< Back Mark Warner Senior Project Manager Mark Warner, a Project Manager at Camelot Energy Group, has over 5 years of experience in the renewable energy development and EPC contractor space. Mark has extensive background in project development, siting, energy analysis, design, construction planning, and permitting for commercial and utility-scale solar projects. Mark holds a Bachelor of Science Degree in Mechanical Engineering Technology from the University of Maine. mark.warner@camelotenergygroup.com

< Back Aaron King, PE Director of Programs & Policy Aaron is a Senior Project Engineer at Camelot Energy Group and has over 10 years of experience in the solar and storage industry. Aaron works across Camelot’s Technical Advisory and Owner’s Engineering departments supporting clients on a wide variety of services. He has acted as a project manager and technical lead on different projects and portfolios at all different stages of development from M&A due diligence, design and permitting, construction monitoring, site inspections, testing & commission, and asset management. Aaron started his career designing commercial rooftop systems and solar canopies. Aaron has also worked as a technical PV consultant and owner's engineer with a range of different clients including utilities, property management companies, EPCs, municipalities, state governments, and large universities. Aaron is a licensed Professional Electrical Engineer (Power) in the state of Massachusetts and holds a M.S. in Energy Systems Engineering from Northeastern University and a B.S. in Mechanical Engineering from Johns Hopkins University. aaron.king@camelotenergygroup.com

< Back Shawn Shaw, PE Founder, CEO Shawn Shaw is the founder and CEO of Camelot Energy Group and has over 21 years of experience in the renewable energy and energy storage industry. During that time, Shawn has supported public programs in more than 10 states and acted as technical advisor to many of the largest banks and financiers in the world, providing technical due diligence, owner’s engineering, and independent engineering on well over 8 GW of solar PV and 5 GWh of energy storage projects in the US, Latin America, and Europe, ranging from design and construction of offgrid island power systems to acting as Independent Engineer for financing multiple 400MWh energy storage projects in complex US markets. Shawn has experience working with a wide variety of equipment suppliers, project developers, banks, financiers, government entities, and incentive program administrators. Shawn is a registered electrical engineer (Power Systems) in New York State and holds a B.S. in Applied Physics from Rensselaer Polytechnic Institute. Recently authored Energy Storage Systems: Based on the IBC, IFC, IRC, and NEC in collaboration with the International Code Council. shawn.shaw@camelotenergygroup.com

The Clean Energy Helpdesk At Camelot, we believe that local authorities, communities, local governments, and non-profits should have access to the same expert advice that is available to developers and big banks. After all, if we are going to power a just and sustainable society with clean energy, it is going to take support from everyone. As such, we have launched the Clean Energy Helpdesk, wherein members of these groups can ask for pro bono support from Camelot’s team of experts. Requests will be responded to in the order received and Camelot staff will provide up to 8 hours of expert consulting to help address topics such as: Training and technical support on battery energy storage systems for local authorities Technical assistance on energy storage codes and standards Guidance on zoning best practices Q&A with municipal officials about a planned project Business case for community renewable energy If you are a community, local authority, local government entity, or non-profit and need help with questions like these, please fill out the form below. We will be glad to review your questions with you and offer help. Clean energy should be a win-win for everyone, so if you have questions please reach out. How can we help? First name Last name Organization* Email* Phone Tell us what you need help with... Send

Feb 13, 2025 NERC’s New Compliance Threshold Big changes are coming for renewable energy projects in North America. Starting in May 2025, NERC will require all inverter-based resources (IBRs) with an aggregate nameplate capacity of 20 MVA or more—connected at 60 kV or higher—to register as a Generator Owner (GO) and/or Generator Operator (GOP). If your solar, wind, battery storage, or fuel cell project falls into this category, compliance is no longer optional—it’s mandatory. 1. Understanding the New Requirements Historically, NERC registration was only required for facilities above 75 MVA and 100 kV, but these new thresholds mean that many mid-sized energy projects will now be subject to NERC oversight for the first time. The goal? Enhancing grid reliability as more inverter-based resources connect to the bulk power system. 2. Key Steps for Compliance If your project meets the new criteria, here’s what you need to do: Assess Your Facilities – Determine if your current or planned projects exceed the 20 MVA and 60 kV thresholds. Begin the NERC Registration Process – Registering with NERC isn’t an overnight task. The process can take 6–12 months, depending on factors like documentation requirements, technical assessments, and coordination with regional reliability entities. Early registration helps avoid bottlenecks and ensures compliance well ahead of the May 2026 enforcement deadline. Develop a Compliance Plan – This includes: Meeting NERC Reliability Standards , such as PRC-024 (Generator Frequency and Voltage Protection) to ensure proper coordination with the grid. Updating operational procedures , like implementing real-time monitoring systems to log and report grid disturbances. Training personnel on cyber and physical security best practices to align with CIP (Critical Infrastructure Protection) requirements. Conducting regular audits to ensure ongoing compliance with evolving regulations. Engage with Experts – Compliance can be complex, and mistakes can be costly. Partnering with experienced professionals ensures a smoother transition. 3. How Camelot Energy Group Can Help At Camelot Energy Group, we can assist you with NERC registration and compliance support for energy storage and renewable energy projects. Whether you’re navigating the registration process for the first time or need a tailored strategy to meet NERC’s evolving reliability standards, our team of experts is here to help. From registration assistance to ongoing compliance support, we provide: End-to-end NERC compliance services tailored to your specific project Technical assessments to determine your compliance obligations Regulatory expertise to help you avoid penalties and operational risks With the May 2026 compliance deadline approaching, early action is critical. Don’t let regulatory hurdles slow down your project—reach out to Camelot Energy Group today to ensure you stay ahead of the curve. Contact us to discuss your NERC compliance strategy! < Back Back

