Solar PV + Storage and the California ISO Energy Market. Peter Ganz (MEM 18) Dr. Lori Bennear, Advisor. First Solar, Client

Transcription

1 Solar PV + Storage and the California ISO Energy Market By Peter Ganz (MEM 18) Dr. Lori Bennear, Advisor First Solar, Client 27 April 2018 Masters project submitted in partial fulfillment of the requirements for the Master of Environmental Management degree in the Nicholas School of the Environment of Duke University.

2 Executive Summary As renewable energy becomes increasingly prevalent in today s power systems, grid operators are faced with new challenges to maintain both the physical reliability and economic viability of the systems they manage. The unique solutions these operators have devised to incorporate variable energy resources such as wind and solar into wholesale power markets have created opportunities and barriers for independent power producers. Independent power producers must be aware of the specific market rules of the territories in which they choose to operate, recognizing what rules can be beneficial to their plants and avoiding penalties that can weaken their plants economics. First Solar is one of those independent power producers seeking to improve the economics of the solar plants it co-owns or operates in the California Independent System Operator (CAISO) territory. Specifically, First Solar has seen solar photovoltaic (PV) power plant economics weakened by energy imbalance settlements in the CAISO energy market. These imbalance settlements occur when forecasts for expected power production from the plants are updated and changed based on increasingly accurate weather forecasts. This can result in additional costs in scenarios where the plant must purchase Real-Time electric power at the market price to make up for lower than expected production or pay penalties for producing more power than is needed. First Solar has recognized the economic impact of the energy imbalance settlements, but also sees a potential solution in battery storage. If a grid-scale battery system can be affordably paired with a solar PV system in the CAISO market, the battery could potentially reduce energy imbalance settlements by discharging power to make up for times when the PV system underproduces electricity and charging during times the PV system overproduces electricity. Using a battery this way is dependent on the market rules, the physical capability of a battery system to store, charge, and discharge energy, and the successful operation of the combined solar PV plus energy storage (PVS) system to respond appropriately to the current state of the grid. This study in coordination with First Solar investigates this possibility. First, a clear understanding of the CAISO energy market and the bidding procedure for variable energy resources (such as a

3 solar PV power plant) is established, explaining energy imbalance settlements and assessing how a battery fits into that system. Next, a Microsoft Excel based data model is constructed in coordination with First Solar s PVS team, combining the physical capabilities of a solar PV system and a battery system with the economics of constructing, installing, and operating a battery system. The economics are assessed as a discounted cash flow model that seeks to optimize the internal rate of return for the plant by adjusting the battery system energy and power capacity sizing. Finally, the results of this model are discussed and assessed, and final recommendations are made. The results of this study showed that a battery system is likely not the ideal way to mitigate energy imbalance settlements. The analysis of the CAISO energy market revealed that a majority of energy imbalance settlements are based solely on instructions from the CAISO to the plant to increase or reduce power based on weather conditions. Only a small component of the total energy imbalance settlement can be mitigated with the increased accuracy of energy delivery to the grid that a battery system can provide. However, a battery system can return positive economics with the introduction of a 5-minute, Real-Time locational marginal price arbitrage operating strategy. By programming the battery system to charge from the PV system when the Real-Time locational marginal price is lowest and then discharge energy when Real-Time locational marginal price is highest, a PVS system can capture additional revenues and more accurately respond to the needs of the grid, rather than simply responding to weather forecast updates from the CAISO. As this study was conducted with a client, I utilized private data that First Solar provided to conduct this analysis. As such, this published academic version is redacted of specific power plant names, specific dates, and specific cost and revenue data.

4 Contents Introduction.. 1 Section I Variable Energy Resources and the CAISO Energy Market Bidding into the CAISO Energy Market with a Variable Energy Resource. 2 First Solar and the CAISO Energy Market Energy Imbalance Settlements Section II PVS Operation & Bidding Strategy Considerations Physical Model Discounted Cash Flow Model Battery Operation Methodology Data Equations Section III PVS Results, Discussion, and Areas for Improvement Model Results Discussion of Results Further Research Recommendations & Conclusion Acknowledgements Bibliography... 26

5 Introduction The rise of renewable energy generation throughout the United States has been especially impactful in California. Plentiful solar and wind resources combined with pro-renewable energy state policy goals has led to rapid scaling of renewable generating resources in the state. This rapid penetration has lead California s independent system operator, California ISO (CAISO), to introduce methods for financially settling discrepancies between Day-Ahead generation bid awards and variable, Real-Time energy production. These energy imbalance settlements occur when a variable energy resource, such as solar or wind, is issued updated electricity dispatch instructions from the CAISO due to more accurate weather forecasts being produced closer in time to the dispatch increment. While energy imbalance settlements have existed in the CAISO for the last 10 years, the rapid drop in the price of battery storage has presented a new opportunity for First Solar to assess mitigation of these settlements while operating PV projects in the CAISO energy market. If a battery system can be coupled with PV system to economically reduce energy imbalance settlements, First Solar could potentially improve the economics of the projects they develop, own, and operate. This paper is structured into three major sections. The first is an explanation of the CAISO energy market, particularly variable energy resource operation and energy imbalance settlements. The second section examines solar PV plus energy storage (PVS) plants, modeling how a battery system can be added to an existing solar PV asset to store and discharge electricity. This section also covers the economics that accompany the construction and operation of the combined system. The third section is a discussion of the model results, areas for further research, and a set of recommendations for First Solar regarding PVS systems. 1

