Carbon-Tuning New York’s Electricity System: Uncovering New Opportunities for CO2 Emissions Reductions

Distributed energy resources (DER), including technologies and services such as behind-the-meter generation, demand response, energy management, and energy efficiency, are touted as effective ways to improve electric system efficiencies and reduce harmful air emissions. The New York State Public Service Commission’s landmark Reforming the Energy Vision (REV) proceeding aims to unleash competitive forces that will invest in DER across the state with the explicit goal of reducing customer bills and the environmental impact of electricity production. As initiatives like New York’s REV continue, understanding the emission impacts of DER deployment becomes vital to ensure these efforts achieve the greatest environmental benefit possible. In this report, we present an analysis of the emission characteristics of New York’s electricity system. Using a linear regression model, we estimate marginal emission rates for CO2 and other pollutants from large centralized power plants. Our results show that the marginal emission rate of the State’s electricity system—and thus the emission reduction potential of DER—is dependent on both the time and location of DER operation in New York

Evolutionary course development in eLearning

Our shared experience is that too often, once a course is produced, insufficient resource is available to support its continued development from one presentation (or semester) to another. At the best this can lead to stale courses and jaded academics; at worst the course deteriorates from one presentation to the next with worsening student retention and performance. This paper outlines the challenges posed by these issues as they have emerged within a suite of eLearning courses created according to the new business model, and describes one of the tools we have developed to support student driven course evolution

Retail Rate Impacts of Distributed Solar: Focus on New England

The Lawrence Berkeley National Laboratory (LBNL) recently issued a study entitled “Putting the Potential Rate Impacts of Distributed Solar into Context,” authored by Galen Barbose. The LBNL study estimates the potential rate impact of distributed solar on national average retail electricity prices, and importantly, compares that impact to the potential impact of other rate drivers such as natural gas prices, renewable portfolio standards, and utility capital expenditures.1 This brief applies a similar style analysis as used by LBNL to regional and state level data to estimate more granular impacts for New England. We estimate rate impacts for various penetration rates of net metered distributed solar and compare them to the potential rate impacts of future natural gas prices, energy efficiency gains, RPS costs, RGGI costs, and utility capital expenditures. Like LBNL, we attempt to isolate the impact of these rate drivers as well as represent uncertainty around future policy choices, commodity costs, and technology costs

Practical application of pseudospectral optimization to robot path planning

To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platform’s dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simplified kino-dynamic models to avoid the significant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robot’s dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predefined analytical functions to enable real world application

The Clean Power Plan Puzzle: The Future of Efforts to Control Climate Pollution in the Northeast

In October 2015 the U.S. Environmental Protection Agency (EPA) finalized the first national plan to cut climate pollution from power plants. Called the Clean Power Plan (CPP), the effort requires a 32% nation-wide reduction in greenhouse gas (GHG) emissions from the power sector. The CPP also gives states multiple pathways to comply. Now states are on the clock: they must submit their individual compliance plans or signal their intent to submit multi-state plans by September 2016. The nine states participating in the Regional Greenhouse Gas Initiative (RGGI), the first market-based trading platform established to cut climate pollution from power plants in the Northeast, must now decide the future of the effort. This paper explores a few of the key issues for state regulators in the RGGI region with a special focus on New York State. We discuss the need to reset the RGGI cap to ensure progress toward New York’s and other state climate pollution reduction goals. We recommend a change to RGGI’s structure that will ensure compliance with the CPP. We discuss the EPA’s proposed Clean Energy Incentive Program (CEIP), an effort to encourage early state actions to reduce emissions. And we discuss other implementation issues with respect to linking RGGI to other mass-based state compliance plans. In brief, we recommend that the RGGI states adopt a new cap that requires at least a 2.5 percent per year reduction in region-wide GHG emissions