publications
If you are unable to access any of these papers, please contact me at rmoglen@epri.com
2024
- Rachel Moglen, Benjamin D. Leibowicz, Alexis Kwasinski, and Grant CruseSocio-Economic Planning Sciences 2024
After a disaster, the power grid helps support infrastructure systems that are essential to the recovery effort in addition to the critical services it provides every day. This paper provides an approach for optimal power grid restoration in disaster contexts with environmental hazards. These hazards, such as unstable structures, smoke, chemical hazards, and abnormal radioactivity, may pose acute and accumulating risks to repair workers and impede the restoration process. We therefore formulate a mixed-integer linear program (MILP) that generates an optimal restoration plan with constraints imposed by acute and accumulating environmental hazards. We also develop a heuristic inspired by trends that we observe in optimal restoration strategies, and we compare its performance to that of an optimal restoration strategy. For our case study, we model a stylized disaster that approximates the patterns of a number of disasters including earthquakes, fires, industrial facility explosions, or nuclear reactor incidents. We analyze the performance of the heuristic and optimal restoration strategies on a modified IEEE 123-bus test network. We find that the optimal restoration strategy is able to restore power service more quickly than the heuristic strategy while also exposing repair workers to less acute and cumulative environmental hazards. We also find that as disaster severity increases, the performance difference between the heuristic and optimal restoration strategies grows. Finally, our results show that both the optimal and heuristic algorithms can be useful tools for identifying vulnerable regions of a power grid.
@article{Moglen2024_PowerRestoration, title = {Optimal restoration of power infrastructure following a disaster with environmental hazards}, journal = {Socio-Economic Planning Sciences}, volume = {95}, pages = {101974}, year = {2024}, issn = {0038-0121}, doi = {https://doi.org/10.1016/j.seps.2024.101974}, url = {https://www.sciencedirect.com/science/article/pii/S0038012124001733}, author = {Moglen, Rachel and Leibowicz, Benjamin D. and Kwasinski, Alexis and Cruse, Grant}, keywords = {Disaster recovery, Restoration, Optimization, Power systems, Resilience}, }
2023
- Rachel Moglen, Kiran Prakash Chawla, Patricia Levi, Yinong Sun, Oladoyin Phillips, Benjamin D. Leibowicz, Jesse D. Jenkins, and Emily A. GrubertiScience 2023
The growing field of macro-energy systems (MES) brings together the interdisciplinary community of researchers studying the equitable and low-carbon future of humanity’s energy systems. As MES matures as a community of scholars, a coherent consensus about the key challenges and future directions of the field can be lacking. This paper is a response to this need. In this paper, we first discuss the primary critiques of model-based MES research that have emerged since MES was proposed as a way to unify related interdisciplinary research. We discuss these critiques and current efforts to address them by the coalescing MES community. We then outline future directions for growth motivated by these critiques. These research priorities include both best practices for the community and methodological improvements.
@article{Moglen2023_MESCritiquesFrontiers, title = {The State of Macro-Energy Systems Research: Common Critiques, Current Progress, and Research Priorities}, journal = {iScience}, author = {Moglen, Rachel and Chawla, Kiran Prakash and Levi, Patricia and Sun, Yinong and Phillips, Oladoyin and Leibowicz, Benjamin D. and Jenkins, Jesse D. and Grubert, Emily A.}, pages = {106325}, year = {2023}, issn = {2589-0042}, doi = {https://doi.org/10.1016/j.isci.2023.106325}, url = {https://www.sciencedirect.com/science/article/pii/S2589004223004029}, keywords = {Macro-energy systems, Energy and climate policy, Decarbonization, Energy modeling, Climate change}, } - Rachel Moglen, Julius Barth, Shagun Gupta, Eiji Kawai, Katherine Klise, and Benjamin D. LeibowiczReliability Engineering & System Safety 2023
Natural disasters pose serious threats to Critical Infrastructure (CI) systems like power and drinking water, sometimes disrupting service for days, weeks, or months. Decision makers can mitigate this risk by hardening CI systems through actions like burying power lines and installing backup generation for water pumping. However, the inherent uncertainty in natural disasters coupled with the high costs of hardening activities make disaster planning a challenging task. We develop a disaster planning framework that recommends asset-specific hardening projects across interdependent power and water networks facing the uncertainty of natural disasters. We demonstrate the utility of our model by applying it to Guayama, Puerto Rico, focusing on the risk posed by hurricanes. Our results show that our proposed optimization approach identifies hardening decisions that maintain a high level of service post-disaster. The results also emphasize power system hardening due to the dependency of the water system on power for water treatment and a higher vulnerability of the power network to hurricane damage. Finally, choosing optimal hardening decisions by hedging with respect to all potential hurricane scenarios and their probabilities produces results that perform better on extreme events and are less variable compared to optimizing for only the average hurricane scenario.