6 Section I Variable Energy Resources and the CAISO Energy Market The California ISO (CAISO) is the entity responsible for operating the wholesale, competitive power market in the state of California, as well as ensuring the reliability and of the transmission grid within the state while coordinating with surrounding balancing authorities. CAISO does this by verifying market participants, centrally taking bids for generation and demand, and optimizing the system and market before issuing operational commands to entities active in its territory (Federal Energy Regulatory Commission 2017). The CAISO operates several markets related to the delivery of electric power to accomplish these goals. 1.) An energy market that matches electricity generation with electricity demand, 2.) an ancillary services market that ensures the grid can operate physically, and 3.) a congestion revenue rights market that allows traders and energy companies to hedge against unexpected spikes in energy prices due to congestion on the power lines (Federal Energy Regulatory Commission 2017). In 2014, CAISO also introduced a new Western Energy Imbalance Market, allowing balancing authorities in the Western United States that do not sit within the CASIO s geographic territory to trade excess energy in Real-Time to balance over-generation from renewable resources. This study does not address the Western Energy Imbalance Market, but rather address energy imbalance settlements which are assessed to energy resources participating in the California energy market. Bidding into the CAISO Energy Market with a Variable Energy Resource Bid Types - When participating in the CAISO energy market, a plant can submit either economic bids or self-schedule bids. An economic bid details exact prices the plant will take for producing exact MWh of energy. Plants must submit bids for increasing amounts of energy, from their minimum operating capacity up to their maximum operating capacity, at increasing prices (in $/MWh) (CAISO 2018). On the other hand, self-schedule bids will simply be bids for a MWh amount of energy, indicating the plant will run regardless of the market price (CAISO 2017). In the case of a solar and wind plants, which are dependent on weather conditions to produce energy, the MWh amount of energy bid through the self-schedule process is based on the weather forecast (more on resources in the next section). Self-schedule bids are also given a higher priority than economic bids; Stated in the CAISO Business Practice Manual on Market 2

7 Practices, A self-schedule is modeled as an energy bid with an extreme price [ ] that effectively provides scheduling priority over economic bids for energy (CAISO 2017). The solar PV plants that First Solar operates will typically submit self-schedule bids, agreeing to take the market price of energy in order to ensure they are called upon to operate in the market. While this means the plant may be taking negative prices of energy at times, it is still beneficial to schedule energy to capture Renewable Energy Credits and be given bid priority to operate. Additionally, sunlight is free, so the plant does not incur fuel costs to run at a low price. Resource Types - Solar PV plants are considered a Variable Energy Resource (VER) by CAISO due to the variable nature of the power they produce. Solar PV power production is primarily dependent on the sun s radiant energy and, other factors constant, the electrical output of a PV panel or cell is strongest when there is direct sunlight hitting the PV panels (Webber 2017). This means that whenever an unexpected obstruction to the direct sunlight occurs, primarily in the form of clouds, the power output of the plant will fluctuate. While forecasting methods have improved, there is still a decent level of uncertainty to solar production forecasts that likely cannot be overcome with better forecasting methods (Aggarwal and Saini 2014). The weather system is simply too complex and chaotic due to its dynamic and non-linear nature (Silver 2015). As such, solar VER plants are assessed the Variable Energy Resource Forecast Charge. This fee is charged to plants that choose to use the CAISO s official solar forecast for bidding. This fee of $0.10/MWh scheduled is essentially in place to compensate CAISO for the effort they put into producing a solar forecast each day (CAISO 2014). However, if a plant operator believes they can create a more accurate forecast than the CAISO, this fee can be waived (CAISO 2017). Locational Marginal Price An important concept of the CAISO energy market is the locational marginal price (LMP). The LMP is the price at which wholesale electric power is bought and sold. The LMP is calculated using three components: System Marginal Energy Costs, the Marginal Congestion Component, and the Marginal Losses Component. LMPs will change over time. First, the CAISO will establish a Day-Ahead LMP (DA LMP). This DA LMP is used in the Day-Ahead Market and is based off forecasted market conditions for the next day. The DA LMP will be the price for an entire hour, so one day will have 24 unique DA LMPs. The Fifteen-Minute Market LMP (FMM LMP) is an updated version of the DA LMP, and is calculated on the trading day, 75 3

