Solar and wind have become the de-facto technologies for attaining 100 percent clean-power targets. But their intermittency is such that we have to store energy for many hours if we rely only on these resources to cover 100 percent of the load. Ensuring solar and wind resources’ adequacy with batteries is much more expensive than using some dispatchable low carbon resources like a natural gas combined-cycle retrofitted with carbon capture (“NG4C”).

We have found that NG4C is more cost-effective than long-duration batteries for delivering 100 percent clean-power in California and Texas. It is also the case when assuming a modest…


Reuters photo

The Arctic blast has shown how challenging is to hedge renewable merchant risk

ERCOT (i.e., Texas) is home to many wind projects under a merchant model, and some of them had to hedge their energy price to secure financing. However, it is far more complicated to hedge wind power generation than dispatchable energy. For instance, the Arctic blast that threw Texas in deep freeze led some wind producers to massive losses because of their hedging program. Without the hedges, they would have triple their cumulated 12-month margin.

Two reasons make hedging wind power generation challenging in ERCOT. One is the…


The short answer is no as long-duration merchant batteries are not yet cost-effective, and the associated margins (i.e., time spreads) are not large enough. For example, if we use the ERCOT West hub, the average values captured in day-ahead and real-time energy price arbitrage under normal conditions — since 2018 — are $15.80/kWh-year, $10.30/kWh-year and $6.05/kWh-year for batteries of 2-h, 4-h, and 8-h duration respectively. The capital cost of storing energy with chemical batteries today is at least $200/kWh, making it too expensive. Merchant batteries are most valuable today for providing flexible capacity than capturing time spreads.

Why ERCOT?

ERCOT (i.e., Texas)…


Introduction

You may have realized as a management consultant at the analyst/associate level that a decent knowledge of data science would have been instrumental in some client engagements for achieving high impact analyses. How often did your analysis requires processing a large amount of data too big for Excel but not big enough to be called Big Data — the “not so Big Data” challenge? When did you wish you knew more than a regression analysis for identifying relationships? How clueless were you when your manager asked you to do an optimal allocation of 100+ resources? How frustrated were you of…

Robin Duquette

CEO, Pyxidr

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