Optimal Generation and Trading in Solar Renewable Energy Certificate (SREC) Markets

ABSTRACT

SREC markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh. Firms offset these certificates against the floor and pay a penalty for any lacking certificates. Certificates are tradable assets, allowing firms to purchase/sell them freely. In this work, we formulate a stochastic control problem for generating and trading in SREC markets from a regulated firm’s perspective. We account for generation and trading costs, the impact both have on SREC prices, provide a characterization of the optimal strategy and develop a numerical algorithm to solve this control problem. Through numerical experiments, we explore how a firm who acts optimally behaves under various conditions. We find that an optimal firm’s generation and trading behaviour can be separated into various regimes, based on the marginal benefit of obtaining an additional SREC, and validate our theoretical characterization of the optimal strategy. We also conduct parameter sensitivity experiments.

KEYWORDS:

• Commodity markets
• stochastic control
• SREC
• cap and trade
• market design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Not all generators of renewable energy who participate in REC markets are regulated Load Serving Entities (LSEs), though, in this work, we largely focus on the decisions faced by those who are regulated.

2. The largest and most mature SREC market in North America is the New Jersey SREC Market.

3. In New Jersey’s SREC market, unused SRECs can be banked for four additional years, giving them a five-year life in total.

4. Any bound that is lower is practically meaningless as firms must be able to generate at or more than their ‘baseline’ generation rate.

5. As with any numerical solution, there is a trade-off between grid size (accuracy of the dynamic program solution) and run-time. The grid we use provides an acceptable trade-off between these two, and we observed no further increase in accuracy by increasing the grid size.

6. While the random numbers used to generate paths are identical, the presence of price impact leads to different paths as impact varies.

7. At the terminal time-step.

Additional information

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada [RGPAS-2018-522715, RGPIN-2018-05705].