Jun 2, 2026 Garden State Energy Storage Program (GSEP) Phase 1, Tranche 2 The Garden State Energy Storage Program is a New Jersey state initiative created under the Clean Energy Act of 2018, which requires New Jersey to deploy 2,000 MW of energy storage by 2030. Phase 1 focuses on large-scale, transmission-connected battery storage projects and uses a competitive bidding process. Tranche 1 wrapped up in early 2026 with awards to three projects totaling 355 MW. Tranche 2 was launched on March 4, 2026, to procure the remaining 645 MW needed to hit the 1,000 MW Phase 1 target. It was also directly triggered by Governor Sherrill's Executive Order No. 2, which declared a utility affordability emergency and directed the BPU to open Tranche 2 within 45 days of January 20, 2026. The program is funded through the BPU's existing Clean Energy Program budget and Orsted Settlement funds - no new charges will be added to ratepayer electric bills. In fact, BPU analysis found that in most scenarios the capacity savings generated by Tranche 2 projects will exceed the cost of the incentives, making the program a net financial benefit to ratepayers. Who Can Apply? Private and government entities only - electric distribution companies are not eligible Project must be at least 5 MW AC of installed capacity The project must be physically in New Jersey and connected to the PJM transmission network Standalone storage projects and storage paired with solar or other Class I renewable energy resources are eligible Storage paired with solar cannot be received or plan to receive incentives from the Successor Solar Incentive (SuSI) Program for the same storage capacity All commercially available battery technologies are eligible Equipment must be new as projects that have already started construction are not eligible Incentive Structure Winners receive a fixed annual incentive payment per MW of effective nameplate capacity, paid out over 15 years Bids are submitted in $ per MW of effective nameplate capacity and projects are ranked in ascending order of their relative costs Annual payments are tied to dispatch availability - projects must be available for dispatch at least 7,900 hours per year to receive their full payment and are also adjusted downward if the project loses storage capacity due to battery degradation over time Application Process There are two stages to the application process: Stage 1 - Pre-Qualification Submit a Pre-Qualification Request showing the project meets basic eligibility and maturity requirements BPU Staff reviews the submission and notifies applicants of any deficiencies Applicants are given the opportunity to correct deficiencies before the final deadline Stage 2 - Final Application and Bid Submit a complete Final Application along with your incentive bid price Must be submitted by August 7, 2026 Documents Required Proof of site control (lease agreement or property title) Detailed permitting plan, including an execution plan for any permits not yet secured at the time of submission Financial documentation showing the ability to fund construction Evidence of PJM interconnection progress along with path towards interconnection prior to the cliff date Safety and code compliance assurances, including NFPA 855 compliance and UL 9540 certification Year-by-year DC and AC degradation projection for the project's operational lifespan Non-refundable application fee of $200 per MW of nameplate capacity Application Process Additional considerations: Projects on brownfield sites or serving overburdened communities should include relevant supporting documentation, as these factors can positively influence the Board's award decisions Resources: All forms and instructions are posted at cleanenergy.nj.gov/programs/energy-storage/garden-state-energy-storage-program-phase-1-tranche-2 Questions can be submitted to energy.storage@bpu.nj.gov Answers are posted publicly on a rolling basis for all applicants and the public to view Dates to Remember Applicants can submit materials up until August 7, but only those who pre-qualify by June 10 are guaranteed a deficiency review and chance to correct issues before the final deadline. After August 7, no changes are accepted. All bids remain valid for nine months from that date. What's Next? After an Award Winners have 30 days from receiving their award letter to: Register with the BPU Submit a milestone report with estimated dates for: Financial closing Start of construction Planned Commercial Operation Date (COD) Guaranteed Commercial Operation Date (COD) Construction cannot begin until the BPU issues a conditional approval following a complete registration review Delay Penalties: Projects that miss their planned COD are penalized $1,000 per MW per day of delay The Division Director can grant extensions of up to 180 days for good-faith delays Extensions beyond 180 days are only granted in the case of a documented force majeure event Risk of Losing the Award: Projects that miss their guaranteed COD by 36 months risk having their incentive award revoked entirely The Board may waive this penalty if the applicant demonstrates good cause in writing Raafe Khan < Back Back

< Back Bill Atkinson, CEM Senior Project Engineer Bill is a Senior Engineer with over 17 years of experience in the renewable energy and energy storage industry. During that time, Bill has worked extensively developing and implementing rigorous quality assurance and inspection processes for clean energy incentive programs and Bill has inspected more than 530MW of PV and energy storage systems. Bill has performed hundreds of design reviews, technology evaluations, major agreement reviews, and site assessments. Bill is a Certified Energy Manager, Certified PV System Inspector, and holds a B.S. in Community and Regional Planning and Sustainable Technology from Appalachian State University. bill.atkinson@camelotenergygroup.com

