SOLAR RENEWABLE ENERGY CREDITS (SRECs) PRICE FORECAST

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1 SOLAR RENEWABLE ENERGY CREDITS (SRECs) PRICE FORECAST - A LINEAR OPTIMIZATION APPROACH Dawei Zhou Daniel R. Bradley Navigant, Inc Old Country Road, Suite 402 Westbury, NY dzhou@navigant.com dbradley@navigant.com ABSTRACT In the current State-specific renewable energy market, four basic drivers determine solar renewable energy credits (SRECs) prices SRECs supply, SRECs demand, cost of energy and state compliance policies. Most of this information can be found on ISO and state commission websites, which provides an adequate view of the current state of the SRECs market. However, to fully understand the SRECs markets and forecast the SRECs price, a better understanding of the relationships among supply, demand, cost and policies is required. This paper studies the relationships among all the drivers to the SRECs price and predicts the SRECs prices using linear optimization method to minimize the energy cost considering the balance between supply and demand as well as the constraints from compliance policies. This method can be applied for multi-state SREC markets, such as PJM SREC market. We provided preliminary results of the SRECs price forecast in PJM to demonstrate the ability of this method. Application of this method may provide pricing guideline for solar market investors and participants. Keywords SRECs price, price forecast, linear optimization, minimum cost flow, optimal power flow 1. INTRODUCTION A growing number of states have recently enacted Renewable Portfolio Standards ( RPS ) laws which require electric utilities to procure a specified percentage of the energy they supply to their customers from renewable sources. Some of these states further require a portion of the requirement to be met by energy from solar energy systems. RPS compliance is measured with Renewable Energy Credits (RECs) or with solar-specific SRECs. SRECs represent the generation attributes of one megawatt of qualified solar energy that can be traded separately from the electricity produced by the system. A challenge in understanding the state RPS markets is that no two are alike. This fact greatly complicates the interaction of SRECs across a region of states RPS markets making it difficult to understand where SREC prices are headed. In RPS compliance markets, SREC prices are capped at a specified price above which electric utilities can pay a penalty or an alternative compliance payment. Beneath this cap, the market prices of SRECs can be highly volatile. When it comes to understanding SREC markets, most market participants don t know where to turn. There are many sources of excellent information including: PJM Interconnection, LLC s ( PJM ) GATS bulletin board, trading platforms such as SREC Trade and Flett Exchange, commission and utility websites, and SREC brokers that provide market prices and insights of the current state of the SREC market. Beyond the current market, it is not always clear what drives prices of SRECs and where SREC prices are headed in the future. To answer these questions, we studied the RPS markets for solar in the eastern states in PJM, including Delaware, Pennsylvania, New Jersey, Maryland, Ohio and the District

2 of Columbia. Our findings are that, when studied as a system, there are four primary drivers to the current and future market price of SRECs: demand, supply, cost of solar energy, and state RPS policies. In this paper we present a case study example that explores these SRECs price drivers and how they are used to forecast SRECs prices using a linear optimization methodology that minimizes the cost of meeting the compliance state RPS compliance targets while balancing solar supply within the context of a patchwork of regulatory markets, each with its own set of rules. 2. LINEAR OPTIMIZATION MODEL The SRECs pricing problem can be represented by a flow diagram of a network. Figures 1 is an example of the Multi- State network with SRECs supply and demand as well as policy constraints. Supply constraint: ( ) Renewable Capacity in State n Policy constraint: (, ) (, ) This network can be solved with linear optimization technique as minimum cost flow problem. A variety of solution methods for the minimum cost flow problem are described in reference [3], [4], [5] [6] and [7]. These may be categorized into a number of principal approaches, namely primal, dual, primal-dual and scaling algorithms. For example, negative cycle and primal simplex are both primal algorithms, while dual methods include successive shortest paths and a specialization of the dual simplex method for linear optimization problems. In this paper, we used the Microsoft Excel 2007 solver to handle the linear optimization problem. Microsoft Excel 2007 Solver uses simplex method, with bounds on the variables and the branch and bound method, for optimizing linear problems. This algorithm was developed and implemented by John Watson and Dan Fylstra, of Frontline Systems, Inc [8]. 3. EXAMPLE CASE STUDY: EASTERN PJM 3.1 Overview Fig. 1: Example of Multi-State network Mathematically, this network is represented as (, ), with supply and demand [1] [2]. The transaction path between State m and n is (, ), which has policy constraint (, ). The cost of renewables in State n is ( ). The definition of the problem is to minimize the total cost of the system: With the constraints: ( ) ( ), Demand constraint: ( )= ( ) In eastern PJM, the following states have RPS laws with solar carve out provisions: Delaware, Pennsylvania, New Jersey, Maryland, Ohio and the District of Columbia. Across these states, the underlying RPS market structure and rules differ which complicates renewable energy development and procurement. Moreover, as developers and utilities in PJM increasingly rely on SRECs to meet RPS requirements, SREC valuation and SREC price forecast are becoming a more important factor in solar energy financing. New Jersey s RPS only qualifies solar energy systems that are directly connected to its distribution grid. This restriction prevents solar energy systems in other states from selling SRECs into the New Jersey market. Because of this restriction, we did not include New Jersey in the case study. Across these states, we examine SREC price drivers and how they are used to forecast SRECs prices using a linear optimization methodology that minimizes the cost of meeting the compliance state RPS compliance targets while balancing solar supply within the context of a patchwork of regulatory markets, each with its own set of rules. 3.2 SREC Price Drivers

