively participate in energy markets for the benefit of the ratepayer. The Quanta Technology methodology is unique and powerful in that it can explore multiple alternative energy storage business models in parallel with “utility” applications and it can simultaneously analyze their combined economic benefits. The Figure 1 Feeder load and allowable rating simply shows the feeder loading curve-vs-rating throughout the day. A modest overload exists from hours 11 to 19. In Figure 2 the loading is reduced by the BESS dispatch. (BESS = “Battery Energy Storage System”) In figure 3, BESS dispatch – Capacity Deferral and Time Arbitrage – the BESS dispatch is refined to not only avoid the overload, but also to manage the BESS charging and discharging, so as to reduce energy costs at the substation (costed at Locational Marginal Price for that station hourly) via time arbitrage (it is technically arbitraging the hourly prices via time shifting energy demand, we use the term “time arbitrage” for this). Figures 4 and 5 compare the cases and show more detail of the BESS charging and discharging.

The capacity deferral application is inherently a peak shaving application which shifts some wholesale or transmission-level energy draw from on-peak to off- peak, allowing the deferral of an upgrade required to meet just a few peak hours a year (see Figure 5 and 6). As load grows, the peak-shaving occurs more frequently. In any regulatory environment where the transmission energy carries a higher cost to the distribution utility and its ratepayers on-peak/off-peak (TOU pricing or dynamic hourly pricing, for example), the peak shaving automatically generates savings. The Quanta Technology analysis platform accurately computes these savings looking at the hourly profiles across a full year.

When the energy storage is not needed for peak-shaving reliability purposes, it can still be used for time arbitrage or energy shifting. It can be done in a way that maximizes savings. Quanta Technology uses a sophisticated optimization scheme to compute the optimal scheduling of energy charging and discharging to maximize savings across the year (see Figure 7 and 8). Note, this can be done without active market participation in the bidding process. The storage is a “price taker” in the market environment. An valid application for a regulated or municipal utility is as well.


Going a step further, the unneeded energy storage capacity can be used for participation in ancillary services markets. The energy storage economics can be further enhanced by using some of the capacity not for time shifting but for providing reserves and regulation. The Quanta Technologies optimization co-optimizes the allocation of capacity to energy-shifting/time-arbitrage. Delivery of the ancillary products reserves regulation, while meeting needs for reliability applications (See Figure 9 and 10). This could be done by the utility as a participant or as a “shared application”, where a 3rd party market-participant is paying for the use of the resources. Gaining the revenues from ancillaries requires an “active market participation” as a bidder, note. Figure 6 compares Capacity Deferral (“CD”), Capacity Deferral plus Arbitrage (“CDA”), and Capacity Deferral plus Arbitrage and Ancillaries (“CDAAM”) patterns the charging and discharging, as well as how the BESS capacity is allocated to the different applications. There is a “lot going on,” and depending upon the relative pricing of the energy and different ancillaries, BESS capacity, and overriding capacity deferral needs, the patterns can change dramatically from day-to-day, or as a function of BESS capacity. In the plots, the energy prices (only) are shown as Y axis, and the ancillaries prices will vary, more or less.

Results from this overall roadmap are insightful and quite different than higher-level or spreadsheet approaches. Blanket assessments based on “typical” data that do not look at hourly details will either overstate the number of feeders that can benefit from storage – badly – or fail to identify those that can, due to pessimistic and simplistic assessments of benefits. Even more importantly, the roadmap process can be then transferred to real-world planning using the detailed engineering analytics now available.


We then analyze how storage penetration develops under these three scenarios (CD, CDA, CDAAM per above). under “rules” for deployment based on utility economics as used in rate-case analysis. This includes the cost of storage, ratepayer benefits, and the utility ROI investment hurdles. Storage deployment on feeders in the population increases at different rates over time, based on how load growth and energy prices will affect the economic viability of BESS on a particular feeder. Figure 7 shows the total storage deployment over time for a large feeder population (> 1000 feeders). This allows analysis of the sensitivity of storage viability against parameters such as energy prices, interest rates, storage costs, storage efficiencies, and storage lifetime/depreciation charges. Figure 8 reduces all of this to cumulative ratepayer benefits – what matters at a day’s end.

Furthermore, plots over time can be created to show the ratepayer cost accrued for each year of energy storage operation (Figure 9). These are shown for the cases of CD, CDA, and CDAAM operation for batteries. The benefit is the time value of the deferral plus the arbitrage while the battery remains to collect arbitrage / ancillary revenue after distribution station or feeder capacity upgrades. Generic data for capacity costs are used, but it is possible to instead use specific feeder costs if such analysis exists.. The orange line represents the benefit of the battery without any arbitrage. The red line represents the benefits of the battery with arbitrage. The yellow line represents benefit of the battery with inclusion of the ancillary services.

Conclusion
It is possible to develop an overall “roadmap” for BESS deployment in a distribution system that pragmatically assesses a large population of stations and feeders and which explores different storage applications/business models as well as the sensitivities to technology costs, energy prices, interest rates, and so on. The full roadmap development then becomes a good vehicle for communicating the utility perspective on storage to regulatory bodies, and for informing  the utility management. The roadmap is also an effective way to screen a large feeder population for storage viability after the detailed engineering analysis using specific BESS products and controllers against individual feeders has been performed.
Energy Storage is “happening” today. Utilities are well advised to get ahead of the problem and have a roadmap ready at hand for the day when management asks for it! Quanta Technology can assist in the development and maintenance of that plan using proven methodologies.

 

Document Authors: 
Dr. Ralph Masiello
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