Aug 23, 2024 Solar Availability Series Part 2 Welcome back for Part 2 of Camelot’s series on solar availability, which is an appropriately hot topic as the industry continues to mature. If you’re just joining us for the series, Part 1 can be found here , and it includes some background on the current state of industry assumptions. Today we’ll cover the not-so-simple task of calculating and reporting downtime, along with some implications. Subsequent parts will describe ways of maximizing availabilities and Camelot’s official stance as an IE. Thank you for joining us! Introduction As expressed in Part 1 , availability is a way of quantifying lost generation potential due to outages; it measures whether a component or system is operating when it ought to be. An availability of 100% at any given time means everything is operating when it should, whereas an availability of 0% means the entire component or system is offline. The plot below illustrates a case where the entire site stopped producing power and was restored the following day. There will be more on this figure later. SCADA Data Collected at a Utility-Scale Solar Project Over Two Summer Days To better summarize the operations at a project based on high-resolution data collected at a site, production and availability data are typically aggregated and reported into monthly operating reports (MORs) which are shared with key stakeholders on a project. Monthly numbers are also aggregated into quarterly and annual reports. Because there is typically some seasonal variation in downtime, most folks will refer to annual availability numbers when benchmarking against expectations, and so when we talk about availability assumptions, we are referring to annual averages . A Deeper Dive Into Metrics The simplest but less useful measure of availability is time-based. It’s calculated as Uptime/(Uptime+Downtime) , so it only considers the time it takes to bring the system back online over the period. However, the most useful measure of availability in most contexts is energy-based . It uses an estimate of the energy lost during the period, and is calculated as Actual Production/(Actual Production+Lost Production) . We care more about lost production than anything; when building out a financial model, we multiply pre-downtime production by the assumed availability to arrive at post-downtime production, so we want to use energy-based availability if possible. This is often why, despite PVSYST’s ability to model downtime, the loss factor is most commonly applied outside of PVSYST; the software interprets the loss as time-based and will apply random downtime throughout the modeled year, resulting in an unintended energy-based loss. Time-based availabilities are not well suited for financial modeling, and we recommend time-based metrics only be used if they are defined and used in O&M contracts, as we’ll touch on below. How are uptime, downtime, actual production, and lost production determined? Uptime and downtime are relatively easily defined on a site-level. SCADA systems will typically flag periods when the site or major components are down, and the duration of these events will sum to be the downtime for the site. In cases when a portion of the site is offline, uptime is often weighted by the portion of the affected site (ideally on a production-potential basis). Actual production comes directly from the power meter, typically at the point of interconnect (POI). Calculating lost production usually involves several steps which are all built into the software used to log and report operational data: Determine “expected production” for each timestep based on the energy model for the site and the existing, measured site conditions (eg irradiance). The model should be validated as an accurate representation of the relationship between measured inputs and production. Referring to the plot above, expected production is the red line, which is based primarily on the plane-of-array irradiance (green line). Calculate the energy lost for each timestep, which is represented by the “Δ” in the plot above. Sum energy lost at each timestep across the entire reporting period. The same calculations hold for any reporting period. To calculate an annual availability number based on monthly data, you can sum the monthly time or production values before doing the same math, or take an energy-weighted average of the monthly availability numbers. What about data gaps or QC? Unfortunately, we see data concerns very often at operating sites, and garbage in equals garbage out. Some meters and sensors will have redundancy onsite in case one fails, but if we run into data concerns due to whatever issues arise, all may not be lost. Even in a system-wide SCADA outage or memory failure, some form of data are always being collected or modeled onsite, and inferences can be made. As a couple examples: If an inverter power meter at a site with 5 central inverters starts to fail, but the inverter should still be online, an operator can verify the inverter’s availability using the POI (revenue) meter. The total power at the POI meter minus the power from the other inverters should roughly equal the power from the fifth inverter (“roughly” because of electrical losses and measurement uncertainties, which can generally be determined from operational data anyways). Even if the entire site goes offline for a period of time and no actual measured data is available, besides the power flowing to the grid at the POI, high-resolution meteorological satellite data can be used. Operators can observe the relationship between the solar resource and production during a fully-operational period to fill in the gaps and define expected production. Admittedly, many O&M providers will not go to the effort to fill in data gaps when they occur, which can lead to missing or inaccurate data. This, in turn, can lead to an inaccurate understanding of overall system performance, which in some cases can even impact a project’s valuation: availability is a key factor when reforecasting a project’s future production, and we have seen cases where missing data makes a significant difference in the uncertainty (leading to lower P99s). This is where Technical Advisors such as Camelot Energy Group can help ensure you are working with the most accurate data you can. Not only can availability be calculated based on a fundamentally different basis (time vs energy), but we need to be careful to scrutinize what is included in the definition as well. Until now, we’ve focused on System Availability, but you might find other metrics floating around and serving other purposes. A few common terms and measures are: System Availability - Captures all quantifiable downtime over the entire site for the entire period, with no carveouts. The following is a list of possible