Search

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. Keywords: Electricity markets, Game theory, Technology adoption. Equilibrium framework, Environmental regulations, 1. INTRODUCTION The electric power sector is in the process of a major transition in its composition of fuels, technologies, and regulatory environment, and this transition is likely to accelerate [...]

Electricity generated by fossil fuels is dispatchable, meaning that generators can be turned on when needed. By contrast, renewable energy tends to be intermittent because of the variability of natural sources such as wind and sunlight. New approaches are needed to solve the challenges to electricity systems posed by the growing share of variable renewable energy (VRE) in these systems. Specifically, how should the physical power system and markets for electricity be structured to deliver electricity at low cost and reflect consumers' preferences for reliability? Current power systems have largely achieved these two goals through a competitive market for generation based on marginal cost pricing and through mandated overcapacity to ensure 100 percent reliability to consumers. Decarbonization-induced increases in the share of generation from nondispatchable VRE create operational challenges: ensuring 100 percent reliability in a VRE-dominated system will be costly and inefficient. Preferences for reliability are heterogeneous, as some consumers will insist on 100 percent reliable energy, whereas others may be willing to forgo reliability for lower cost. In this article, we discuss ways to design a power system and electricity market that can address this inefficiency. [ABSTRACT FROM AUTHOR]

Stochastic programming models are large-scale optimization problems that are used to facilitate decision making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current [...]

Generation Expansion Planning (GEP) can inform regulation, electricity market design, and regional system planning by identifying adaptive investment strategies. Relevant uncertainties include hourly variability in load and renewable generation and decadal-scale uncertainty in technology, markets, and regulation. A multi-stage and multi-scale stochastic GEP model that represents these uncertainties at sufficient resolution becomes intractable. We present an approach for representing this multi-scale uncertainty, and compare it to existing methods, applied to a two-stage stochastic GEP model with a cumulative carbon emission target. For long-term uncertainty, we compare partitioning methods, which reduce the number of decision variables but retain all scenarios, to representative scenario methods, which retain only a subset of the original scenarios. For short-term uncertainty, we compare methods that select representative weeks based on distance metrics in the parameter space to methods that use the covariance of outcomes across feasible decisions to select weeks. We find that scenario reduction methods struggle to find the appropriate investment levels for variable renewable generation and consequently produce more costly plans than scenario partitioning methods. While simple approximating methods perform well with larger models, covariance-based approximations have the best performance overall. [ABSTRACT FROM AUTHOR]

The rapid increase of renewable generation and its anticipated continued growth requires greater operational flexibility in modern power systems. Previous studies explored the addition of more flexible resources to improve system flexibility, but this may also be achieved at lower cost by enhancing existing power plants via flexibility retrofits. Potential flexibility upgrades to existing thermal plants include modifications that enable faster ramp rates, increase maximum load levels, decrease minimum load levels, and provide faster and lower cost startup operations. In this paper, we focus on improvements to existing natural gas combined cycle generators, and apply a unit commitment model to analyze the impact that each specific upgrade has on their operational dynamics and profitability from both the system perspective and from the asset owner perspective. We show that the value of each type of flexibility improvement varies depending on the capacity factor and usage pattern of the generator before the upgrade, and that the relative benefit of each type of flexibility varies. We demonstrate diminishing returns to increased flexibility when upgrading multiple generators simultaneously. Sensitivity tests for several shares of renewable generation, different generation mixes and different net load realizations confirm the generality of the results shown. [ABSTRACT FROM AUTHOR]

One major difficulty in transmission expansion planning is selecting the representative scenarios to use to evaluate candidate transmission networks. The variability in demand and renewable generation makes the inclusion of several scenarios critical when calculating reliability and cost, but including too many scenarios in an optimization is computationally intractable. To reduce the number of representative operating conditions needed to obtain an accurate approximation of the system, we propose a method rooted in multivariate statistics that exploits the latent correlative structure between different scenarios and network configurations. The proposed algorithm includes an objective and rigorous way to select a subset of scenarios that provide as much information about the system as possible, and a method to accurately approximate the system cost from that scenario subset. The result is a set of scenarios and weights that are easily incorporated into traditional transmission expansion planning formulations. We apply this to a 312-bus WECC model with 8,736 distinct operating conditions. The transmission plans found with the proposed method are more reliable and have a lower total cost than those from other scenario reduction techniques, as well as a smaller error between the expected system performance from the optimization objective and the actual system performance. [ABSTRACT FROM AUTHOR]