8 minutes prior to the trading hour. The FMM LMP is more granular than the DA LMP, with each hour of the day having 4 unique FMM LMPs. The FMM LMP is more granular as the CAISO has better information on the system requirements than it did in when formulating the DA LMP; Forecasts of weather, supply, and demand are typically more accurate the closer they are in time to the production increment. The Real-Time LMP (RT LMP) is the most granular LMP, and is calculated on the trading day, 5 to 10 minutes prior to the trading increment. A DA LMP is for 5- minute increments, and thus each hour of the day has 12 unique RT LMPs. The increased granularity of the RT LMP follows the same logic as the FMM LMP. The closer in time to the actual trading increment, the more knowledge and accuracy the CAISO can include in its calculation of the price (CAISO 2015). Locational Marginal Price Granularity for 1 Hour DA LMP FMM LMP FMM LMP FMM LMP FMM LMP RT LMP RT LMP RT LMP RT LMP RT LMP RT LMP RT LMP RT LMP RT LMP RT LMP RT LMP RT LMP Figure 1 In theory, LMPs should be indications of the state of the grid and the demand for power. When there is large demand for power, LMPs should be higher than when there is low demand for power. In CAISO, LMPs have even been known to go negative during the middle of the day when solar power penetration is highest, incentivizing plants to shut down and curtail power so as not to overload the physical constraints of the grid (Walton 2017). These negative LMPs can result in plants that run during those pricing increments owing money back to the CAISO. Understanding the LMP is essential for a power producer such as First Solar, as the LMP determines the revenue stream it can expect from projects. The amount of power bid and produced by projects will be multiplied by the different LMPs to determine how much the plant is paid. The Bidding and Settlement Process As this research addresses solar PV plants, it is important to understand the entire bidding process a VER plant operator must consider when submitting self-schedule bids into the market. A trading day begins at 12 am and ends at 11:59 pm, but preparation can begin in the week leading up to the trading day. 1. The Day-Ahead Market The first deadline a plant operator or scheduler must meet occurs at 10 am the day before the trading day. By 10 am, a plant must have submitted 4

9 bids for MWh amounts of energy it can produce on average for each hour of the trading day. As the bids are specific to hours of the trading day, they can vary throughout the day based on the forecast the plant is using. These bids may be submitted up to a week in advance. After the bids are submitted, the CAISO will run optimization models for the Integrated Forward Market (IFM) and the Residual Unit Commitment (RUC). The IFM clears the bids submitted by plants to meet the demand forecasted by consumers. Ancillary services requirements are also cleared in the IFM. The RUC ensures that demand will be met and informs the CAISO if there is a need to procure more capacity. Plants will be awarded a certain amount of MWh based on their bids and system demand and will be paid for those MWh at the Day-Ahead Locational Marginal Price. The results of the RUC and IFM are published at 1 pm by CAISO the day before the trading day (Eshleman 2017) (CAISO 2017). Figure 2 details the timeline of the Day-Ahead Market. Figure 2. CAISO Bidding Deadlines Before Trading Day (CAISO 2017) 2. The Fifteen-Minute Market On the trading day, VER plants can receive two potential updates to the bids cleared in the Day-Ahead Market. These updated bids occur due to more accurate weather forecasts than the forecasts used during the Day-Ahead planning process. The first updated bid occurs 75 minutes before the beginning of a trading hour during the Fifteen-Minute Market (FMM). Using the more accurate forecast, the CAISO will adjust the MWh awards for each plant up or down relative to the Day-Ahead reward. (Eshleman 2017) (CAISO 2017). The plant is assessed its first energy imbalance settlement 5

10 based on the adjustment. The payment is derived from multiplying the FMM change in bid by the FMM LMP.!"#$%$&'( *(++%("(&+ #1 = /00 01h "(&+ /00 90: Figure 3. CAISO Fifteen-Minute Market Timeline (CAISO 2017) 3. The Real-Time Market During the actual trading hour, CAISO will issue its final instructions to plants as to how they should dispatch power during the Real-Time Market (RTM). These instructions are for 5-minute increments and are communicated to the plants via the CAISO s Automatic Dispatch System (ADS), a digital user interface all plant operators in the market use. The ADS instructions are given to the plants 2.5 minutes before each 5-minute increment begins. CAISO issues the dispatch commands as positive or negative values, telling a plant how to adjust its dispatch in relation to the bid adjustments from the FMM (Eshleman 2017) (CAISO 2017). The second imbalance settlement is assessed at this point and is derived by multiplying the 5-minute dispatch instructions by the RT LMP.!"#$%$&'( *(++%("(&+ #2 = <=0 01h "(&+ <= 90: The plant is then responsible for hitting the dispatch instruction at exactly 2.5 minutes into the 5-minute increment and holding power production at that level until new instructions are issued. After each 5-minute increment has passed, the final imbalance 6

11 settlement is assessed. This is derived by multiplying the power output deviation at 2.5 minutes into the increment from the RTM instructed dispatch amount by the RT LMP.!"#$%$&'( *(++%("(&+ #3 = (0(+(@(4 :AB(@ C6+D6+ <=0!&7+@6'+(4 :@A46'+FA&) <= 90: Figure 4. CAISO Real-Time Market (CAISO 2017) 4. Energy Imbalance Settlement Summary The first two imbalance settlements are calculated using instructions from the CAISO based on forecasts and are unable to be influenced by plant operators. An improved forecast in the Day-Ahead Market could help reduce these charges, but there is no action a plant operator can take on the trading day to mitigate these charges. However, the third imbalance settlement, the difference between the Real-Time Market instructions and actual production, can be mitigated by more accurate operation of the plant. This is where energy storage has the potential to improve the economics of a PV plant, allowing a plant operator to either push extra energy on to the grid to make up for under-production or send extra generation to the battery system to avoid over-production, smoothing out the variability of solar power production. It is also important to note that energy imbalance settlements are assessed to variable energy resources that choose to sell power in the Day-Ahead Market (as described in this section). However, plant operators do have the option to only bid in the Real-Time Market, but at the risk of losing scheduling priority. First Solar and the CAISO Energy Market As a solar PV plant owner and operator, First Solar has key business interest in understanding the CAISO energy market, particularly from the standpoint of a plant scheduler and operator. To 7