synonyms, noting that the definition of every availability metric should be scrutinized because they can be inconsistent: Plant Availability Project Availability Operational Availability Total Availability Overall System Availability (OSA) An inverter fire which caused system-wide availabilities to drop for a significant period of time Component Availability – Captures only the availability of an individual component over a given time. These commonly include inverter availability or module availability , but can be broken into any components, including trackers. Sometimes referred to as Manufacturer Availability . Contractual Availability – Sometimes also referred to as Guaranteed Availability, this metric is the most commonly-confused one of them all. It should be clearly defined in an O&M agreement, and the downtime it includes can vary. The denominator in the calculation is often more complicated than simple “total time” or “total production” during the period, and both parts of the equation can include carveouts for periods which are often deemed outside of the operator’s control. This is the most commonly-reported time-based availability, but we are seeing an increase in contracts which define Contractual Availability on an energy basis. This incentivizes operators to perform maintenance at more optimal (lower resource) times. Balance of System (BOS) Availability – Includes the availability of all components other than the modules and inverters, such as wiring, mounting structures, and monitoring equipment. Sometimes also termed Balance of Plant (BOP) Availability, but as always, the definitions must be scrutinized. Grid Availability – Captures downtime when the grid is not available to accept power generated by the project. This is the most common carveout for contractual availabilities, as it is almost always outside the control of the operator. We hope this moderately deep dive into solar availabilities helps to put the numbers into perspective and emphasize the importance of understanding what metrics you are looking at when evaluating a project’s uptime. We can always go deeper into the topic, and we’d be happy to support with any questions you may have. The next article in this series will cover a number of ways of maximizing availability and improving your metrics. In the meantime, for questions and more details about Camelot Energy Group and our distinct attitude towards these issues, please reach out at info@camelotenergygroup.com . About Camelot Energy Group is a technical and strategic advisor to owners and investors in clean energy and energy storage projects, programs, and infrastructure. Guided by our core values of courage, empathy, integrity, and service we seek to support the energy needs of a just, sustainable, and equitable future. Our team has experience in supporting 7+GW of solar PV and 10+ GWh of energy storage and offers expertise in technology, codes and standards, engineering, public programs, project finance, installation methods, quality assurance, safety, contract negotiation, and related topics. Our services are tailored to a providing a different kind of consulting experience that emphasizes the humanity of our clients and team members, resulting in a high quality bespoke service, delivered with focus, attention, and purpose. Key services include: -Technical due diligence of projects and technologies -Owner’s representative and engineer support -Strategic planning -Training and coaching -Codes and standards consulting -Contract negotiation and support < Back Back

Mar 27, 2026 From lab to grid: making LDES bankable The grid already faces multi-hour and multi-day imbalances caused by transmission constraints, renewable intermittency, and extreme weather volatility. The rapid addition of data centers further complicates this situation, adding peak load to an already stressed grid. As the traditional 2-4 hour storage market tightens, and large-AI-based loads demand a higher degree of reliability and redundancy, long-duration energy storage (LDES) is gaining serious attention from developers, Independent Power Producers (IPPs), utilities, and investors. LDES matters now more than ever because: Renewable penetration is accelerating , leading to increased curtailment. Industrial electrification is increasing baseload demand , adding stress to transmission and distribution systems. Peak load is growing , leading to overbuilding of generation. Extreme weather is stressing grids globally , increasing the need for flexibility. Contrary to popular belief, LDES is not a future solution. The technologies exist today, but have yet to be successfully field-tested in long-term projects. When deploying LDES at scale, the deciding factors will be cost, performance, and commercial viability, which will all determine the market’s true winners and losers. The chemistry war: A distraction from the real issue Energy storage professionals have debated which chemistry or brand name is ideal for long-duration applications. This debate, while lively, is besides the point. Industry efforts should focus on technology-agnostic procurement – picking the technology that fits the use case. While most markets still anchor to the 4-hour lithium-ion benchmark, reflecting yesterday’s grid needs, intraday needs exceed 4 hours, and multi-day reliability events are increasing. Lithium currently wins on performance and experience, with ~90% round-trip efficiency, a mature bankability profile, and proven deployment at scale. However, lithium performs best for 4-hour use cases (or less) and 15-20 year technical life expectations. If efficiency, upfront capital outlay, and energy density are critical to the project, lithium-ion typically wins. But when fire safety, total cost of ownership, a fully or primarily domestic supply chain, or >8-hour discharge needs dominate, a non-lithium technology may be superior. “The longer the better” is the right answer for most LDES projects, but each deployment will have varying problems and solutions. Longer doesn’t just mean longer discharge duration, but also a longer calendar life. Duration should be defined by system need, not by lithium’s historical average, and the right chemistry cocktail should be tailored not to industry standard but to individual use cases. Scaling too fast will break things Despite record installation numbers, the long-term degradation performance of utility-scale storage remains uncertain. Most assets are underwritten on lab-based, accelerated testing, so we truly don’t understand how these systems are expected to perform between years 10 and 20 of their operating lives. The