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New Berkeley Lab Study Explores Impacts of High Variable Renewable Energy Futures

Posted May 16, 2018

Increasing penetrations of variable renewable energy (VRE) can affect wholesale electricity price patterns and make them meaningfully different from past price patterns. Many long-lasting decisions for supply- and demand-side electricity infrastructure and programs are based on historical observations or assume a business-as-usual future with low shares of wind and solar. Our motivating question is whether certain electric-sector decisions that are made based on assumptions reflecting low VRE levels will still achieve their intended objective in a high VRE future. We qualitatively describe how various decisions may change with higher shares of wind and solar and develop a foundation for quantitatively evaluating the impacts of VRE on these long-lasting decisions.

The study uses capacity expansion and unit commitment models to generate hourly energy and ancillary service prices along with annual capacity prices for scenarios with low and high VRE levels. The detailed grid simulations focus on the year 2030 for four electricity markets in the U.S.: CAISO, ERCOT, SPP, and NYISO. The analysis contrasts modeled market results for high wind and solar shares of 40-50% to a low renewable baseline reflecting 2016 penetration levels.

The technical report, which is accompanied by a slide deck along with the underlying raw modeling results, can be downloaded here. A webinar summarizing key findings will be held on May 23 at 10 am Pacific Time (1 pm Eastern). Register for the free webinar here.

We find that high shares of variable energy resources lead to several profound changes in the characteristics of electric power systems.

The most fundamental changes relate to the timing of when electricity is cheap or expensive and the degree of regularity in those patterns. Diurnal price patterns change significantly, especially in scenarios with large solar shares. With high solar shares in ERCOT, for example, prices slump in the middle of the day to an average of $10/MWh and then rise in the evening to an average of $80/MWh. These price dynamics may support electric-vehicle charging infrastructure at commercial sites that can be accessed during the day instead of residential charging that would occur at night—opposite of the situation with low shares of VRE.

The frequency of periods with low prices (below $5/MWh) increases to between 3% and 19% of hours in the high VRE scenarios depending on the region and mix of renewables. High solar in ERCOT, with its limited interconnection capacity to neighboring regions, experiences the highest frequency of periods with near-zero prices.

Average annual hourly wholesale energy prices also decrease with more VRE penetration, by $5-$16/MWh depending on the region and mix of wind and solar. Decreases in average wholesale prices and common occurrences of periods with very low prices will affect the profitability of VRE and inflexible generators that operate in these hours, but also presents an opportunity to shift or increase demand at very low cost

Additional findings include:

Price volatility increases with higher VRE shares, particularly in the high wind scenarios. Morning prices in the spring in CAISO can vary between $0 and $50/MWh with high wind, but fall in a much narrower range in the low VRE scenario.

Ancillary service prices rise substantially with high VRE (up to 8-fold), especially for regulation and spinning reserves. Higher ancillary service prices could attract new market entrants such as batteries or incent wind and solar to offer these services themselves.

Peak net-load hours are shifted over shorter time periods into the evening, yet are distributed over more days of the year. This highlights opportunities for demand response programs or redesigned critical peak pricing programs.

While we only highlight qualitatively the possible impact of these altered price patterns on demand- and supply-side electric sector decisions in this report, the core set of electricity market prices derived here provides a foundation for later planned quantitative evaluations of these decisions in low and high VRE futures.

Source: LBL

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