3 3.2.1 Demand Demand for SRECs is mandated by states RPS law with annual targets. Typically, an electric utility s obligation under an RPS is the product of the requirement, expressed as a percentage, and its retail sales. The SREC requirements by state are presented in Table 1 on a calendar year basis. TABLE 1: ANNUAL SOLAR REQUIREMENTS BY STATE (SRECS) Year PA OH MD DC DE , ,000 70,000 9,100 51, , , ,000 13,000 79, , , ,000 17, , , , ,000 23, , , , ,000 29, , , , ,000 35, , , , ,000 43, , , , ,000 51, , , ,000 1,190,000 59, , , ,000 1,490,000 65, ,969 Source: Flett Exchange ( Cost of Solar Energy In theory, the long-run price of an SREC should reflect the incremental revenue required by renewable resources to provide targeted returns over the life of the project. Installed cost and estimated electric production are the two primary components used in determining cost of solar energy, and both have an impact on the required price of SRECs. The lower the installed cost of a solar energy system, or the more electricity the solar energy system produces, the lower the SREC price the project requires to meet its returns. Installed cost and production of electricity are influenced by the amount of solar irradiance available and by local loans, grants and incentives available for solar energy system development as well as the solar panel technology. We compare the cost of solar energy with local retail electricity rates to measure the shortfall which revenue from the sale of SRECs will help to close. An example of a comparison of cost of solar energy systems is shown in Figure Supply The existing supply of SRECs is composed of solar energy systems that are qualified to produce SRECs and, to a lesser extent, SRECs that have not yet been used by an electric utility for state RPS compliance and can be used in future years. Data on existing SRECs is accessible in centralized tracking databases, such as PJM-EIS Generation Attribute Tracking System ( GATS ). GATS tracks the electric output of each qualified solar energy system for the issuance of SRECs. GATS then tracks ownership of each SREC over its life until it is used by an electric utility for state RPS compliance. Little information exists on the future supply of SRECs other than larger solar energy systems that connect directly to the electric grid and have to file interconnection applications with the PJM. To project future growth of solar energy systems, we turned to the Navigant Photovoltaic Market Penetration model, which forecasts commercial, industrial and residential solar energy systems installations. This peer reviewed model uses a technology substitution approach to estimate the rate at which the marketplace will adopt a new technology. Fig. 2: PJM States cost of solar energy forecast State RPS Policies Each state RPS defines its own market, including what SRECs qualify for compliance. A prime example of this is the comparison between Pennsylvania and its two neighbouring states, Ohio and New Jersey. In Pennsylvania, SRECs from any state in PJM qualify and can be sold into its solar market; in Ohio, 50% of required SRECs must come from within the state, and the remaining 50% from adjacent states; and New Jersey, which limits SRECs only to those generated on its distribution system, which effectively limits SRECs to those generated in the state.