12 contextualize the CAISO Energy Market and examine the potential benefits a battery system can provide, this analysis uses a specific plant operating in the CAISO territory that First Solar has stake in. The 20 MW project is operated and monitored by First Solar from the First Solar Operations Center in Tempe, AZ. Energy Imbalance Settlements As discussed, energy imbalance settlements are applied as forecasts become increasingly more accurate the closer in time to the trading increment and as the CAISO updates bids and dispatch instructions. Imbalance between Day-Ahead awards and Real-Time production do not always result in charges, however. A combination of either over or under production and positive or negative LMPs will result in four scenarios (Figure 5). Imbalance Scenarios Instructions or Production Market LMP Result Over-Produce Positive Plant is Paid Under-Produce Negative Plant is Paid Over-Produce Negative Plant is Charged Under-Produce Positive Plant is Charged Figure 5 A closer look at the data from the 20 MW plant reveals that a majority of these payouts occur when the RT LMP spikes or dips significantly. Below are two detailed examples showing how these charges were applied to the 20 MW plant, the first due to an under-delivery of power and the second due to an over-delivery of power. These examples are extreme cases with atypical LMPs, but work to show how imbalance settlements are assessed each hour. Under-Delivery Example Figure 6 shows an hour of trading in April During this hour, the plant paid $7, to the CAISO for under-delivering scheduled power. After being awarded 9.36 MW at the DA LMP of $29.94, the plant was paid $ On the trading day, the plant was then awarded an additional $ in the Fifteen-Minute Market as its bid was increased from the amount originally awarded (Imbalance Settlement #1). However, the financial damage occurred in the Real-Time Market. The plant was instructed to reduce its production by 6.86 MW while LMPs were over $1,000 (Imbalance Settlement #2). Over the course of the hour, the instructed reductions resulted in $6, worth of imbalance settlements the plant was 8

13 responsible for paying back to the CAISO. Additionally, the plant was unable to produce the final instructed amount, resulting in an additional $ worth of imbalance settlements (Imbalance Settlement #3). This single hour of trading and operation is detrimental to the plant s economics. The Day-Ahead award of $ minus its cumulative imbalance settlements resulted in a net loss of $7, for operating in the market for one hour. The imbalance charges incurred in this hour made up 25% of the imbalance settlements the plant incurred during April Having energy storage on-hand can hopefully allow the plant operator to reduce these imbalance settlements. A charged battery system could potentially allow the plant to bring deviations between instructions and production to $0.00 while presenting an opportunity to over-deliver and capitalize on the $1,000+ RT LMPs. Over-Delivery Example Figure 7 details a trading hour in which the plant produced more power than the amount awarded in the Day-Ahead Market. This over-production occurred when the RT LMPs were negative, resulting in an over-production imbalance settlement. The plant was awarded MW for the hour in the Day-Ahead Market. The CAISO was forecasting a negative DA LMP of -$1.52/MWh at the time, resulting in the plant paying the CAISO $ On the trading day, the plant was instructed to reduce production in the Fifteen-Minute Market, while FMM LMPs were significantly more negative. This resulted in the CAISO paying the plant back $ (Imbalance Settlement #1). However, in the Real-Time Market, the plant was instructed to increase production from previous instructions while the RT LMPs were between negative $100 to negative $300, resulting in $1, in imbalance settlements paid from the plant to the CAISO (Imbalance Settlement #2). This instruction to increase production while LMPs were negative seems counter-intuitive. After speaking with a member of the plant s asset management team however, it seems this is likely due to the weather forecast calling for more sunlight than predicted. Finally, the plant was unable to exactly meet instructions in actual production, producing 3.99 more MWs than instructed over the hour. This overproduction combined with negative RT LMPs led to another $ in imbalance settlements paid from the plant to the CAISO (Imbalance Settlement #3). 9

14 Overall, the plant lost $1, for operating during this hour in February 2017 due to bid adjustments and over-production. The $1, imbalance settlement in the single hour made up 9% of February 2017 imbalance settlements for the plant. Again, energy storage presents an opportunity for the plant operators to reduce the overall imbalance settlement. If there had been an empty battery on site, the operator could have sent excess PV power to the battery rather than over-producing the 3.99 MW over the trading hour. Depending on the size of the battery, the operator could have even attempted to under-produce instructions and take advantage of the negative RT LMPs, making up for profits lost in the instructed Fifteen-Minute and Real-Time Markets. The imbalance settlements variable energy resources are responsible for paying present unique opportunities to improve the economics of plants. To understand the feasibility of capitalizing on these opportunities by combining energy storage with solar PV plants, it is necessary to understand the ways a PVS plant is modeled electrically and economically. 10

