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Transmission planning requires solving an optimal power flow problem for each considered investment plan for multiple operating conditions. AC power flow formulations are more accurate, but for transmission planning on large systems may be computationally intractable and can converge to local optima. Regional transmission studies usually rely on linearized DC power flow formulations, which reduce computation time but sometimes produce solutions that are inferior when the AC flows are resolved. We propose an approach to regional-scale transmission planning that approximates the AC power flow results using the faster DC formulation. An initial sample of investment plans is simulated with both AC and DC power flows, and then the singular value decomposition is applied to characterize the error between the two formulations and form a projection that estimates the AC costs from the DC costs. The estimation is used in a DC formulation of the transmission investment problem. We show that the proposed approach accurately estimates the AC operation costs and returns expansion plans that are consistently reliable with AC power flows. We also demonstrate that traditional DC formulations can produce low-cost, AC-feasible investment plans if enough representative hours are selected and weighted effectively.

As electricity from coal declines, co-firing coal plants with biomass has been proposed to extend coal unit life, increase production, and reduce carbon emissions. Previous studies reach conflicting conclusions on whether coal biomass co-firing would result in a net increase or decrease in carbon emissions. We explore whether biomass co-firing would decrease emissions using a novel framework that includes two critical features of electricity markets: strategic adoption decisions by firms and intertemporal constraints on power plant operations. We apply this framework to a case study based on the Midwestern U.S. electricity market and show that profit maximizing firms will retrofit mid-efficiency coal units, rather than the most or least efficient units. We demonstrate that, contrary to expectations, this strategy leads to a net increase in system-wide carbon emissions under high carbon prices because of the other generators displaced by co-firing units.

With the increased focus on responding to climate change by accelerating a transition to a low-carbon energy system, differing views remain on the combination of energy technologies that will best achieve this goal. Identifying technological pathways is complicated by wide uncertainties in economic and technological factors. Analyses that neglect these uncertainties can produce pathways for a low-carbon energy future that are highly granular and specific, but which are based on a particular assumption about future conditions and imply a need to make specific technology commitments over a long period of time. We frame the energy transition problem as the identification of one near-term investment strategy that is flexible across a wide range of possible future costs, followed by many alternative subsequent investment plans, each of which responds to realized future costs to achieve an aggressive emissions reduction target. Using an example of planning a low-carbon power system under uncertainty, we demonstrate the option value of not ruling out some energy technologies in the near term.

Climate policy decisions are necessarily sequential decisions over time under uncertainty, given the magnitude of uncertainty in both economic and scientific processes, the decades-to-centuries time scale of the phenomenon, and [...]

As scientists seek to better understand the linkages between energy, water, and land systems, they confront a critical question of scale for their analysis. Many studies exploring this nexus restrict themselves to a small area in order to capture fine-scale processes, whereas other studies focus on interactions between energy, water, and land over broader domains but apply coarse resolution methods. Detailed studies of a narrow domain can be misleading if the policy intervention considered is broad-based and has impacts on energy, land, and agricultural markets. Regional studies with aggregate low-resolution representations may miss critical feedbacks driven by the dynamic interactions between subsystems. This study applies a novel, gridded energy–land–water modeling system to analyze the local environmental impacts of biomass cofiring of coal power plants across the upper MISO region. We use this framework to examine the impacts of a hypothetical biomass cofiring technology mandate of coal-fired power plants using corn residues. We find that this scenario has a significant impact on land allocation, fertilizer applications, and nitrogen leaching. The effects also impact regions not involved in cofiring through agricultural markets. Further, some MISO coal-fired plants would cease generation because the competition for biomass increases the cost of this feedstock and because the higher operating costs of cofiring renders them uncompetitive with other generation sources. These factors are not captured by analyses undertaken at the level of an individual power plant. We also show that a region-wide analysis of this cofiring mandate would have registered only a modest increase in nitrate leaching (just +5% across the upper MISO region). Such aggregate analyses would have obscured the extremely large increases in leaching at particular locations, as much as +60%. Many of these locations are already pollution hotspots. Fine-scale analysis, nested within a broader framework, is necessary to capture these critical environmental interactions within the energy, land, and water nexus.

Given uncertainty in long-term carbon reduction goals, how much non-carbon generation should be developed in the near-term? This research investigates the optimal balance between the risk of overinvesting in non-carbon sources that are ultimately not needed and the risk of underinvesting in non-carbon sources and subsequently needing to reduce carbon emissions dramatically. We employ a novel framework that incorporates a computable general equilibrium (CGE) model of the U.S. into a two-stage stochastic approximate dynamic program (ADP) focused on decisions in the electric power sector. We solve the model using an ADP algorithm that is computationally tractable while exploring the decisions and sampling the uncertain carbon limits from continuous distributions. The results of the model demonstrate that an optimal hedge is in the direction of more non-carbon investment in the near-term, in the range of 20-30% of new generation. We also demonstrate that the optimal share of non-carbon generation is increasing in the variance of the uncertainty about the long-term carbon targets, and that with greater uncertainty in the future policy regime, a balanced portfolio of non-carbon, natural gas, and coal generation is desirable.

