Michigan Memorial Phoenix Energy Institute - Energy, Science, Technology and Policy (ESTP) Award
Energy use and climate change are two of the most critical areas for sustainability research in the 21st century. Greenhouse gas emissions from the electric power sector are larger and growing faster than any other source in the U.S., and as a result, electricity is often the focus of policy. For example, 22 states have introduced “renewable portfolio standards” (RPSs) that require increasing amounts of electricity to come from renewable generators, with long-term targets of up to 33%. Though many targets are regarded as aggressive, it is widely agreed that even more renewable energy will be needed to adequately address the climate change problem. Yet not enough is understood about the maximum amount of renewables the grid can accommodate, how to optimally deploy them, or the outcomes of public policy in this realm.
The purpose of this research is to understand the interplay between renewable energy technologies, electricity consumption patterns, and policies designed to support renewable energy technologies. One of the key challenges to integrating renewable generators into the grid is that resource availability and electricity demand are variable in space, and often uncorrelated in time. This program will create a framework that models this intermittency. The framework will be incorporated into market-driven models of consumer and producer investment in clean energy technologies. These models will be used to evaluate a range of policy and economic options, which themselves may vary spatially.
Objectives and activities. The first objective is to study renewable generator performance and optimization in situations where renewables meet portions of energy consumption beyond current RPS targets. To support this objective, we will engage in three basic research activities. First, we will develop analytical techniques and GIS models that characterize spatial-temporal correlation in output for a number of renewable resources (wind, solar, geothermal, etc). Second, we will create optimization methods that accommodate the intermittent characteristics of renewables. Third, we will build lifecycle analysis models to examine the impact of intermittency on emissions. This effort will result in a framework that identifies optimal spatial configurations of renewable generators under various objective functions such as grid reliability, CO2 emissions reductions, and cost/kWh. Variables subject to study will include the mix and location of renewables, the electricity infrastructure, and the demand profile itself.
The second objective is to study the interplay between renewable technology and the effectiveness of policy instruments and market mechanisms that support renewables. We will pursue this objective via two basic research activities. First, we will construct microeconomic models of market mechanisms designed to support renewables (e.g. renewable energy certificates, or RECs), and use REC market data to empirically test hypotheses generated by the models. Second, we will use generating firm data to evaluate how policy influences investments in renewable generation capacity. Here, in addition to considering the spatial variability of resources, we will study the effect of spatial variability in policies—such as differences among states in RPS targets and rules for REC trading.
Impact. Research in this unexplored area of large-scale renewable energy resource availability and utilization will open lines of investigation at the interface of energy policy, applied economics, industrial ecology and power systems engineering. The intellectual merit of the work will derive from: (1) basic research in spatial analysis of resource availability, (2) new optimization methods for large spatial problems, (3) life cycle analysis methods that accommodate intermittency (4) new models of environmental markets and (5) analyses of market performance and firm behavior. The work will guide energy and climate policy at state, regional and national levels and investment in electricity generating assets.
- A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles [Proc. ASME]
- Examining the Benefits of Optimal Spatial Diversification of Wind Capacity
- Impact of Battery Sizing on Stochastic Optimal Power Management in Plug-in Hybrid Electric Vehicles
- Tapping the Energy Storage Potential in Electric Loads to Deliver Load Following and Regulation, with Application to Wind Energy