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Hi, I’m Ben!

An ad hoc collection of electricity policy papers and hiking pictures.


My name is Ben Griffiths and I explore the intersection of energy economics, technology, and public policy. I work on New England's wholesale power markets and other regional energy issues at LS Power. Previously, I worked for the Mass AGO, a Boston-based energy consultancy, and a failed wind energy start-up. I earned a master's at UT Austin and a bachelor's from Boston University. I've also written a handful of papers and reports on rate design, resource planning, avoided costs, and energy storage.

I cycle around town most of the time and walk in neat places whenever possible. I’m always happy to talk about energy policy, Greek history, bikes, and hikes.

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I've written or co-written a handful of papers on energy storage, storage-induced emissions, rate design, energy policy, and where they all meet in the middle.

A few of my favorites are below; more can be found in my archive or on SSRN.

Algorithmically developing efficient time-of-use electricity rates (2020)

Time-of-use (“TOU”) electricity rates are challenging to design and results often rely on a regulator’s subjective sense of reasonableness. Here, I describe an objective method to develop TOU rates using optimization techniques. The method identifies rates which minimize the difference between TOU and hourly costs, thereby ensuring rates are efficient and cost based. The technique also enables direct comparison of competing designs. I conclude by demonstrating how this methodology could be applied in practice.

Keywords: Rate Design, Time-of-Use Rates, Tariffs, Optimization

Expensive, Ineffective, & Occasionally Counterproductive: Clean Peak Standards Simulation Results for New England (2020)

Clean Peak Standards (“CPS”) have been proposed as a method to better align renewable generation with periods of higher electricity demand and higher emissions, by requiring that a percentage of peak period demand be met with renewables or clean-charged energy storage. Proponents argue that CPS can reduce costs, reduce emissions, and improve market efficiency.

Using a production-cost and capacity-expansion optimization model, we assess how CPS may affect wholesale market outcomes. We parameterize the model to approximate the New England system, and we test combinations of CPS and Renewable Portfolio Standards (RPS) that reflect needs into the 2040s. In some instances, we find that CPS are ineffective and expensive; in others, we observe that CPS make the grid dirtier and more expensive. CPS offer de minimis reductions in production costs (less than 1%), suggesting efficiency is not improved. Depending on formulation, CPS lead to modest increases in carbon emissions (less than 2%), or modest reductions. Reductions, when present, come at high cost: RPS can reduce emissions by 5-10 times more, per dollar spent. Despite the paucity of benefits, CPS increase system costs (less than 5%). These results suggest that regulators can achieve similar market and environmental outcomes, at lower cost, if they simply do not implement CPS.

Keywords: Policy Effects, Energy Storage, Renewable Electricity, GHG Emissions Reductions, Wholesale Electricity Markets

Reducing emissions from consumer energy storage using retail rate design (2019)

Published in Energy Policy (Vol. 129, June 2019, P. 481-490)

Minimizing retail electricity costs via demand charge management and energy arbitrage is a common application of behind-the-meter energy storage systems (ESS).  Research suggests that ESS tend to increase grid emissions, but some speculate that retail rate design could lessen its impact. This paper tests that theory. In this analysis, we pair five years of historic data from ISO New England (ISO-NE) and the PJM Interconnection with 15 commercial building load profiles to reveal how different rate designs influence emissions from ESS used for bill minimization. 

We find that rate design can be used to lessen the emissions impact of ESS in some markets and reduce net system emissions in others. In ISO-NE, rate design can be used to generate significant reductions in net system emissions. In PJM, emissions from the cleanest rate design are half as numerous as those from the dirtiest. Demand charges and energy charges offer multiple mechanisms to reduce ESS induced emissions. Minimizing different types of demand charges requires different quantities of dispatch from ESS: some demand charges can be minimized by ESS in just a few hours while others require discharge on many days. Real-time energy charges increase dispatch compared to flat energy charges because the ESS is used for both demand charge management and energy arbitrage (flat tariffs preclude energy arbitrage). Separately, real-time energy charges reduce emissions per MWh-stored compared to flat charges because the real-time rates exploit the positive correlation between marginal price and marginal emissions in the wholesale markets we study. A particular rate design that minimizes emissions in one market may increase emissions in another. This confounds the creation of universal solutions and instead highlights the need for approaches tailored to a specific energy market.

P.S., I also learned how to make linear programs using python, pyomo, & GLPK which is pretty neat.

Finding Carbon Breakeven: Induced Emissions from Economic Operation of Energy Storage in Renewables-Heavy Electricity Systems (2017)

In this paper, I explore varying system resource mixes and energy storage system operational modes that enable carbon-neutral, or carbon-reducing, usage. Specifically, I model the carbon emissions induced by energy storage operated in three ways – energy arbitrage (EA), demand charge management (DCM), and carbon minimization (MinCO2) – in 16 simulated electricity systems where wind and solar assets generate 17% to 81% of annual energy. Dispatch of a 1MW/4MWh battery is simulated for each operational mode and in each resource scenario (for a total of 64 combinations).

I find that energy storage is carbon-neutral, or carbon-reducing, in systems generating 17% to 40% of annual energy from renewables, depending on operational mode.

P.S., I also learned how to make some great looking charts and graphs.