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This paper investigates the strategic pricing behavior of a leader in a two-person, noncooperative game when profits depend on the purchasing response of a follower who not only reacts to changing prices instantaneously, but also builds long-lived consumption infrastructure that affects future demand. As an application of such a system, we formulate the relationship between these players in the natural gas and electricity generation industries as a bilevel problem. The leader is a natural gas producer whose objective is to maximize profit; the follower is an electric utility who solves a capacity expansion and dispatch problem with the objective of minimizing the cost of electricity generation and long-run investments. To find solutions, the bilevel problem is reformulated as a mixed-integer linear program by replacing the lower-level player's model with its Karush–Kuhn–Tucker conditions, which are necessary and sufficient for optimality here, and linearizing the upper-level player's objective function using the strong duality conditions of the lower-level problem. After parameterizing the model with publicly available data for Texas, we conduct scenario analyses through 2040, evaluating strategies of the natural gas producer under different policies regarding carbon taxes and incentives for carbon capture and storage (CCS). We then observe how the lower-level player responds to these strategies in terms of the evolution of the generation mix, added capacity, and CO 2 emissions. We also quantify the effects of strategic pricing by running scenarios where natural gas prices are fixed. Key findings include: (1) different levels of carbon tax and CCS incentive can have non-monotonic effects on the optimal natural gas price and producer profit, (2) effects of CCS incentives can spill over to technologies without CCS, and (3) omission of strategic pricing from the model not only decreases the profit for the producer but also can increase the costs for the electricity sector. • Bilevel model for interaction between natural gas producer and electric utility. • Carbon policy effects on electricity capacity mix, generation mix, and CO 2 emissions. • Effectiveness of different reformulations of bilevel programming problems. • Demonstration of how strategic natural gas pricing affects carbon policy outcomes. [ABSTRACT FROM AUTHOR]

Accurate forecasting of peak electricity load has long been an active area of research in electricity markets, and power systems planning and operation. Unanticipated climate-induced surges in peak load can lead to supply shortages causing frequent brownouts and blackouts, and large-scale socioeconomic impacts. In this paper, the climate sensitivity of daily peak load is characterized by leveraging advanced statistical machine learning algorithms. More specifically, a rigorously tested and validated predictive model based on the Bayesian additive regression trees algorithm is proposed. Results from this study revealed that maximum daily temperature followed by mean dew point temperature are the most important predictors of the climate-sensitive portion of daily peak load. Among the non-climatic predictors, electricity price was found to have a strong positive association with the daily peak load. Economic growth was observed to have an inverse association with the daily peak load. While the proposed framework is established for the state of Texas, one of the most energy-intensive states with geographic and demographic susceptibility to climatic change, the methodology can be extended to other states/regions. The model can also be used to make short-term predictions of the climate-sensitive portion of daily peak load. • Maximum temperature is the most important predictor of climate-sensitive peak load. • Dew point temperature significantly influences peak load both during summer and winter. • Maximum sustained wind speed lowers daily peak load. • Electricity price has a strong positive association with peak load. • Economic growth of the state is inversely associated with peak load. [ABSTRACT FROM AUTHOR]

Growth of electricity generation from variable renewable resources like wind and solar has raised questions about future grid stability. This paper used several renewable energy penetration scenarios to determine when an electric grid might be more vulnerable to frequency contingencies, such as a generator outage. Unit commitment and dispatch modeling was used to quantify system inertia, an established proxy for grid stability. A case study of the Electric Reliability Council of Texas grid was used to illustrate the method. Results from the modeled scenarios showed that the Texas grid is resilient to major grid changes, even with relatively high penetrations (∼ 30% of annual energy generation compared to 18% in 2017) of renewable energy. However, retiring nuclear power plants and private-use networks in the model led to unstable inertia levels in our results. When the system inertia was constrained to meet a minimum threshold in our model, multiple coal and natural gas combined-cycle plants were dispatched at part-load or at their minimum operating level to maintain stable system inertia levels. This behavior is expected to expand with higher renewable energy penetrations and could occur on other electric grids that are reliant on synchronous generators for inertia support. [ABSTRACT FROM AUTHOR]