15 Figure 6 Under-Production Example 11

16 Figure 7 Over-Production Example 12

17 Section II PVS Operation & Bidding Strategy Considerations In order to economically optimize the solar PV plus battery storage (PVS) system, two models with unique constraints and considerations must be built and combined. The first is a physical model of the electrical system, specifying what the PVS system is capable of regarding the physics of energy generation and the market rules governing what electricity can be physically generated by an asset. The second model is a financial model that takes into account costs of different components of the storage system, such as fixed installation costs and variable sizing costs. Together, these two models can be used to optimize the sizing of the battery system to achieve maximum economic performance of the PVS system. Physical Model Electrically modeling the addition of a battery storage system to a PV system involves considerations of the power capacity, the energy capacity, and the roundtrip efficiency of the battery. The power capacity influences how quickly a battery can be charged or discharged, and is measured in units of power, typically megawatts (MW). The energy capacity of the battery governs the amount of energy a battery can discharge or charge, and is measured in units of energy, typically megawatt hours (MWh). The roundtrip efficiency of a battery measures the percentage of energy that can be used from a battery system after the energy is generated, stored, and retrieved from the battery (Homer Energy 2017). As this analysis assesses adding a battery system to an existing PV asset, a key market parameter must be considered. The Large Generator Interconnect Agreement (LGIA) is agreed upon between the plant developer and the system operator and specifies the maximum amount of power a generating asset can produce at any instant. In this analysis, the LGIA is set at 20 MW. Therefore, the 20 MW PV array and battery system can never combine to exceed 20 MWs of instantaneous power at any time, constraining the upper limit of allowed generation. The LGIA is in place to allow the system operator (in this case the CAISO) to model the generating asset and consider its impact on grid reliability. This analysis also operates under the assumption that the battery system can only be charged from the associated PV system, rather than assuming the battery can also charge from the grid. 13

18 This assumption necessitates the constraints that the battery system cannot charge unless there is PV production, and that the system can never charge at a rate higher than the current PV production. The second constraint will primarily come into play in the early and late hours of the day, as the sun is rising and setting. Discounted Cash Flow Model Financially modeling a battery system involves assessing expected costs such as installing the battery system, sizing the system, and operating and maintaining the system over its lifetime. It also involves assessing the expected new revenues the battery system enables by combining arbitrage opportunities and imbalance settlement mitigation, as well as the tax revenues that come from the Investment Tax Credit and depreciation write offs. Together, these costs and revenue assumptions are combined into a discounted cash flow model measuring the net present value and maximizing the internal rate of return of the project over a 20-year expected lifetime. To populate the costs of the financial model, First Solar costs and a 2020 installation year for the system are used. The capital expenditures (CAPEX) of the project are divided into two categories, fixed costs and variable costs. The fixed costs include all costs of a project that will not scale with the sizing of the battery system, such as the battery control system, construction and O&M mobilization, permitting, transaction costs associated with procurement of land, etc. These fixed costs are estimated at $350,000 per project. This number takes into consideration the cost synergies that are captured by building the battery system with a PV asset. If this battery system was built as a stand-alone project, these costs would increase. The variable costs of CAPEX involve the sizing of the battery components and are driven by the specific power capacity and energy capacity of the battery. The power costs are assessed at $75/kW of power capacity. The energy sizing costs are assessed at $250/kWh of installed energy capacity. The operation and maintenance costs (OPEX) are assessed at $8/kWh of installed energy capacity per year and include all preventative and corrective maintenance, battery warranty, and insurance. To populate expected positive cash flows, monetization of the Investment Tax Credit (ITC) was applied to the year 1 CAPEX investment, assuming that this project can bring on a tax equity investor and qualify for 95% of the ITC, as all of its charging will be done with the associated PV 14

19 system. As this project is modeled for construction in 2020, the ITC rate is set at 26% of CAPEX, in accordance with the step-down process of the tax credit (Solar Energy Industries Association 2018). Another financial benefit comes from depreciating the asset in year 1. This depreciation revenue is a stark change from the typical Modified Accelerated Cost Recovery System that has been in place for energy assets. This change is due to the new tax law passed in December 2017 (Roselund 2017). The project s revenue stream comes from the bidding strategy and operation of the battery system to push energy onto the grid during times of high RT LMP pricing and sending PV generation to the battery system during times of low and negative RT LMP pricing. This methodology is explained in the following section. This revenue stream is expected for each of the 20 years of PVS system s lifetime. Other components of the financial model include the tax rate and the discount rate. Under the new tax bill passed in December 2017, the corporate tax rate is 21%, used in the model to determine the tax benefit or payment the project owes after depreciating the asset (Roselund 2017). Three different discount rates were modeled to represent the typical range used in the renewable energy industry: 6%, 8%, and 10%. Battery Operation Methodology Developing a strategy to mitigate imbalance settlement payouts to the CAISO was based off of existing LMP arbitrage methods, charging the battery when LMPs are low and thus least profitable, and discharging and selling that power into the market when the LMPs are high, maximizing the value of storing and discharging that power. This arbitrage was done daily, on a 5-minute increment basis throughout the day. The battery is instructed to split its charging and discharging based on the energy sizing of the battery. For example, a 5 MWh battery can charge 1 MW of power during 60 of the 5-minute increments of the day and discharge 1 MW of power during 60 of the 5-minute increments of the day. As a trading day has minute increments, the battery will charge during the 60 increments when the RT LMP is lowest and PV power is available, discharge during the 60 increments when the RT LMP is highest, and idle during the remaining 168 increments of the day. In theory, this method should reduce the third imbalance settlement assessed to VERs by both a.) responding to the pricing signals being sent by the CAISO and b.) more accurately hitting instructions to increase or decrease production. As mentioned in 15