utility-scale storage industry is little more than a decade old, and no battery fleet has reached end-of-life. At this stage, decommissioning frameworks remain theoretical rather than concrete. Commissioning engineers and project managers currently rely on performance metrics documented by accelerated lab testing instead of real-world use cases and stressors. Furthermore, few asset owners of deployed projects possess true fleet-level transparency regarding battery health and key dispatch metrics. Taken together, these factors make project failure – or faster-than-promised degradation – highly likely. Depending on the project structure, some teams will catch and fix these issues over time. However, many won’t have a fix available to them due to the rapid evolution of cell form factors and subsystem hardware and software architecture. With storage remaining untested in long-term, real-world projects, industry skepticism remains a hurdle. Overcoming this will require LDES demonstrating real-world degradation performance, ease of integration, enhanced safety, lower lifecycle costs, and reliability comparable to lithium. Long-term financial viability also matters. Buyers need confidence that the supplier will be around for multiple decades to provide technical support, spare parts, and warranty response. We also need to ensure that we close the gap between economic forecasts and operational realities, and how risk is underwritten. Hopefully, with deployment and manufacturing scale, the economics will follow, making LDES the right choice for energy generation projects and facilities. Policy frameworks shape LDES deployment now, but they remain far behind Historically, ancillary service markets have been the early proving ground for energy storage around the world. Because these products reward fast response over short time windows—typically minutes to about an hour—short-duration batteries had a built-in advantage: they could follow rapid control signals and deliver frequent, shallow charge-and-discharge cycles that align well with today’s battery performance. But as growing renewable generation pushes fossil “thermal” plants further down the dispatch order and into a more backup role, the grid increasingly needs LDES to do what fast services can’t: capture excess clean energy that would otherwise be curtailed, provide resilience and flexibility during longer imbalances, and help keep the lowest-cost electricity available when it’s needed. That said, the current ancillary service market designs reward speed, not endurance. If fundamental price signals evolve to incentivize lower-cost, longer-duration assets that perform at a high reliability standard, the market will rise to the challenge. LDES needs clear market incentives. Those signals may show up over time, but capacity markets could help make long-term projects financeable now. The recent federal policy changes promoting domestic manufacturing now reshape the equation. Lithium-ion supply chains remain heavily dependent on both mining and processing outside the U.S.. With new FEOC guidance under the OBBB and tariff policy implementation, any critical mineral material that can be found domestically gains a huge homefield advantage in cost and tax credit eligibility. Many lithium alternatives in LDES, such as zinc and sodium, draw on U.S. deposits. While gaining traction, these technologies remain untested at a mass scale and still lack the affordability and performance of lithium-ion. Policy levers can accelerate innovation and encourage market adoption, and policymakers have many in the works. LDES provides essential infrastructure. As the grid incorporates more renewable energy sources and retires older fossil fuel facilities, only massive deployment and integration of LDES can guarantee grid reliability. The technology exists; companies are building it, and deployments are happening. Yet cost competitiveness, efficiency gaps, and operability at commercial scale remain real barriers. Companies that can combine cost discipline, bankability, and execution excellence will define the next era of grid infrastructure, and we need it sooner rather than later. As featured in ESS News. What's your take? Email us at hello@camelotenergygroup.com for any questions! Raafe Khan < Back Back

Sep 11, 2024 Solar Availability Series Part 4 Welcome back for Part 4 of Camelot’s series on solar availability. If you’re just joining us for the series, here are some links to parts 1 , 2 , and 3 . We’ve set the groundwork with a summary of the ongoing validation efforts from IEs, and the resulting changes the industry is making to their assumptions. We’ll revisit their reasoning here. We’ve also described how availabilities are calculated and reported, and touched on ways of maximizing availability by minimizing downtime. If you’ve followed along with the last few parts and you’ve been waiting for our own stance as an Independent Engineer (IE), look no further! Thank you for joining us. Re-Setting the Scene Until somewhat recently, the utility-scale solar industry didn’t have the kind of established history needed to accurately predict or validate what long-term average availabilities will be at newly-proposed projects. Engineering judgement said that a relatively simple solar project would see the equivalent of about 3-5 days of total site outages per year, leading to expected availabilities of about 98.5% to 99.2%. For modeling simplicity, most everyone assumed a relatively consistent availability throughout a project’s lifetime. However, as projects became operational, the industry started to question itself. Especially early in new projects’ operational lives, downtime was high and availabilities were lower than expected due to teething issues. Even after the initial startup period, many folks started seeing trends with their average availability levels below what they had hoped. Over the last year we have started to see the beginnings of some robust data-backed approaches to redefining availability assumptions, aided by all the new operating data which is available to us. There have been three IEs who have recently updated their assumptions based on aggregated data from the projects they supported. ICF led the charge with its performance paper published by kWh Analytics in 2023. DNV and Natural Power followed suit with their own methodology updates in early 2024. Others with access to the data have weighed in as well, from NREL to kWh Analytics. Here, we focus in on the results of the IE validations, each of which took slightly different approaches and used different data sets. The table below