@article{Moglen2023_NexusResilience, title = {A Nexus Approach to Infrastructure Resilience Planning under Uncertainty}, journal = {Reliability Engineering & System Safety}, volume = {230}, pages = {108931}, year = {2023}, issn = {0951-8320}, doi = {https://doi.org/10.1016/j.ress.2022.108931}, url = {https://www.sciencedirect.com/science/article/pii/S0951832022005464}, author = {Moglen, Rachel and Barth, Julius and Gupta, Shagun and Kawai, Eiji and Klise, Katherine and Leibowicz, Benjamin D.}, keywords = {Infrastructure hardening, Resilience, Stochastic optimization, Disaster planning, Critical infrastructure systems, Energy-water nexus}, }
2022
- Katherine Klise, Rachel Moglen, Joseph Hogge, Daniel Eisenberg, and Terranna HaxtonJournal of Water Resources Planning and Management Dec 2022
The two Category-5 hurricanes that impacted the United States Virgin Islands in 2017 exposed critical infrastructure vulnerabilities that must be addressed. While the drinking water utility has first-hand knowledge about how the hurricanes affected their systems, the use of modeling and simulation tools can provide additional insight to aid investment planning and preparedness. This paper provides a case study on resilience analysis for the island’s potable water systems subject to long term power outages. Power outage scenarios help quantify differences in water delivery, water quality, and water quantity during and after the disruption. The analysis helps illustrate important differences in system operations and recovery time across the islands. Results from this case study can be used to better understand how the system might behave during future disruptions, provide justification for investment, and provide recommendations to increase resilience of the system. The analysis framework can also be used by other utilities to explore vulnerability to long term power outages.
@article{Klise2022_USVIResilience, author = {Klise, Katherine and Moglen, Rachel and Hogge, Joseph and Eisenberg, Daniel and Haxton, Terranna}, title = {Resilience Analysis of Potable Water Service after Power Outages in the U.S. Virgin Islands}, journal = {Journal of Water Resources Planning and Management}, volume = {148}, issue_date = {December 2022}, number = {12}, pages = {05022010}, year = {2022}, month = dec, doi = {10.1061/(ASCE)WR.1943-5452.0001607}, url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29WR.1943-5452.0001607}, }
2020
- Pattanun Chanpiwat, Steven Gabriel, Rachel Moglen, and Michael SiemannASME Journal of Engineering for Sustainable Buildings and Cities Feb 2020
This paper develops means to analyze and cluster residential households into homogeneous groups based on the electricity load. Classifying customers by electricity load profiles is a top priority for retail electric providers (REPs), so they can plan and conduct demand response (DR) effectively. We present a practical method to identify the most DR-profitable customer groups as opposed to tailoring DR programs for each separate household, which may be computationally prohibitive. Electricity load data of 10,000 residential households from 2017 located in Texas was used. The study proposed the clustered load-profile method (CLPM) to classify residential customers based on their electricity load profiles in combination with a dynamic program for DR scheduling to optimize DR profits. The main conclusions are that the proposed approach has an average 2.3% profitability improvement over a business-as-usual heuristic. In addition, the proposed method on average is approximately 70 times faster than running the DR dynamic programming separately for each household. Thus, our method not only is an important application to provide computational business insights for REPs and other power market participants but also enhances resilience for power grid with an advanced DR scheduling tool.
@article{Chanpiwat2020_DemandClustering, author = {Chanpiwat, Pattanun and Gabriel, Steven and Moglen, Rachel and Siemann, Michael}, title = {{Using Cluster Analysis and Dynamic Programming for Demand Response Applied to Electricity Load in Residential Homes}}, journal = {ASME Journal of Engineering for Sustainable Buildings and Cities}, volume = {1}, number = {1}, year = {2020}, month = feb, issn = {2642-6641}, doi = {10.1115/1.4045704}, url = {https://doi.org/10.1115/1.4045704}, note = {011006}, } - Rachel Moglen, Pattanun Chanpiwat, Steven Gabriel, and Andrew BlohmEnergy Systems Aug 2020
In the face of the considerable volatility in electricity market prices, retail electric providers (REPs) occupy the difficult position between the utility and the end-user. They purchase electricity either through long-term contracts, in the day-ahead or spot markets, or self-generate. This electricity is then sold to the end-user at a fixed price. Thus, REPs bear the financial burden of electricity price uncertainty. In a market where prices can increase by orders of magnitude in as little as 15 minutes, single spikes have driven some REPs to bankruptcy. To mitigate the negative effects of market volatility, REPs can employ demand response (DR), shifting consumers’ electricity load from periods of high prices to lower-priced time periods. While DR comes in many forms, we focus on thermostatically-controlled residential DR, made possible through internet-connected thermostats. The timing of residential heating and cooling load makes it a particularly financially advantageous target for DR. We present a dynamic programming (DP) formulation designed to optimally schedule DR events to maximize their savings for the REP. We also consider the effects of price uncertainty on the savings through the use of stochastic dynamic programming (SDP). Our case study examines the profitability of thermostatically-controlled DR, specifically of air-conditioning (A/C) load, in the Electricity Reliability Council of Texas (ERCOT). We find that optimal management of thermostatically-controlled DR can generate significant savings to the REP, potentially improving their profit margins as much as 25%. However, we also find that few events generate most savings, necessitating precise scheduling facilitated by our formulation. Electricity price uncertainty marginally reduces these expected savings, but optimally-scheduled DR still offers REPs an effective method of mitigating risk.