Climate change and other environmental challenges require the development of new energy technologies with lower emissions. In the near term, R&D investments, either by the government or the private sector, can reduce the costs of these lower-emitting technologies. However, the returns to R&D are uncertain, and there are many potential technologies that may emerge to play an important role in the future energy mix. In this paper, we address the problem of allocating scarce R&D resources across technologies when uncertainties exist. We develop a multistage stochastic dynamic programming version of an integrated assessment model of the climate and economy that represents endogenous technological change through R&D decisions for two substitutable noncarbon backstop technologies. We demonstrate that near-term R&D investment in the higher cost technology is justified and that the optimal R&D investment in the higher cost technology increases with both higher variance and higher skewness in the distribution of returns to R&D.

ABSTRACTA pressing question facing policy makers today in developing a long-term strategy to manage carbon emissions from the electric power sector is how to appropriately balance investment in R&D for driving innovation in emerging low- and zerocarbon technologies with investment in commercially available technologies for meeting existing energy needs. Likewise, policy makers need to determine how to allocate limited funding across multiple technologies. Unfortunately, existing modeling tools to study these questions lack a realistic representation of electric power system operations, the innovation process, or both. In this paper, we present a new modeling framework for long-term R&D and electricity generation capacity planning that combines an economic representation of endogenous non-linear technical change with a detailed representation of the power system. The model captures the complementary nature of technologies in the power sector; physical integration constraints of the system; and the opportunity to build new knowledge capital as a non-linear function of R&D and accumulated knowledge, reflective of the diminishing marginal returns to research inherent in the energy innovation process. Through a series of numerical experiments and sensitivity analyses— with and without carbon policy—we show how using frameworks that do not incorporate these features can over- or under-estimate the value of different emerging technologies, and potentially misrepresent the cost-effectiveness of R&D opportunities.

Airport delays are very common in most of the heavy traffic airports. Most of these delays are due to heavy traffic volume, capacity shortages, poor weather conditions, inefficiencies of aircraft operations and the effect of schedule changes. These delays can be significantly reduced by balancing capacity and demand. Long-term options can be capacity expansion and adding more facilities, both of which are time consuming and involve significant investment. Sometimes such expansion may not be possible due to high population density in the vicinity of the airport. Demand management is an available alternative implementable through slot limits or congestion pricing. These measures are often not implemented due to opposition from airline operators. This study proposes another approach by improving airport capacity at the tactical level. Airport throughput basically depends on the availability of runways. This can be improved by balancing arrival and departure rates. The air traffic controllers make such decisions based on their experience and depending on inertia, wind speed and directions, arrival and departure schedules, noise abatement procedures, configuration switch proximity and inter-airport coordination. This article supports a decision support tool to balance the arrival and departure rates considering the stochasticity of the airport operations by solving the problem of runway configuration selection. (35 refs.) Results: The dynamic control of runway configurations as well as arrival and departure rates is formulated as a finite-horizon dynamic programming model. A day of operation is assumed from 6am and is divided into 72 periods of fifteen minutes duration. The time periods are indexed as . The decisions are made in the beginning of each period of fifteen minutes. Operations between midnight and 6am are not considered here as during this constraints are noiseabatement procedures and other environmental issues and not the capacity. Inputs to the problem are the arrival and take of schedules and estimates of capacity of each runway. The scheduled number of arrivals and takeoffs during the period are respectively. The capacity of each runway configuration is represented as an operational throughput envelope and will be non-increasing in the presence of continuous demand. This representation includes the variability of the traffic mix such as aircraft types and the sequencing of arrivals and departures. Since weather conditions are important, the operational throughput envelope for each run way includes visual meteorological conditions (VMC) and instrument meteorological conditions (IMC) so that good and poor weather conditions are taken in to account in this model. It is shown here that the association of different VMC and IMC generates different arrival and departure schedules. Also larger average throughput can be achieved in VMC then in IMC. The arrival service rates and the departure service rates for a runway are non-increasing. Consider a period in a day when there are large number of arrivals and departures. It will be advantageous to operate under configuration 2 to have a large number of departure throughputs. If in another period the arrivals are more, then changing to configuration 1 will be helpful. However the cost and the practical difficulties in changing configurations also are to be considered. The stochasticity of airport operations makes it difficult to foresee future evolutions. The decisions on each period depend up on the state conditions such as arrival queue length, departure queue length, the runway configuration at use at the preceding period, and the weather conditions for the current period. As per the safety regulations, the runway can be used only if the tailwind and the crosswind do not exceed five and twenty knots. Some runways may face this problem of unsafe wind condition and hence may not be operable during that period. Arrival and departure queue are modeled as using two dynamic and stochastic queuing models. Demand is considered as a Poisson process whose rates are determined by the number of arrivals and departures scheduled in that fifteen minute period. The service is modeled as an Erlang process of order . The rates are stationary for the fifteen minute period but not stationary for the time horizon. The arrival and departures rates not independent of each other as they depend on the same weather conditions. Since the model is Markovian it is tractable. Switching runway configurations is a challenging task since many agencies are involved and the aircraft need to be re-routed, impacting overall scheduling and costs. In this study, the cost of configuration change is included as the cost of idling of the runway during the change over and the arrival and departure rates are zero since no aircraft is being serviced during the changeover period. This increases the queue length of both the arrival and departure queues. This can be effectively incorporated in the model by modifying the queue length probabilities. Additional changes in the model cane incorporated without any extra computational cost. Weather and wind dynamic also are suitably captured in to the model as time-varying two-state Markov chain. Solution algorithms and numerical examples are also provided. [ABSTRACT FROM AUTHOR]