summarizes the projects which went into the IEs’ comparisons, and some key comments from their results. We’d like to highlight a few key findings from this comparison: Every IE relied on data from monthly operating reports produced by the operators, which are rarely independently calculated or verified. As described in part 2 of this series, there is no single, standard way that availabilities are defined or reported across the industry. The conclusions from these studies should be interpreted carefully, especially because the data QC processes have not been explicitly described. DNV’s analysis used more data and resulted in recommendations which are more clearly tailored to the sites. ICF found that fixed tilt systems showed lower availabilities than tracker systems while DNV found the opposite. Despite every IE noting lower availabilities early in a project’s life, only DNV adjusted their recommendation to treat the first year differently from other years. No IE has taken a stance on availability changes later in a project’s life yet. Here is a summary of the IE’s post-validation default availability recommendations. As you can see, only DNV makes a distinction between different kinds of projects at this time, though every IE noted that they are open to changing their assumptions based on project-specific data such as operator or technology history. In practice, however, IEs are often reluctant to deviate from their standard assumptions, as this requires going out on a proverbial limb. While that conservatism is understandable, it may be producing unintended consequences. For instance, if an IE will not give “credit” for more robust technology choices or operating strategies, then owners have little incentive to consider any options but those that can be considered “bankable” at the lowest possible cost. This approach penalizes owners for considering better than baseline equipment, spending more on O&M, or otherwise looking for creative solutions to improve availability. The need for more data was a theme repeated by each company, and this will likely ring true for as long as we do this kind of work. Our availability assumptions will need to be updated regularly, just like we update our approaches to Energy Yield Analyses. Camelot’s Recommendations The Camelot team is compiling the data needed to supplement these studies and validate our conclusions, and we welcome the opportunity to work with industry partners on this effort. In the meantime, we base our own recommendations off the meta-study described above and in Part 1. Without further ado, here is our own take on availability projections: Until we have more information, we should not be differentiating between different mounting types . ICF’s and DNV’s observations contradicted each other. It’s likely other factors influenced the analyses, especially the sample sizes and quality of the input data. The factors which can impact downtime should be studied further, which means collecting more data, ensuring its accuracy, and capturing all potentially-relevant project details. In addition to mounting types, the difference between inverter technologies must be studied further as one of the primary sources of downtime observed at operating sites. For instance, the higher availability noted by DNV on smaller fixed-tilt sites than larger fixed-tilt sites may indicate a reliability advantage for string inverters over relatively small sites with central inverters. This would align with our general experience with operating sites but the data to positively confirm this is not yet available in sufficient quantity. The major sources of downtime should be studied and modeled separately . Using an overall system availability as a metric can muddy the waters significantly, especially when trying to tease out the impact of different design decisions on future performance. When performing energy yield analyses for wind energy projects, some IEs will include assumptions for balance of plant availability, grid availability, and turbine availability separately. Not only can this improve our validations (data allowing), but it will improve the way we assess technology tradeoffs at the design stage. Swapping out a more robust system for a less-robust one should impact only the downtime assumption for that system. Camelot recommends the industry work towards a bottom-up availability model based on historical failure/downtime data at the module, tracker, inverter, MV, HV, and BOS levels. These levels correspond with likely failure points within the system and provide a lowest common denominator that can be adjusted during project design to optimize expected availability. Ensuring this approach has buy-in from IEs will provide a financial incentive to specify better equipment and design better sites. Year-1 availability should be modeled separately from later years due to initial startup issues observed in each validation. Nearly all financial models are already set up to account for annually-varying losses, so adjusting our assumptions based on the clear signals we see from the data appears to be a no brainer. The industry should start modeling a ramp-down in availability later in projects’ life, as DNV may have alluded to, because component failure rates impact availability trends. Without more data, it is difficult to say the magnitude of the decreases because of the other factors at play. However, based on our experience modeling availability at other infrastructure projects, Camelot considers it reasonable to model availability as a ramp-down as a project nears the end of its design life. The “bathtub curve” shown below is an Engineering concept which supports this idea. It shows how infant mortality failures likely contributed to the observed availabilities in the first 6-12 months of operation, and highlights the further need for more operational data as projects age. This is applicable to individual components in many physical systems. Aggregated across an entire system and accounting for typical replacements and maintenance, one might expect to see a flatter availability curve, but with some consideration for early- and late-stage failures. We have seen this already with 10-15 year old PV sites, where owners struggle to obtain compatible replacement equipment that can be “dropped in” to replace original equipment onsite. As technology continues evolving quickly, we can expect new module types, inverter technologies, sensing devices, and code requirements to all play a role in the maintainability of PV sites in the late stages of their useful life. Camelot’s Balanced Approach The summary below