20 Section I, this methodology only has the ability to impact the third imbalance charge, assessed by multiplying the difference between the CAISO instructions and actual production in the Real- Time Market by the RT LMP. Constraints Certain constraints are built into the model based off both physical and marketbased limitations. 1.) For this analysis, the energy capacity of the battery system must be 2 times the size of the power capacity. 2.) The maximum power capacity of the battery system is 20 MW, equivalent to the nameplate capacity of the PV array it is paired with. 3.) The maximum energy storage capacity of the battery system is 80 MWh, allowing the battery to potentially sustain 4 hours of operating at maximum power output when starting from a fully charged state. 4.) The maximum amount of 5-minute increments a battery can charge or discharge 1 MW in a day is 144 each. This is based off the total number of 5-minute increments in a trading day, 288, meaning that once the battery sizing reaches 12 MWh of energy capacity, the system will potentially spend half of the day charging, and half of the day discharging. The battery energy capacity is allowed to go above 12 MWh, with unused energy carrying over into the next trading day. 5.) The battery can only charge when the PV system is producing power. 6.) The battery can only charge up to the amount of PV power being produced. 7.) The total output of the PVS system cannot exceed the LGIA of 20 MW. Assumptions Underlying this methodology are some key assumptions that must be understood to assess the practicality and applicability of the analysis. The first assumes perfect foresight regarding RT LMP pricing at the beginning of every day, rather than 5-10 minutes before the trading increment. This assumption allows for the RT LMPs to be ranked from highest to lowest per day, enabling the price arbitrage. The second assumption relates to the classification of the PVS system by CAISO. As discussed in section I, solar PV plants are variable energy resources (VERs), and thus are subject to a specific set of market rules regarding how they are treated in the market. This analysis assumes that adding a battery system would not affect the VER status of the plant, meaning it would still be subject to energy imbalance settlements, self-scheduled bidding, and CAISO forecasting of production. 16

21 Data The data used in this analysis was provided by First Solar. It includes 1-minute electricity production data from a 20 MW solar PV plant, 5-minute energy imbalance settlement data from the CAISO, and hourly Day-Ahead reward data from the CAISO. The 1-minute production data was aggregated into 5-minute increments and combined with the settlement data. One year of uninterrupted data was used to model expected revenues of adding different sized batteries. The year used spans from August 2016 through July This complete year of data is assumed to be a typical year of LMPs from the CAISO and production from the plant. Equations 1.) Battery State of Charge Function:! "# %h'()"*) +h,* =. / ;<=> "#?"@%h'()"*) +h,* =. / > Where:. / is the state of charge at time t in MWh 3 4 is decision variable indicating action of battery 3 1 = +1, indicating charge; 3 B = 1, indicating discharge 3. / is battery power in MW at time t; divided by 12 to convert from MW to MWh ;<= is round trip efficiency of battery as a percentage; only applies when charging Subject To: 0. / F'++,(G =*,()G.'H'%"+G 0 3. / F'++,(G IJK,(.'H'%"+G 2.) PV+S System Output to Grid Function: ILM / = IL /.N / + O / Where: 17

22 ILM / is the amount of power in MW being sent to the grid at time t IL / is the amount of power in MW being produced by the PV array at time t.n / is the amount of power in MW being used to charge the battery at time t O / is the amount of power in MW being discharged to the grid from the battery at time t Subject To: 0 ILM / PQR3 0.N / F'++,(G IJK,(.'H'%"+G 0 O / F'++,(G IJK,(.'H'%"+G 3.) New Revenue Calculation - The calculation of the expected new revenues that can be achieved by a battery system is based off replacing the third imbalance charge with the numbers produced by the PVS physical model. This imbalance charge is assessed by taking the difference between actual production in each 5-minute increment and the Real-Time Market dispatch instructions issued by the CAISO and multiplying by the RT LMP. As running the model with the arbitrage methodology produces a different final production value that what actually occurred, the third imbalance charge is replaced as follows: ILM RST'U'*%,.h'(), #3 = (ILM MG@+,S YJ?,U,? I(J?Z%+"J* ;<Y O"@H'+%h R*@+(Z%+"J* ) ;< PYI Using this new PVS Imbalance Charge, the revenue stream modeled in the financial model is: M+J('), ;,\,*Z, = ILM RST'U'*%,.h'(), #3 IL RST'U'*%, M,++U,S,*+ #3 This PVS Revenue is the result of the reduced Imbalance Settlement #3, and the arbitrage being captured by the batteries response to LMP pricing signals from the CAISO. 18