In 2011, the state of Texas experienced the lowest annual rainfall on record, with similar droughts affecting East Africa, China and Australia. Climate change is expected to further increase the likelihood and severity of future droughts. Simultaneously, population and industrial growth increases demand for drought-stressed water resources and energy, including electricity. In the US, nearly half of water withdrawals are for electricity generation, much of which comes from greenhouse gas emitting fossil fuel combustion. The result is a three-way tension among efforts to meet growing energy demands while reducing greenhouse gas emissions and water withdrawals, a critical issue within the so-called water–energy nexus. We focus on this interaction within the electric sector by using a generation expansion planning model to explore the trade-offs. We show that large reductions in CO2emissions would probably increase water withdrawals for electricity generation in the absence of limits on water usage, and that simultaneous restriction of CO2emissions and water withdrawals requires a different mix of energy technologies and higher costs than one would plan to reduce either CO2or water alone.

Given the large uncertainties regarding potential damages from climate change and the significant but also uncertain costs of reducing greenhouse emissions, the debate over a policy response is often framed as a choice of acting now or waiting until the uncertainty is reduced. Implicit in the "wait to learn" argument is the notion that the ability to learn in the future necessarily implies that less restrictive policies should be chosen in the near term. I demonstrate in the general case that the ability to learn in the future can lead to either less restrictive or more restrictive policies today. I also show that the initial decision made under uncertainty will be affected by future learning only if the actions taken today change the marginal costs or marginal damages in the future. Results from an intermediate-scale integrated model of climate and economics indicate that the choice of current emissions restrictions is independent of whether or not uncertainty is resolved before future decisions, because, like most models, the cross-period interactions are minimal. With stronger interactions, the effect of learning on initial period decisions can be more important.

Expanded production of natural gas liquids (NGLs, ethane, propane, butanes) in the United States may continue to be utilized in domestic and international chemical manufacturing; however, this would require the development of expanded transport infrastructures. Electricity generation is an alternative use for NGLs. As a case study of the value of NGLsin electricity generation, the potential use of NGLsin the Texas electrical grid was evaluated. It was assumed that electricity would be generated in 25‐MWgas turbines, located at gas‐processing plants where NGLsin produced gas are separated to create pipeline‐quality gas. The potential magnitude of NGL‐based power generation in the Texas grid, using NGLsproduced in the Eagle Ford and Barnett oil and gas production regions in Texas, was estimated to be up to 2.7 GWof electricity, 6% of average demand on the grid. The NGL‐based electricity generation displaced coal and natural gas‐based generation, with little impact on the use of renewables. At the assumed efficiency (34.7%) and nonfuel operating cost ($7/MWh) of small gas turbines, NGLsare only fully utilized for electricity generation in the Texas grid when fuel prices are lower than $1.50 per million BTU(a discount of $0.90 per million BTUrelative to the fuel price of coal assumed in the modeling). Higher prices might be supported if NGLsare used for electricity generation at times of high demand or if larger, higher efficiency turbines are utilized. WIREs Energy Environ2018, 7:e258. doi: 10.1002/wene.258 This article is categorized under: Fossil Fuels > Systems and InfrastructureEnergy Infrastructure > Systems and Infrastructure Electricity generation, as an alternative to chemical manufacturing, for the use of natural gas liquids is assessed