Operating costs and market rules are likely to have an impact on the rewards of participating in a Vehicle to Grid system. This paper investigates these impacts by developing a model of a centralized Vehicle to Grid system and applying it to the 2015 wholesale electricity market in Texas (Houston Hub) for selling energy and capacity. Three scenarios are examined. In the first scenario, electric vehicles are paid based on a fixed retail market price; in the second, they are paid a time-varying retail market price; in the third, the virtual power plant shares 50% of its total reward with the participating vehicles. The results demonstrate that, while this system is always financially profitable to the virtual power plant and the system operator gets grid services, the electric vehicles could lose money. Further, results show that these vehicles with lower per unit output-battery cost could lose more money because of extensive battery over-use and insufficient reward at current market prices. Lower battery costs, subsidies for participation in this system, and more rewarding market products could all make their participation more economically viable. [ABSTRACT FROM AUTHOR]

Replacing legacy high carbon technologies (LHCTs) with new low carbon technologies (NLCTs), particularly NLCTs featuring variable renewable resources, can disproportionally influence the costs associated with maintaining a reliable and competent grid system. Traditional cost metrics and publicly available tools that make use of these metrics fail to capture the total systems cost (TSC) implications of such changes, highlighting the need for a tool that will capture these implications. This paper presents the results of a tool developed to estimate the System Cost of Replacement Energy (SCoRE) when NLCTs replace LHCTs in an operating region. The SCoRE estimates potential changes in the TSC resulting from the replacement. The SCoRE tool is applied to the Electric Reliability Council of Texas (ERCOT) operating region. LHCTs, namely coal- and natural gas-fired power plants are replaced by NLCTs including solar photovoltaics and wind, with and without energy storage, and coal and natural gas power plants equipped with carbon capture and storage (CCS). The results showcase the TSC implications of different replacement scenarios, which for ERCOT include larger increases in the TSC if LHCTs are replaced with wind and solar, in lieu of natural gas power plants equipped with CCS, for all decarbonization targets considered. • Technology transformations to achieve decarbonization will impact system costs. • SCoRE tool estimates system costs implications of LHCT being replaced with NLCT. • Wind and NGCC + CCS is the least cost pathway for 90% decarbonization in ERCOT. • Renewable energy curtailment appears cheaper than co-locating it with energy storage. • Solar PV + Battery storage become impractical at >70% decarbonization. [ABSTRACT FROM AUTHOR]

With the shift in renewable portfolio standards, conventional fossil-fuel based generators are expected to be partially or fully replaced with renewable energy sources such as photovoltaics (PVs). However, replacing the conventional synchronous generators with PV generators may raise new stability concerns such as lack of inertia, insufficient reactive power and voltage fluctuations, which can jeopardize the reliability of the entire network. In this paper, various impacts of integrating utility-scale PVs and rooftop PVs and replacing the existing conventional generators on the stability of a power network are investigated. Detailed steady state, transient and small signal analyses are conducted on a large test system, namely Texas 2000-bus system for different PV penetration levels. System transients such as line faults, double-line faults, loss of generator, loss of PVs and cloud cover scenarios are simulated to study their impacts on the test system with different PV penetration levels. It is demonstrated from the impact analysis that the benefits of PVs are highly dependent on a spectrum of factors such as node criticality, type, location and penetration of PVs, and type of transients. Furthermore, the influence of battery energy storage system that are generally associated with PVs on the system stability is also discussed. • Modeling and analysis of power network with different solar penetration levels. • Voltage stability analysis of grid with solar determine the most sensitive buses. • Transient and small-signal stability analysis to analyze the impacts of solar. • Analysis with storage devices to determine their influence in network damping. [ABSTRACT FROM AUTHOR]