23 Section III PVS Results, Discussion, and Areas for Improvement Model Results The final model was run to maximize the Internal Rate of Return by changing the sizing of the energy storage and power capacity of the battery system. Using the Evolutionary optimization method in Microsoft Excel s Solver Add-On, the following results were produced using the three modeled discount rates. IRR Optimization Results Discount Rate (%) Energy Size (MWh) Power Size (MW) NPV ($) IRR (%) 6% $13.1 Million 34.6% 8% $10.5 Million 34.6% 10% $8.4 Million 34.6% Figure 8 While the optimization allowed battery energy capacity sizing to vary from 0 to 80 MWh and power capacity sizing from 0 to 20 MW, the optimal solution produced is 21.9 MWh energy capacity with 10.5 MW of power capacity. This system could thus discharge 10.5 MW of energy for 2 straight hours, although due to the 5-minute increment discharge strategy, it is unlikely to do so. The results show that with this battery sizing, the PVS system could generate $2.14 million of revenues per year from the RT LMP price arbitrage and imbalance charge mitigation methodology. The system would require $6.6 million worth of capital investment in year 0 and $174,871 each year of its lifetime for operations and maintenance. It could capture $1.6 million of tax revenues through monetization of the ITC and $5.8 million of depreciation in year 1. The physical model works to increase the total output of the PVS system via discharging the battery when RT LMPs are high and using PV production to charge the battery when RT LMPs are low. Figure 9 shows how the system operated on a standard November day in The output of the PVS system is lower than the potential available PV capacity in the early hours, as that energy is being used to charge the battery. However, in the later hours, the PVS system is operating at a higher capacity than the available PV capacity, as the battery is recognizing high RT LMPs and pushing stored energy to the grid. As the sunlight begins to taper off from 3 pm to 5 pm, the PVS system is able to provide a consistent 10 more MW of power than a stand-alone 19

24 PV system would be able to, capturing high RT LMPs and thus additional revenues. During this one day of simulated operation, the PVS system was able to change Energy Imbalance Settlement #3 from $ of positive settlement into $7, of positive settlements. 25 Simulated Battery Action - November Power (MW) :05:00 AM 6:20:00 AM 6:35:00 AM 6:50:00 AM 7:05:00 AM 7:20:00 AM 7:35:00 AM 7:50:00 AM 8:05:00 AM 8:20:00 AM 8:35:00 AM 8:50:00 AM 9:05:00 AM 9:20:00 AM 9:35:00 AM 9:50:00 AM 10:05:00 AM 10:20:00 AM 10:35:00 AM 10:50:00 AM 11:05:00 AM 11:20:00 AM 11:35:00 AM 11:50:00 AM 12:05:00 PM 12:20:00 PM 12:35:00 PM 12:50:00 PM 1:05:00 PM 1:20:00 PM 1:35:00 PM 1:50:00 PM 2:05:00 PM 2:20:00 PM 2:35:00 PM 2:50:00 PM 3:05:00 PM 3:20:00 PM 3:35:00 PM 3:50:00 PM 4:05:00 PM 4:20:00 PM 4:35:00 PM 4:50:00 PM Available PV Capacity (Excluding Night Losses) Battery Action PV+S System Output to Grid Figure 9 Discussion of Results The results of this study are promising for the future of PVS systems in the CAISO energy market. The Internal Rate of Return for this project is very high, indicating that the initial investment in the project would be returned at 34.6% profitability. These results are sensitive to two major tax financing structures. The model assumed a 2020 Commercial Operation Date (COD), qualifying the project for the ITC at a rate of 26%. Figure 10 shows how different CODs affect NPV and IRR. As the ITC steps down, the economics are diminished. However, the project maintains a high IRR, even at only a 10% tax credit. 20

25 Figure 10 The model also assumes that the entire cost of the project will be depreciated in year 1, as the US tax bill passed in December 2017 allows. If the project were to adhere to the Modified Accelerated Cost Recovery System (MACRS) that has been the standard in energy project depreciation, the economics would be reduced with the IRR dropping from 34.6% to 32.5% (Figure 11). ITC Sensitivity - 8% Discount Rate COD ITC Rate NPV IRR % $10.5 Million 34.6% % $10.3 Million 33.4% % $9.7 Million 30.3% Depreciation Schedule Sensitivity Method Depreciation Period NPV IRR 100% in Year 1 1 year $10.5 Million 34.6% MACRS 6 years $10.3 Million 32.5% Figure 11 The results of this analysis are also highly dependent on the two major assumptions that were specified. As the RT LMP arbitrage dispatch strategy relies on the assumption of perfect foresight into RT LMPs at the beginning of each day, there is a high level of uncertainty associated with the revenues this system is capable of capturing. At the optimal sizing of energy and power capacity, the battery system is able to add $2.14 million of revenue per year through imbalance charge mitigation and RT LMP price arbitrage. A majority of this revenue is driven by RT LMP price arbitrage. Before adding a battery system, the 20 MW plant incurred $30,750 worth of Imbalance Settlement #3 charges in the test year. The battery system could bring these charges to $0.00 via more accurate dispatch of the PVS system in the market. However, the remaining $2.1 million of revenue per year is achieved from RT LMP price arbitrage, allowing the battery system to achieve the optimal 34.6% IRR. Looking at a less ideal scenario, where IRR is equal to the discount rate (making the NPV equal to $0.00), allows this analysis to be used to establish a performance threshold for RT LMP price arbitrage. As long as the RT LMP arbitrage can achieve yearly revenues of $656,249 just 31.1% of the revenues achieved under the perfect foresight assumption, the 21