@article{Moglen2020_DemandResponse, title = {Optimal Thermostatically-Controlled Residential Demand Response for Retail Electric Providers}, author = {Moglen, Rachel and Chanpiwat, Pattanun and Gabriel, Steven and Blohm, Andrew}, journal = {Energy Systems}, pages = {1--21}, year = {2020}, month = aug, publisher = {Springer}, doi = {10.1007/s12667-020-00400-0}, url = {https://doi.org/10.1007/s12667-020-00400-0}, }
2019
- Rachel MoglenJun 2019
Electricity price volatility in the Electricity Reliability Council of Texas (ERCOT) poses significant financial threat to many players in the electricity market, though most of the financial burden falls on the retail electric providers (REPs). REPs are contractually obligated to purchase and provide all electricity that the end-user wishes to consume, even when the purchase of this electricity brings them financial losses. Electricity prices in ERCOT can increase from typical ranges (30/MWh) to 3,000/MWh in as little as 15 minutes, causing REPs to seek mitigation techniques to avoid paying price spike prices for electricity. We explore two techniques for REPs to schedule mitigation techniques: a price prediction-based heuristic approach, as well as an optimal scheduling algorithm using dynamic programming (DP). We aim to optimally schedule these mitigation techniques which shift load from high price periods to times of lower prices, called demand response (DR). To achieve this load shifting, REPs remotely manipulate internet-connected thermostats of residential customers, thereby controlling a fraction of residential HVAC load. We found that the price prediction approach was highly unreliable, even for predicting prices as near as 5 minutes out. We therefore chose to rely on the DP as the primary scheduling model. By applying the DP deterministically to historical electricity price and weather data, the load-shifting technique is shown to potentially improve REP profit margins by 10% to 25% per customer annually. Most of these savings come from a few crucial events, highlighting the usefulness of the DP and the importance of accuracy in the timing of DR events. Due to the uncertainty in electricity prices, we apply a multi-objective approach considering the REP’s conflicting objectives: maximizing savings and minimizing financial risk. Results from this multi-objective formulation point to shorter duration DR events in the evening being the least risky, with additional savings possible through riskier short midday events. To ensure that REPs could apply our DP formulation for use in near real-time decision-making applications, the computation speed was verified to be under one second for 24 stages (i.e., 1-hour intervals for one day.)
@mastersthesis{Moglen2019_DemandResponse, title = {Optimal Scheduling of Residential Demand Response Using Dynamic Programming}, author = {Moglen, Rachel}, school = {University of Maryland, College Park}, year = {2019}, month = jun, doi = {https://doi.org/10.13016/ldoz-wwby}, }
2018
- Glenn Moglen, Richard McCuen, and Rachel MoglenJournal of Hydrologic Engineering Aug 2018
A proposed quantification of the fundamental concepts in the Natural Resources Conservation Service (NRCS) rainfall-runoff relation is examined to determine changes relevant to peak discharge estimation and drainage design. Changes to the NRCS curve number, storage, and initial abstraction relations result in different estimates of the runoff volume, timing of runoff, and peak discharge produced by long-established empirical equations that quantify event-based runoff. The ratio of existing-to-proposed runoff depth estimates is determined across a range of curve numbers and storm depths. Similarly, the ratio of existing to proposed peak discharge is determined as a function of curve number, storm depth, and design storm within the NRCS TR-55 model. Using an infrastructure design based on the existing equations as the baseline, that infrastructure is underdesigned (overdesigned) if the proposed NRCS equations produce larger (smaller) estimates of volumes or discharges than the existing equations for the same watershed conditions. This study shows that the proposed NRCS equations more likely identify existing infrastructure as being underdesigned for smaller storm events and lower curve numbers. Conditions and design storm produce large variation in the design ratios. Storms with return periods on the order of 2–10 years generally result in peak discharge underdesign ratios that span from 1 to as high as 2 or 3 depending on the watershed conditions, storm size, curve number, and design element in question. Existing infrastructure overdesign is more likely the case when return periods approach 25, 50, or 100 years, with typical overdesign ratios ranging downward from 1.0 to as small as 0.8. In most cases, required storage volumes, as prescribed by the NRCS TR-55 approach, are found to be smaller using the proposed equations, leading to both cost savings and reduced pond residence times. Three common design problems are provided only to illustrate typical underdesign and overdesign findings. The central focus is on the potentially negative consequences of proposed changes to a limited aspect of curve number hydrology.
@article{Moglen2018_CurveNumber, author = {Moglen, Glenn and McCuen, Richard and Moglen, Rachel}, title = {Consequences of Changes to the NRCS Rainfall-Runoff Relations on Hydrologic Design}, journal = {Journal of Hydrologic Engineering}, volume = {23}, number = {8}, pages = {04018032}, year = {2018}, month = aug, doi = {10.1061/(ASCE)HE.1943-5584.0001681}, url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0001681}, }