Dharik S. Mallapragada

Aron Brenner, Rahman Khorramfar, Dharik Mallapragada et al.

A rapid transformation of current electric power and natural gas (NG) infrastructure is imperative to meet the mid-century goal of CO2 emissions reduction requires. This necessitates a long-term planning of the joint power-NG system under representative demand and supply patterns, operational constraints, and policy considerations. Our work is motivated by the computational and practical challenges associated with solving the generation and transmission expansion problem (GTEP) for joint planning of power-NG systems. Specifically, we focus on efficiently extracting a set of representative days from power and NG data in respective networks and using this set to reduce the computational burden required to solve the GTEP. We propose a Graph Autoencoder for Multiple time resolution Energy Systems (GAMES) to capture the spatio-temporal demand patterns in interdependent networks and account for differences in the temporal resolution of available data. The resulting embeddings are used in a clustering algorithm to select representative days. We evaluate the effectiveness of our approach in solving a GTEP formulation calibrated for the joint power-NG system in New England. This formulation accounts for the physical interdependencies between power and NG systems, including the joint emissions constraint. Our results show that the set of representative days obtained from GAMES not only allows us to tractably solve the GTEP formulation, but also achieves a lower cost of implementing the joint planning decisions.

Marc Barbar, Dharik S. Mallapragada

Power sector capacity expansion models (CEMs) that are used for studying future low-carbon grid scenarios must incorporate detailed representation of grid operations. Often CEMs are formulated to model grid operations over representative periods that are sampled from the original input data using clustering algorithms. However, such representative period selection (RPS) methods are limited by the declining efficacy of the clustering algorithm with increasing dimensionality of the input data and do not consider the relative importance of input data variations on CEM outcomes. Here, we propose a RPS method that addresses these limitations by incorporating dimensionality reduction, accomplished via neural network based autoencoders, prior to clustering. Such dimensionality reduction not only improves the performance of the clustering algorithm, but also facilitates using additional features, such as estimated outputs produced from parallel solutions of simplified versions of the CEM for each disjoint period in the input data (e.g. 1 week). The impact of incorporating dimensionality reduction as part of RPS methods is quantified through the error in outcomes of the corresponding reduced-space CEM vs. the full space CEM. Extensive numerical experimentation across various networks and range of technology and policy scenarios establish the superiority of the dimensionality-reduction based RPS methods.

Michael Giovanniello, Dharik S. Mallapragada

Regulators and voluntary corporate sustainability efforts are increasingly adopting time-matching requirements (TMRs) for clean electricity procurement for large loads, such as data centers, and electricity-intensive fuel production, such as hydrogen. We use a stochastic capacity expansion model (CEM) framework to assess how inter-annual weather variability affects the cost, composition, and emissions of procurement-driven infrastructure to meet annual and hourly TMRs using the case study of a grid-connected hydrogen producer in Texas. Our approach, which relies on co-optimizing investments and hourly operations over nine weather scenarios, reveals that hourly TMR comes at a higher cost premium compared to annual TMR than previously estimated by single-scenario deterministic modeling, while emissions outcomes remain directionally consistent. Demand flexibility and partial hourly TMR (80-90%) lower the cost premium while preserving emissions benefits. We further examine how binding renewable portfolio standards (RPS) interact with TMR costs and emissions outcomes. When an RPS is applied to non-H2 electricity demand, annual TMR reduces emissions comparably to hourly TMR at a lower cost. Incorporating H2-related electricity demand directly into the RPS constraint, rather than imposing a separate TMR, achieves similar emissions outcomes at still lower cost, suggesting that TMR-based clean electricity procurement, particularly hourly matching, offers limited additional value in regions with stringent grid decarbonization policies.

Youssef Shaker, Jun Wen Law, Audun Botterud et al.

Policies focused on deep decarbonization of regional economies emphasize electricity sector decarbonization alongside electrification of end-uses. There is growing interest in utilizing hydrogen (H2) produced via electricity to displace fossil fuels in difficult-to-electrify sectors. One such case is heavy-duty vehicles (HDV), which represent a substantial and growing share of transport emissions as light-duty vehicles electrify. Here, we assess the bulk energy system impact of decarbonizing the HDV segment via either H2, or drop-in synthetic liquid fuels produced from H2 and CO2. Our analysis soft-links two modeling approaches: (a) a bottom-up transport demand model producing a variety of final energy demand scenarios for the same service demand and (b) a multi-sectoral capacity expansion model that co-optimizes power, H2 and CO2 supply chains under technological and policy constraints to meet exogenous final energy demands. Through a case study of Western Europe in 2040 under deep decarbonization constraints, we quantify the energy system implications of different levels of H2 and synthetic fuels adoption in the HDV sector under scenarios with and without CO2 sequestration. In the absence of CO2 storage, substitution of liquid fossil fuels in HDVs is essential to meet the deep decarbonization constraint across the modeled power, H2 and transport sectors. Additionally, utilizing H2 HDVs reduces decarbonization costs and fossil liquids demand, but could increase natural gas consumption. While H2 HDV adoption reduces the need for direct air capture (DAC), synthetic fuel adoption increases DAC investments and total system costs. The study highlights the trade-offs across transport decarbonization pathways, and underscores the importance of multi-sectoral consideration in decarbonization studies.

Aron Brenner, Rahman Khorramfar, Dharik Mallapragada et al.

Solving large-scale capacity expansion problems (CEPs) is central to cost-effective decarbonization of regional-scale energy systems. To ensure the intended outcomes of CEPs, modeling uncertainty due to weather-dependent variable renewable energy (VRE) supply and energy demand becomes crucially important. However, the resulting stochastic optimization models are often less computationally tractable than their deterministic counterparts. Here, we propose a learning-assisted approximate solution method to tractably solve two-stage stochastic CEPs. Our method identifies low-cost planning decisions by constructing and solving a sequence of tractable temporally aggregated surrogate problems. We adopt a Bayesian optimization approach to searching the space of time series aggregation hyperparameters and compute approximate solutions that minimize costs on a validation set of supply-demand projections. Importantly, we evaluate solved planning outcomes on a held-out set of test projections. We apply our approach to generation and transmission expansion planning for a joint power-gas system spanning New England. We show that our approach yields an estimated cost savings of up to 3.8% in comparison to benchmark time series aggregation approaches.