Two-Stage Monte Carlo Simulation to Forecast Levelized Cost of Electricity for Wave Energy
Rachael Green is currently an undergraduate senior at the University of California, Berkeley. She is majoring in Environmental Economics and Policy in the College of Natural Resources with a minor in History from the College of Letters and Science. She has worked as an undergraduate researcher in the collaborative effort between the Renewable and Appropriate Energy Laboratory and CalWave Power Technologies, Inc. Despite wave energy’s vast global potential, there has been relatively little commercial deployment to date. This has been partially attributed to the large uncertainty in both the current estimated and future expected electricity generation costs associated with wave technologies. Her presentation quantifies the uncertainty of the forecasted levelized cost of electricity (LCOE) for wave energy as it relates to United States and European Union energy targets. Next month she will present this work at the International Conference on Renewable Energy Research and Applications. After graduating, Rachael hopes to continue working in the renewable energy field.
Rong HAN (Ph.D Candidate in Beijing Institute of Technology, Visiting Scholar in Berkeley Energy and Climate Institute).Research progress of IAMs Downscaling model, Evaluation model and Land use model
Rong HAN is currently a third year PhD Candidate in Center for Energy & Environmental Policy Research, Beijing Institute of Technology. She came to Berkeley's Energy and Climate Institute as visiting scholar for one year. During the process of PhD student, her research mainly focus on assessment of global climate policy, China’s carbon emission trading market and carbon finance. These researches have published on Journal of Cleaner Production, Natural Hazards, and Energy Policy.
Climate change is a complex and comprehensive process, which can only be understood on the basis of the combined insights from various scientific disciplines. In recent years the need for integration of information among ‘earth system’ (ES), ‘vulnerability, impact, and adaptation assessment’ (VIA), and ‘integrated assessment’ (IA) communities has become stronger. The IAM (integrated assessment models) model is designed to couple of ES and IA models to account of the possible feedbacks between human systems and the earth system on the global scale. Her presentation will be focus on the recent research progress of IMA downscaling model, evaluation model and land use model.
James Merrick’s research focuses on the improvement of mathematical modeling methods to address a variety of energy and climate planning problems. This talk will discuss this research, with an emphasis on how to structure models to provide economic and policy insight, focusing on appropriate valuation of renewables and energy storage options.
James completed his PhD in Management Science and Engineering at Stanford University in January 2018. He previously completed a dual masters degree in Technology & Policy and Electrical Engineering & Computer Science at MIT, and a Bachelor of Engineering degree at University College Dublin. Since completing his PhD, James applies his research to, and builds optimization models for, EPRI, a stealth robotics startup in San Francisco, and a major electricity generator in Ireland. In addition, James is undertaking a number of research projects with colleagues at NASA, EPRI, and Stanford and when possible, likes to help develop his family’s farm in Ireland.
According to the U.S. Department of Energy, wave energy has the potential to power over 100 million US homes, but is completely underutilized at the moment. Wave energy has the advantage of higher predictability, nighttime availability, and a high energy density (~30 kW/m of coastline). Such high energy densities also enable the use of the renewable resource for desalination. The Renewable and Appropriate Energy Laboratory at UC Berkeley has partnered with CalWave Power Technologies, one of the winners of the US Wave Energy Prize, to better assess this potential. To learn more about CalWave, please visit http://calwave.org. This presentation will include preliminary results from this collaboration including appropriate siting, economic modeling, and performance characterization for wave energy technologies.
A video abstract for the paper is available here.
The global carbon emissions budget over the next decades depends critically on the choices made by fast-growing emerging economies. Few studies exist, however, that develop country-specific energy system integration insights that can inform emerging economies in this decision-making process. High spatial- and temporal-resolution power system planning is central to evaluating decarbonization scenarios, but obtaining the required data and models can be cost prohibitive, especially for researchers in low, lower-middle income economies. Here, we use Nicaragua as a case study to highlight the importance of high-resolution open access data and modeling platforms to evaluate fuel-switching strategies and their resulting cost of power under realistic technology, policy, and cost scenarios (2014–2030). Our results suggest that Nicaragua could cost-effectively achieve a low-carbon grid (≥80%, based on non-large hydro renewable energy generation) by 2030 while also pursuing multiple development objectives. Regional cooperation (balancing) enables the highest wind and solar generation (18% and 3% by 2030, respectively), at the least cost (US$127 MWh−1). Potentially risky resources (geothermal and hydropower) raise system costs but do not significantly hinder decarbonization. Oil price sensitivity scenarios suggest renewable energy to be a more cost-effective long-term investment than fuel oil, even under the assumption of prevailing cheap oil prices. Nicaragua's options illustrate the opportunities and challenges of power system decarbonization for emerging economies, and the key role that open access data and modeling platforms can play in helping develop low-carbon transition pathways.
The large-scale utilization of electricity generated from biomass partnered with carbon capture technology — dubbed bioenergy with carbon capture and sequestration (BECCS) by the researchers — could result in greatly reduced emissions and a “carbon-negative” power system in the western US, according to new research from the University of California at Berkeley.
Study lead author, Daniel Sanchez, noted in a recent press release that this combination could potentially offset the carbon emissions associated with other sources as well — such as fossil fuel power plants, and the transportation sector (diesel- and gas-powered vehicles).
o be specific, the new research found that BECCS when combined with aggressive renewable energy deployment and fossil fuel–associated emissions reductions could result in a “carbon-negative power system” in Western North America by the year 2050 — with an up to 145% emissions reduction as compared against 1990 levels.