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 NGLs in electricity generation, the potential use of NGLs in the Texas electrical grid was evaluated. It was assumed that electricity would be generated in 25-MW gas turbines, located at gasprocessing plants where NGLs in produced gas are separated to create pipelinequality gas. The potential magnitude of NGL-based power generation in the Texas grid, using NGLs produced in the Eagle Ford and Barnett oil and gas production regions in Texas, was estimated to be up to 2.7 GW of 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, NGLs are 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 BTU relative to the fuel price of coal assumed in the modeling). Higher prices might be supported if NGLs are used for electricity generation at times of high demand or if larger, higher efficiency turbines are utilized. [ABSTRACT FROM AUTHOR]

Recent work on operational flexibility—a power system’s ability to respond to variations in demand and supply—has focused on the impact of large penetration of renewable generation on existing power systems. Operational flexibility is equally important for long-term capacity expansion planning. Future systems with larger shares of renewable generation, and/or carbon emission limits, will require flexible generation mixes; yet, flexibility is rarely fully considered in capacity planning models because of the computational demands of including mixed integer unit commitment within capacity expansion. We present a computationally efficient unit commitment/maintenance/capacity planning formulation that includes the critical operating constraints. An example of capacity planning for a Texas-like system in 2035 with hypothetical RPS and carbon policies shows how considering flexibility results in different capacity and energy mixes and emissions, and that the omission of flexibility can lead to a system that is unable to simultaneously meet demand, carbon, and RPS requirements. [ABSTRACT FROM PUBLISHER]

Previous studies of market power within a regional system have considered multiple competing generation operators and the role of transmission constraints. However, these studies typically assume simplified structures in which each operator is restricted to a unique node in a transmission constrained network. Real systems typically have operators making decisions for units in several zones at once. Past studies also implicitly treated market power as a static concept for a given set of market rules and network configuration. We demonstrate that market power is dynamic, and can vary significantly with fuel prices and even with large-scale weather patterns. We also demonstrate the impact on region-wide market power of operators that manage units in multiple zones. We use the Electric Reliability Council of Texas (ERCOT) as an illustrative case study, and apply a conjectured supply function equilibrium (CSFE) approach that accounts for transmission constraints. We show that the companies with greater influence on the market price will depend on the relative prices of coal and natural gas. We also show that a weather event, such as a period without any wind, can have a substantial impact on market power. [ABSTRACT FROM AUTHOR]

The cycling regime of thermal power plants significantly increases in the presence of intermittent renewables, increasing fuel and operation and maintenance costs from startups. Some regional electricity market operators adopt complex bidding mechanisms to account for nonconvex cost components that are not reflected in the marginal cost of energy, while other markets rely solely on simple bids with revenue sufficiency conditions. This paper compares the impacts of different bidding rules on wholesale prices and on the remuneration of units in power systems with a significant share of renewable generation. We distinguish the effects of bidding rules from the effects of regulatory uncertainty that can unexpectedly increase renewable generation by considering two distinct situations: 1) an “adapted” capacity mix, which is optimized for any given amount of renewable penetration, and 2) a “nonadapted” capacity mix, which is optimized for zero renewable penetration, but operated with different nonzero levels of renewables. We show that, although in the transitory state the impact on remuneration of having a nonadapted system dominates over the effect of the startup remuneration mechanisms explored, in equilibrium, bidding rules play an important role in making power plants whole while impacting on the cost borne by consumers. [ABSTRACT FROM PUBLISHER]

Designing future capacity mixes with adequate flexibility requires capturing operating constraints through an embedded unit commitment approximation. Despite significant recent improvements, such simulations still require significant computation times. Here we propose a method, based on clustering units, for approximate unit commitment with dramatic improvements in solution time. This method speeds computation by aggregating similar but non-identical units. This replaces large numbers of binary commitment variables with fewer integers while still capturing individual unit decisions and constraints. We demonstrate the trade-off between accuracy and run-time for different levels of aggregation. A numeric example using an ERCOT-based 205-unit system illustrates that careful aggregation introduces errors of 0.05%–0.9% across several metrics while providing several orders of magnitude faster solution times (400\,\times ) compared to traditional binary formulations. Further aggregation increases errors slightly (\sim 2\times ) with further speedup (2000\,\times ). We also compare other simplifications that can provide an additional order of magnitude speed-up for some problems. [ABSTRACT FROM PUBLISHER]

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 CO2 emissions would probably increase water withdrawals for electricity generation in the absence of limits on water usage, and that simultaneous restriction of CO2 emissions and water withdrawals requires a different mix of energy technologies and higher costs than one would plan to reduce either CO2 or water alone. [ABSTRACT FROM AUTHOR]