The Renewable Energy Delivery (RED) project has conducted projects spanning several technologies to address critical challenges in scaling up renewable sources. RED researchers apply traditional supply chain approaches – such as network design, forecasting, demand shaping, supply planning, storage, and distribution management – to energy supply networks as they increasingly incorporate renewable sources like wind that are more intermittent. The combination of spatial and temporal demand for multiple energy forms, intermittent supply, and multiple paths for transmission makes energy a rich supply chain for investigation. We aim to develop spatial-temporal models to design energy storage and transmission strategies for renewable energy delivery.
Vehicle-to-Grid Revenue Potential for Electric and Plug-In Hybrid Electric Vehicle Fleets
Vehicle-to-grid (V2G) describes a system where electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) can connect to the electric grid to provide ancillary services, such as frequency regulation, to grid operators. This thesis evaluates the opportunities for V2G-enabled EVs and PHEVs to participate in the regulation services market and lower their net costs, making them more cost competitive with conventional vehicles. We build a ten-year net cash flow model for a fleet of delivery trucks to assess the costs and benefits of adopting this technology. To project potential V2G revenue, we utilize a simulation model developed by a grid system operator. Based on exploration of numerous scenarios we determine which combination of factors produce the greatest overall benefit. Our results indicate that EV and PHEV fleets offer lower operating expenses for urban pickup and delivery services. In addition, fleet managers can expect to offset 5-11% of the total cost of ownership with V2G revenue.
The results of this project were published as a master’s thesis by Kristen Nordstrom and Andres De Los Rios Vergara. Clay Siegert of XL Hybrids played an important role as a company advisor for the project. The Executive Summary is available on the Publications page.
Large-Scale Battery Storage for Wind
Wind energy is the fastest-growing energy source in the world. However, due to the inherent issues of intermittency and remoteness, the need to complement wind energy with storage is well recognized. Pumped Hydro Electric Storage units are the established and most widely studied energy storage application. Other technologies like compressed air, flywheels, and batteries are emerging, but are not yet considered economically viable.
This project aimed to design a profitable battery storage system. Configuration options included network design decisions (e.g., facility location, capacity, market participation, and grid energy supplement) and daily operating policies (i.e., policies for charging and discharging the battery). A detailed Monte Carlo simulation model was developed with realistic conditions for wind plant output, market prices, storage costs, and technical characteristics to calculate profits. The simulation results identify decisions and conditions under which a large-scale battery storage installation can be profitable. Counter to prevailing wisdom, the project demonstrated that a large-scale battery in the grid could be profitable without special subsidies. Moreover, the differential between simple and sophisticated daily operating policies indicates great promise for further work.
The results of this project were published as a master’s thesis by Prashant Saran and Clay Siegert and in the Proceedings of the 2010 IEEE Power Engineering Society General Meeting.
Green Hydrogen from Wind in Spain
This RED project, focused on optimizing the potential supply chain of hydrogen, was funded by the Government of Spain (Ministry of Industry) and included researchers at MIT and the Zaragoza Logistics Center through the MIT SCALE Network. The research was developed in direct collaboration with Acciona Energy, the world’s largest developer of wind parks and third-largest operator of wind energy.
We developed a comprehensive decision support model – utilizing principles from the commercial supply chain, systems engineering, and operations research – to optimize delivery of hydrogen produced by wind power through electrolysis. We applied the model to various future scenarios to inform strategic evaluation of various technologies for the production, storage, and distribution of hydrogen and assess infrastructure development as a phased rollout.
Biofuel Supply Chain Design Challenges
The biofuel supply chain comprises several stages and includes processes such as harvesting, collecting, processing, transporting, handling, warehousing, distributing, and retailing. To begin, biomass feedstock from agricultural residues, forest resources (e.g., hardwood and softwood residues, and thinnings), and dedicated cropping systems (e.g., poplar and switchgrass) must be transported to the refinery. However, unlike petroleum, biomass production is diverse, distributed, and seasonal, with significant fractions that cannot be used for fuel. Pretreatment facilities could allow biomass to be stored and transported economically to the biorefinery. The scale – location and size – of these pretreatment facilities is an important design decision; and these strategies may vary by geographic region according to supply-demand conditions. These strategies must also incorporate the inbound transportation of feedstock in its various forms to the biorefinery.
Biorefinery management offers further opportunities to optimize the combination of efficiency, capacity, and cost for each facility. Just as with pretreatment facilities, the size and location of biorefineries are critical design decisions. Further production planning regarding the product mix (i.e., quality or grade), batch size, and blending must be aligned with the available supply and the distribution plans.
Various distribution strategies can be deployed to move biofuel from biorefineries to marketing terminals before being trucked to service stations, truck stops, and other large-scale operations. Currently, most fuel is distributed through common carrier transportation providers using various modes (e.g., pipeline, barge, ship, rail, and truck). Assuming biofuels are fungible and can use existing distribution systems, there are still many opportunities to optimize the flow by using the appropriate mode and the option of intermediate breakout storage to achieve the lowest landed cost.
Specific analysis is needed to assess the role of numerous technologies and processes to transform biomass supply into fuel, various channels and methods for transporting feedstock and fuel, as well as diverse market conditions that impact demand and overall SC performance. Furthermore, robust assessment approaches are needed to estimate key measures, such as cost, revenue, environmental impact (e.g., GHG), oil displacement, and system safety and security. These measures are critical in determining the economic and environmental viability of the entire system.
A 2010 master’s thesis by Sooduck Chung and Michael Farrey offers a comprehensive description of the entire biofuel supply chain network. It also offers focused analysis on switchgrass, which holds the most promise in the U.S. because of its ability to be scaled and its potential for high yields. However, landed cost analysis across the five stages in the biofuel supply chain - feedstock production, feedstock logistics, ethanol production, distribution, and end use - shows that ethanol from switchgrass is not competitive in price compared with gasoline at 2010 prices. The thesis also focuses analysis on feedstock production and logistics to evaluate various harvesting, storage and transportation options in moving switchgrass to the refinery. Transporting feedstock in its most energy dense form provides the lowest transportation costs; but this usually means additional costs and steps to preprocess the feedstock. In this case, the options to bale, grind, or pelletize the switchgrass are considered for varying sizes of refineries. Analysis showed that grinding switchgrass is cost effective up to a distance of 22 miles from the refinery, where pelletizing switchgrass becomes cheaper.