provides a graphical representation of each IE’s default availability recommendations over time, and includes Camelot’s own recommended defaults (when no other project-specific information is available). We note the following: Camelot’s approach accounts for the size impacts observed by DNV, which appears to be a strong signal in the data, but does not differentiate between technologies until more information is made available supporting the distinction. Much like DNV, Camelot’s recommended availability starts slightly lower in year 1 before reaching steady operations, as is supported by all studies. We recommend modeling availability declines after year 20 based on several factors, including the bathtub curve concept described above, the typical useful life for major components, and our expectation that the impacts of mid-life failures will likely offset by the efficiencies gained from experience during operations. While we see this assumption as a necessary recognition of late-stage wear-out failures, it’s worth noting that its impacts on a financial model are muted by the time value of money. On average, Camelot’s assumptions are less pessimistic than ICF, and strike a balance between the assumptions reported by Natural Power and DNV. Camelot will consider quantitative adjustment to our base availability assumptions for sponsor efforts that materially result in increased reliability, such as: Demonstrating better than average historical availability for project- specific equipment (e.g., inverters) through operational data (as described in item 3 above) Adding incentives to O&M Agreements for increased availability, beyond simply guaranteed levels Purchasing extra spare parts for more vulnerable system components likely to need frequent replacing Investing in predictive analytics and above-market O&M services to reduce the frequency and severity of unplanned maintenance events While these recommendations may be Camelot’s “default” values, as an IE which cares heavily about the accuracy of our projections, we will always consider factors such as operator experience or the relative track record of the technologies deployed at each site. As the saying goes, “show us the data.” Before we close, it is important to underscore an important point. Recent reporting that indicates PV projects are falling short of expected availability is a call to action for all of us. It is a call to action for more data, better analysis, and a deeper understanding of what causes PV systems to underperform. It is, notably, not a call to action for unnuanced conservatism. Simply whacking a few points off availability is, in our view, insufficient to the task of ensuring a better-performing PV fleet and it creates blind spots. We hope our fellow IEs will join us in not simply erring on the side of conservatism but, rather, will continue to advance our knowledge of these issues and build better, and more nuanced models that reward innovation, investment, and effort. We hope you’ve found this series to be helpful, and we welcome the opportunity to partner with any of our readers who would be able to support with future efforts. Although this is the last of our solar availability series for now, we fully intend to revisit the topic in the future. For our storage-oriented audience, you can expect a similar discussion on availability assumptions for BESS technologies in upcoming articles. About Camelot Energy Group is a technical and strategic advisor to owners and investors in clean energy and energy storage projects, programs, and infrastructure. Guided by our core values of courage, empathy, integrity, and service we seek to support the energy needs of a just, sustainable, and equitable future. Our team has experience in supporting 7+GW of solar PV and 10+ GWh of energy storage and offers expertise in technology, codes and standards, engineering, public programs, project finance, installation methods, quality assurance, safety, contract negotiation, and related topics. Our services are tailored to a providing a different kind of consulting experience that emphasizes the humanity of our clients and team members, resulting in a high quality bespoke service, delivered with focus, attention, and purpose. Key services include: -Technical due diligence of projects and technologies -Owner’s representative and engineer support -Strategic planning -Training and coaching -Codes and standards consulting -Contract negotiation and support. < Back Back

Camelot Energy Group is a technical & strategic advisor to owners and investors in clean energy & energy storage projects, programs & infrastructure. We specialise in Solar, Energy Storage, Consulting, Engineering, Batteries, Due Diligence, Energy Access, Strategy, Owner’s Engineering & Advisory. GET IN TOUCH Contact Us Boston, Massachusetts hello@camelotenergygroup.com First Name Last Name Email Phone Leave us a message... Submit Thanks for submitting!

Oct 10, 2024 Part 1: VDER Revenue Stack Many developers and financiers rely on the Value of Distributed Energy Resources (VDER) Calculator, a freely accessible spreadsheet calculator tool ( here ) to calculate expected VDER revenues for potential projects. While this tool is freely available and relatively easy to use, we find that it can be insufficient for accurately modeling some potential revenue streams. Some potential shortcomings of an approach relying solely on the VDER calculator could include: The VDER calculator uses only a linear degradation model and a fixed round-trip efficiency value for the life of the project. In reality, degradation follows a curve and RTE also degrades over time. The VDER calculator uses historical call periods for Locational System Relief Value (LSRV), when in actual operation, an operator would act to maximize LSRV revenues by discharging coincident with Demand Reduction Value (DRV) periods. This can result in the VDER calculator under-representing LSRV revenues. Actual Location Based Marginal Pricing (LBMP) revenues are calculated at the nodal level, while the VDER calculator uses zonal-level data, which is not sufficiently granular to accurately capture true prices. ConEd revenues are calculated by Group (A-D) and these groups are not present in the VDER calculator. So, while the VDER calculator is a helpful tool for preliminary analysis, when making an investment in utility-scale BESS, it is important to supplement this initial analysis with a more detailed revenue forecast that accounts for the many additional variables present in actual operations. Like other leading BESS market analytics experts, Camelot uses an optimized dispatch