4 The diagram below (Figure 3) illustrates the complex interactions between state SREC markets in PJM (2011): supply, demand and cost of solar energy. An example projection of SRECs price for each State is displayed in figure 5. A notable result is that Ohio s SRECs price is relatively higher than other State due to the 50% in-state supply requirement. Fig. 3: 2011 PJM policy constraints in bubble diagram representation 3.3 Problem Solving and Demonstrative Results To understand what drives prices of SRECs and where SREC prices are headed in the future, we developed a linear optimization method which minimizes the total system cost through allocating the cheapest solar supplies possible in the system to meet the compliance requirement of each individual State without violating the policy constraints. Taking Pennsylvania as an example (figure 4), this optimization process searches the cheapest solar resources available in the PJM market to form the optimal supply mix to meet Pennsylvania s compliance requirement for each year. Fig. 4: Pennsylvania supply and demand optimization example. Once the supply is chosen through the optimization process, the margin SREC price is determined by the dynamics of Fig. 5 Preliminary results of the PJM SRECs price The results in figure 5 are only for illustration purpose. The price forecast can be refined with more detailed assumptions and in-depth understandings of the SREC markets, which will be discussed in section Regulatory Issues and Market Design The above example is a simplified case only to demonstrate the application of the linear optimization approach to reflect the complex relationship of supply, demand and policy constraints among individual States. Regulatory issues and market design features also impact SREC prices, including: Solar Alternative Compliance Payment ( SACP ).In some states, RPS laws provide for SACP, which offers utilities an option for compliance with RPS. In instances of SREC scarcity or unavailability, or in cases where the price is unexpectedly high, utilities can pay the SACP in lieu of purchasing SRECs. In these instances, SACP acts as a cap on the price of SRECs. Any changes to a state s SACP have an immediate impact on the expectation of SREC prices. Banking of SRECs. In most states SRECs do not have to be used to meet the compliance in the year that they are created. SRECS can be banked and used in the future years (usually 2 to 3 years). Banking SRECs allows for flexibility of when SRECs can be used for compliance; however, it complicates the supply and demand picture for market participants. Any change to a state s banking rules would have an immediate impact on SREC prices.

5 The Federal Investment Tax Credit ( ITC ). ITC is the principal federal incentive for developing and installing solar power under Section 48 of the Internal Revenue Code. The solar ITC is generally 30% of the cost of the solar facility. The ITC is typically claimed in full at the time the solar facility is placed in service and is a dollar-for-dollar reduction in federal income tax liability. Without the ITC, many solar energy systems would not be built, which constrains development of supply and ultimately impacts SREC prices. Computer Science and Engineering, Southern Methodist University, Dallas, TX, 1998 (7) Jeffery L. Kennigtonand Richard V. Helgason. Minimum Cost Network Flow Algorithms, Springer US, ISBN , 2008 (8) Microsoft support website: These properties of solar markets and regulatory arrangements create arbitrage opportunities if future SREC prices and current prices are out of line. With the linear optimization approach, these properties have been factored into the Navigant s proprietary RECPET model to quantify their impacts on the SREC price, which results in more realistic price forecast. IV. CONCLUSION A linear optimization approach for SRECs pricing valuation that minimizes the total system cost with consideration of the relationships between the four primary drivers of demand, supply, cost of solar energy, and state RPS policies and taking into consideration regulatory issues and market design can be applied to multi-state SRECs market to provide pricing and valuation guidance for solar investors. REFERENCES (1) Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, Inc. ISBN X, 1993 (2) Andrew V. Goldberg and Robert E. Tarjan. "Finding minimum-cost circulations by successive approximation". Math. Oper. Res. 15 (3): , 1990 (3) Morton Klein. "A primal method for minimal cost flows with applications to the assignment and transportation problems". Management Science 14: , 1967 (4) Andrew V. Goldberg. An Efficient Implementation of a Scaling Minimum-Cost Flow Algorithm, Journal of Algorithms, 1997 (5) Damian J. Kelly, B.Command Garrett M. O Neill, B.Comm. The minimum cost flow problem and the network simplex solution method. A dissertation presented to the National University of Ireland, 1991 (6) J. Kenningtonand J. Whitler. Simplex versus cost scaling algorithms for pure networks: An empirical analysis. Technical Report 96-CSE-8, Department of