Reductions that significant could occur with as little as 7% of total electricity coming from BECCS, according to the new findings — which were arrived at via computer modeling.
In many of the other scenarios explored by the new research, the offsetting of carbon emissions provided by BECCS was more valuable to the electric system than the electricity produced itself was. Of course this kind of “value” is a relative one — as all values are. If governments don’t value the offsetting of carbon emissions, for instance,…
Those behind the new research admit that biomass + carbon capture is still a bit of an unknown in many ways, so the findings are tentative ones, until put into practicem that is — which is what the researchers want.
“There are a lot of commercial uncertainties about carbon capture and sequestration technologies,” stated researcher Sanchez. “Nevertheless, we’re taking this technology and showing that in the Western United States 35 years from now, BECCS doesn’t merely let you reduce emissions by 80% – the current 2050 goal in California – but gets the power system to negative carbon emissions: you store more carbon than you create.”
Possibly… that is. I admit to having some doubts about this, but interesting work nonetheless.
Image Credit: UC Berkeley
The need to mitigate climate change, safeguard energy security, and increase the sustainability of human activities is prompting a rapid and global transition from carbon-intensive fuels to renewable energy (IPCC 2014). Among renewable energy systems, solar energy has one of the greatest climate change mitigation potentials with life cycle emissions as low as 14 g CO2-eq KWh-1 (carbon dioxide equivalent per kilowatt hour; compare this to 608 g CO2-eq KWh-1 for natural gas). Solar energy embodies diverse technologies able to capture the sun’s thermal energy, such as concentrating solar power (CSP) systems, and photons using photovoltaics (PV). Solar energy systems are highly modular ranging from small-scale deployments (≤ 1 megawatt [MW]; e.g., residential rooftop modules, portable battlefield systems, solar water heaters) to centralized, utility-scale solar energy installations (USSE, ≥1 MW) where a large economy of scale can meet greater energy demands. Nonetheless, the diffuse nature of solar energy necessitates that large swaths of space or land be used to collect and concentrate solar energy into forms usable for human consumption, increasing concern over potential impacts on natural ecosystems, their services, and biodiversity therein. For example, at a capacity factor of 0.20, a single terawatt of USSE capacity scales to 142,857 km2, roughly the area of the state of New York, USA, providing challenges for the integration of potentially massive projects into complex and fragmented landscapes.
The decisions humans make about how much land to use, where, and for what end-use are drivers of Earth system processes. For example, changing the use of land or converting it from one land-cover type to another is a source of greenhouse gas emissions, which are released to the atmosphere when biomass, including soil, is disturbed or removed. How then do we decide when to convert a forest that serves as a carbon sink into a farm that feeds a community, or a farm into a PV park that electrifies a rural village? Innovation and policies directing sustainable pathways of land use for energy and food production can be utilized to address an increasing global population of which 1.5 billion today live without access to electricity. Energy poverty leads to a loss of human health and wellbeing and depressed economic and educational opportunities, particularly for women and children. Our research here is designed to demonstrate, quantify, and facilitate the potential of solar energy systems to address global problems related to climate change, energy access, and the sustainability of food systems, which are interconnected. This research draws from ecological field experiments, knowledge data discovery, geographic information systems, spatial and economic modeling, and is comprised of five interrelated projects:
Environmental co-benefits of solar energy
The Energy-Food-Water Cube: Capability and scalability in on-farm energy production
Global solar energy brightspots: Shinning light on the world’s energy insecure
The land-energy-food nexus in California's Central Valley
Limits of land: Global estimates of land for food and energy
Decarbonizing electricity production is central to reducing greenhouse gas emissions. Exploiting intermittent renewable energy resources demands power system planning models with high temporal and spatial resolution. We use a mixed-integer linear programming model – SWITCH – to analyze least-cost generation, storage, and transmission capacity expansion for western North America under various policy and cost scenarios. Current renewable portfolio standards are shown to be insufficient to meet emission reduction targets by 2030 without new policy. With stronger carbon policy consistent with a 450 ppm climate stabilization scenario, power sector emissions can be reduced to 54% of 1990 levels by 2030 using different portfolios of existing generation technologies. Under a range of resource cost scenarios, most coal power plants would be replaced by solar, wind, gas, and/or nuclear generation, with intermittent renewable sources providing at least 17% and as much as 29% of total power by 2030. The carbon price to induce these deep carbon emission reductions is high, but, assuming carbon price revenues are reinvested in the power sector, the cost of power is found to increase by at most 20% relative to business-as-usual projections.
While a doctoral student in RAEL and ERG, Gang He was also a Visiting Faculty Affiliate for the China Energy Group, Energy Technologies Area, at Lawrence Berkeley National Laboratory, as well as an Assistant Professor in the Department of Technology and Society, at Stony Brook University. He has worked with the China Energy Group since 2011. His work focuses on energy modeling, energy economics, energy and climate policy, energy and environment, domestic coal and power sectors and their key role in both the global energy supply and in international climate policy framework. He also studies other interdisciplinary aspects of global climate change and the development of lower-carbon energy sources.
Prior to Berkeley, he was a research associate with Stanford University's Program on Energy and Sustainable Development from 2008 to 2010.