model to calculate future revenues for BESS projects participating in merchant energy and ancillary services markets. However, projects with significant programmatic revenues, like NY VDER projects, often require a more tailored approach to validate revenue streams and financial model inputs, so Camelot has built out additional tools and capabilities to incorporate these revenue streams seamlessly with applicable merchant market opportunities. We provided some background on the VDER program to help developers and investors better understand this critical framework, which you can view here . Below, we have modeled the revenue stack for a 5 MW, 4-hour Battery Energy Storage System (BESS) under the VDER program for various utilities. We estimated LSRV and Installed Capacity (ICAP) revenues manually, while using an optimized dispatch model to estimate LBMP and DRV values. Figure 1 Excerpt from Camelot Q4 2024 NY Market Outlook Report Reasons for manually modeling LSRV and ICAP Alternative 3 (Alt 3) LSRV: Since the VDER Calculator does not distinguish between ConEd Groups (A-D), it can incorrectly place LSRV revenue periods outside the DRV windows for ConEd C and D Groups. In reality, these LSRV calls would correctly align with the DRV windows in each ConEd Group, therefore we have manually adjusted the LSRV periods in ConEd C and D Groups to correct for this. For example, in ConEd Group C , 2023 historical data would suggest that the LSRV period occurs from 2pm-3pm, whereas the DRV period is from 4pm-8pm. In this case, an optimized dispatch might prioritize the DRV period, resulting in no LSRV revenues. Camelot, therefore, adjusts the LSRV revenues to reflect the more likely operating scenario wherein a BESS would gain both LSRV and DRV revenues. Regions with longer DRV windows, such as RG&E, show the greatest loss in LSRV revenues due to capacity degradation in the BESS, as the systems age and become less able to fully discharge over 5+ hour DRV windows. Regions with shorter typical DRV windows or windows capturing most of their revenue within an hour or two , such as ConEd A, were less affected by BESS capacity degradation. Figure 2 Excerpt from Camelot Q4 2024 NY Market Outlook Report ICAP Alt 3: Under the VDER program, ICAP Alt 3 is the sole option for BESS projects and is considered the most lucrative ICAP variant, though this varies by region. Monthly compensation is awarded based on injections during the annual peak hour multiplied by the ICAP Alt 3 rate ($/kW), which fluctuates monthly. Additionally, all ICAP alternatives already account for an ELCC (Effective Load Carrying Capability) adjustment, eliminating the need for further capacity accreditation adjustments. Moreover, since capacity prices fluctuates on a monthly and annual basis, we modeled ICAP manually using the 2024 VDER Calculator and applied an escalation rate based on our market outlook. Key trends and insights from the above figure results The energy component is the smallest contributor to the value stack, largely due to higher charging costs in ConEd and PSEG areas, which face elevated electricity prices caused by high demand, congestion, and transmission losses. Thought energy is discharged at a higher price, too, the difference (high minus low) in price can often be modest. Capacity prices vary significantly by NYISO load zones, making it challenging to predict capacity revenues due to the volatility of auction prices across zones. Prices could decline with the addition of offshore wind, which contributes to both energy and capacity. Historically, capacity prices have been high across Zone J (ConEd NYC) and Zone K (PSEG LI), with Zone J (ConEd NYC) averaging 2.5 times higher than other zones due to expected thermal retirements and the difficulty of integrating new renewables due to land constraints. Projects located in regions with 2 PM to 7 PM DRV windows show the best results, as these times overlap with potential system peak windows. For example, DRV revenues in ConEd and PSEG regions are much higher than in other areas, with ConEd DRV revenues 7.02 times higher than the state average and PSEG DRV revenues 2.22 times higher than the state average. In the Central Hudson utility territory, LSRV does not apply. The highest LSRV revenues are observed in ConEd and PSEG, particularly in ConEd Zone A, where LSRV revenue is 3.17 times higher than the state average. PSEG’s LSRV revenues are, on average, 1.13 times higher than the state average. Conclusions In summary, the VDER revenue stack diminishes considerably when projects are located outside of ConEd and PSEG territories. Though CAPEX and OPEX costs for upstate projects may be generally lower, this is more than offset by the more lucrative revenue streams noted in this article. In calculating these revenue streams, it is important to consider the many market nuances applicable to the VDER revenue stack. The freely available VDER Value Stack Calculator, while a good initial analysis tool, may not be sufficient in all cases to estimate accurate forward revenues and our team recommends a more detailed analysis be done to support development and financing of energy storage projects in New York State. Stay tuned for Part 2, where we will discuss and compare the VDER value stack for hybrid projects under ICAP Alt 1 and Alt 2, as well as the PV Charging Only and the PV & Grid Charging considerations. If you're interested in assessing energy storage and/or hybrid projects in NYISO’s VDER Program, feel free to reach out to us at info@camelotenergygroup.com . About Camelot Energy Group is a technical and strategic advisor to owners and investors in clean energy and energy storage projects, programs, and infrastructure. Guided by our core values of courage, empathy, integrity, and service we seek to support the energy needs of a just, sustainable, and equitable future. Our team has experience in supporting 7+GW of solar PV and 10+ GWh of energy storage and offers expertise in technology, codes and standards, engineering, public programs, project finance, installation methods, quality assurance, safety, contract negotiation, and related topics. Our services are tailored to a providing a different kind of consulting experience that emphasizes the humanity of our clients and team members, resulting in a high quality bespoke service, delivered with focus, attention, and purpose. Key services include: -Technical due diligence of projects and technologies -Owner’s representative and engineer support -Strategic planning -Training and coaching -Codes and standards consulting -Contract negotiation and support. < Back Back