26 project is still economic at an 8% discount rate. When considering whether or not this battery system is worth the investment, First Solar must decide if they believe they can reliably predict RT LMPs with enough accuracy to achieve at least 31.1% of the optimal arbitrage revenues. The second major assumption, that a PVS system will be subject to the same market rules as a PV system in the CAISO market, proved to be a risky assumption. Over halfway through the timeline of this study, I was able to contact an employee of the CAISO who referred me to a technical bulletin on the Implementation of Hybrid Energy Storage Generating Facilities (CAISO 2016). Hybrid energy storage generating facilities are any generators that combine a fuel source with a battery. Therefore, a PV system combined with a battery system would fall in this category. Being classified as a hybrid energy storage generating facility alters the process for operating in the CAISO market, particularly for Variable Energy Resources such as a solar array. Resource ID Considerations The resource ID is the way the CAISO distinguishes different generating resources in the market, tracking capacity, interconnection agreements, and expected electricity generation when modeling the system. Incorporating a battery system with a PV system presents a decision to either classify the entire PVS system under one resource ID or under separate resource IDs. Under a single resource ID, the plant could still retain its VER status, but would lose Participating Intermittent Resources (PIR) status. This is key, as PIR status is what qualifies a plant for the CAISO forecasting and dispatch updates. Under a single resource ID, CAISO cannot accurately predict and model the output of a hybrid plant as it cannot model when the battery is expected to charge and discharge. Therefore, losing PIR status would mean that the CAISO will not issue Fifteen-Minute Market and Real-Time Market updates to a plant for dispatch. Instead, the hybrid plant s scheduling coordinator would either economically bid or self-schedule bids in the Day-Ahead Market, and then update those bids in the Fifteen-Minute and Real- Time Markets themselves based on weather and the state of charge of the battery. The settlement process would operate similarly to when the resource has PIR status, with three energy imbalance settlements occurring due to changes in bids compared to the Day-Ahead Market. However, this method would require a much more involved scheduling coordinator than a resource with PIR status (CAISO 2016). 22

27 If the hybrid generating facility is instead set up under two separate resource IDs, one for the PV system and one for the battery system, the PV system can retain its PIR status. However, two resource IDs would entail two separate settlements with the CAISO, meaning the methodology developed through this study would be invalid. Further Research This study leaves plenty of room for improvement and further investigation of PVS systems in CAISO s energy market. To better address the business challenges energy imbalance settlements present, First Solar must find methods to reduce the uncertainty accompanying the assumptions underlying the analysis. To improve upon the assumption of perfect RT LMP foresight, the study could be expanded to incorporate simulated RT LMPs rather than actual LMPs from a test year. The model could be run with the simulated RT LMPs, and then compared to the actual LMPs to assess the systems performance without foresight. Running multiple simulations against test years could further allow First Solar to establish a standard measure of accuracy, and thus account for expected uncertainties in the economic analysis. To address the second major assumption, various Resource ID scenarios could be tested. Two alternate scenarios stand out: 1.) Modeling expected revenues of a PVS system with a single Resource ID and economic bidding, and 2.) Modeling expected revenues of a PVS system with separate Resource IDs for the PV and the battery, with self-schedule bidding for the PV and economic bidding for the battery. Performing these two alternate analyses would allow First Solar to prepare for different CAISO market scenarios, as well as identifying the most economic option with regards to Resource IDs. Beyond the assumptions of the study, removing the charging constraint applied in this study would allow for a broader search for the optimal economic use of a battery system with a PV system. This study does not investigate how a battery system charging from only the PV system would compare to grid-only charging or both PV and grid charging. Perhaps one of those alternate scenarios could lead to increased economics. Conversely, adding various constraints to the physical model has potential to improve the accuracy of the financial model. This study utilized a high-level approach to the performance of 23

28 the battery, disregarding factors such as degradation, potential replacements costs, and DC-AC ratios (amongst others). Constraining the physical model to a more realistic battery lifetime would likely add additional costs to the financial model, decreasing the IRR of the project. As the 5-minute increment RT LMP arbitrage would require constant cycling of the battery, it is likely a battery system operating under this methodology would wear out faster than First Solar currently models battery systems operating under more consistent and even charge and discharge cycles. More detailed economic constraints could also be added to discounted cash flow model. This study utilized four high-level cost prices, two that scaled with the energy capacity, one that scaled with the power capacity, and one that remained constant. These costs could be broken out into more granular and specific costs, improving the robustness of the considered costs and allowing for better identification of the key costs to this type of battery project. Recommendations & Conclusion While the ideal scenario approach of this study shows that there is potential to use a battery with a PV system to increase economics, the amount of uncertainty must be accounted for before First Solar can make any business decision based on these results. This study showed that mitigation of Energy Imbalance Settlements with a battery is limited to only the third component of the total Energy Imbalance Settlement incurred by a PV plant. However, using a battery system with a PV system to perform RT LMP arbitrage and maximize Imbalance Settlement #3 is promising. I recommend that First Solar investigate simulation of RT LMPs and continue to monitor the CAISO market for market rules regarding hybrid energy systems. If a battery system can be combined with a new or existing PV plant under a single resource ID and maintain Participating Intermittent Resource status, this analysis will become more relevant and actionable. I recommend advocating for this scenario with the CAISO. Finally, I recommend that First Solar investigate the battery degradation that would result from the rapid charging and discharging of a battery system on 5-minute increment timescale. To my knowledge, no utilityscale battery systems are operated in such a way, and thus the true degradation is unknown. 24