arXiv subjects

Ram Rajagopal

Publications and source records attributed to Ram Rajagopal.

At least 37 records · Page 2Linked to original sources

arXiv2024

EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure

To enable the electrification of transportation systems, it is important to understand how technologies such as grid storage, solar photovoltaic systems, and control strategies can aid the deployment of electric vehicle charging at scale. In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid. This python-based platform can run a receding horizon control scheme for real-time operation and a one-shot control scheme for planning problems, with multi-timescale dynamics for different systems to simulate realistic scenarios. We demonstrate the utility of EV-EcoSim through a case study focused on economic evaluation of battery size to reduce electricity costs while considering impacts of fast charging on the power distribution grid. We present qualitative and quantitative evaluations on the battery size in tabulated results. The tabulated results delineate the trade-offs between candidate battery sizing solutions, providing comprehensive insights for decision-making under uncertainty. Additionally, we demonstrate the implications of the battery controller model fidelity on the system costs and show that the fidelity of the battery controller can completely change decisions made when planning an electric vehicle charging site.

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arXiv2024

CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series

Urban transformations have profound societal impact on both individuals and communities at large. Accurately assessing these shifts is essential for understanding their underlying causes and ensuring sustainable urban planning. Traditional measurements often encounter constraints in spatial and temporal granularity, failing to capture real-time physical changes. While street view imagery, capturing the heartbeat of urban spaces from a pedestrian point of view, can add as a high-definition, up-to-date, and on-the-ground visual proxy of urban change. We curate the largest street view time series dataset to date, and propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. We demonstrate the effectiveness of our proposed method by benchmark comparisons with previous literature and implementing it at the city-wide level. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.

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arXiv2023

SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing

Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs). A key reason is that the large-scale, semantically diverse image-text dataset required for developing VLMs is still absent for remote sensing images. Unlike natural images, remote sensing images and their associated text descriptions cannot be efficiently collected from the public Internet at scale. In this work, we bridge this gap by using geo-coordinates to automatically connect open, unlabeled remote sensing images with rich semantics covered in OpenStreetMap, and thus construct SkyScript, a comprehensive vision-language dataset for remote sensing images, comprising 2.6 million image-text pairs covering 29K distinct semantic tags. With continual pre-training on this dataset, we obtain a VLM that surpasses baseline models with a 6.2% average accuracy gain in zero-shot scene classification across seven benchmark datasets. It also demonstrates the ability of zero-shot transfer for fine-grained object attribute classification and cross-modal retrieval. We hope this dataset can support the advancement of VLMs for various multi-modal tasks in remote sensing, such as open-vocabulary classification, retrieval, captioning, and text-to-image synthesis.

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arXiv2023

Improving the Load Flexibility of Stratified Electric Water Heaters: Design and Experimental Validation of MPC Strategies

Residential electric water heaters have significant load shifting capabilities due to their thermal heat capacity and large energy consumption. Model predictive control (MPC) has been shown to be an effective control strategy to enable water heater load shifting in home energy management systems. In this work, we analyze how modeling tank stratification in an MPC formulation impacts control performance for stratified electric water heaters under time-of-use (TOU) rates. Specifically, we propose an MPC formulation based on a three-node thermal model that captures tank stratification, and compare it to a one-node formulation that does not capture stratification and a standard thermostatic controller. These strategies are compared through both real-time laboratory testing and simulation-based evaluation for different water use patterns. Laboratory experiments show cost reductions of 12.3-23.2% for the one-node MPC and 31.2-42.5% for the three-node MPC relative to the thermostatic controller. The performance of the one-node MPC is limited by significant plant-model mismatch, while the three-node formulation better approximates real-world dynamics and results in much more effective cost reduction and load shifting. A simple analysis of how each strategy performs under water use forecast errors is also provided.

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arXiv2023

Equitable dynamic electricity pricing via implicitly constrained dual and subgradient methods

Coordination of distributed energy resources is critical for electricity grid management. Although nodal pricing schemes can mitigate congestion and voltage deviations, the resulting prices are not necessarily equitable. In this work, we leverage market mechanisms for DER coordination and propose a daily dynamic nodal pricing scheme that incorporates equity. We introduce a pricing "oracle," which we call the Power Distribution Authority, that sets equitable prices to manage the grid. We present two algorithms for executing this scheme and show that both methods are able to set prices that satisfy both voltage and equity constraints. Both proposed algorithms also outperform the common utility time-of-use pricing schemes by at least 45%. New market mechanisms are needed as the grid is transforming, and power system operators may leverage these methods for pricing electricity in a grid-aware, equitable fashion.

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arXiv2023

Empirical Exploration of Zone-by-zone Energy Flexibility: a Non-intrusive Load Disaggregation Approach for Commercial Buildings

Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock the energy flexibility potential of central air conditioning systems in complex commercial buildings. However, most existing studies focused on quantifying the energy flexibility on the building level since only building-level energy consumption is normally metered in commercial buildings. This study aims to investigate the impacts of temperature set-point adjustment strategies on zone-level thermal and energy performance by developing a non-intrusive data-driven load disaggregation method (i.e., a virtual zonal power meter). Three university buildings in Northern California were selected to test the proposed load disaggregation method. We found that heterogeneities of energy use and energy flexibility existed across not only buildings but also air handling units (AHUs) and zones. Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones. For the three tested buildings, the top 30% most energy-intensive zones accounted for around 60% of the total energy use; and the top 30% most energy-flexible zones provided around 80% of the total energy flexibility. The proposed method enables the electric grid or district energy system operators to regard the controlled energy-flexible entities as a fleet of AHUs or zones instead of a fleet of buildings and helps unlock the possibility for targeted demand flexibility strategies that balance zone-by-zone energy reduction with zone-by-zone costs to occupants.

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arXiv2023

Coordination of DERs for Grid Reliability via Day-ahead Demand-Supply Power Bounds

A previous study has shown that coordinating DERs to protect the distribution grid can significantly reduce the infrastructure upgrades needed to address future increases in DER and electrification penetrations. Implementing such coordination in the real world, however, is challenging due the temporal and spatial uncertainties about the loads and renewable generation, smart meter and network delays, incomplete information about the grid, different consumer objectives and privacy constraints, and scalability of the coordination scheme. This paper describes a day-ahead 2-layer DER coordination scheme that addresses these challenges. A global controller uses historical load data to compute day-ahead hourly demand upper and lower bounds for each consumer node. It then solves a largest volume axis-aligned box optimization problem to determine corresponding supply power bounds which if followed, ensures grid reliability. A local controller at each consumer node then determines the DER power injections which satisfy the consumer's objectives while obeying its supply bounds. Simulation results demonstrate, for example, that this scheme can capture 62% of the reduction in transformer violations achievable by the perfect-foresight centralized controller used in the aforementioned previous study.

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arXiv2023

Coordinating Distributed Energy Resources for Reliability can Significantly Reduce Future Distribution Grid Upgrades and Peak Load

Current DER coordination schemes, such as demand-response and VPPs, aim to reduce electricity costs during peak demand events with no consideration of distribution grid reliability. We show that coordinating DERs for grid reliability can significantly reduce both the infrastructure upgrades needed to support future increases in DER and electrification penetrations and peak load. Specifically, using a power flow driven simulation-optimization methodology, we compare the potential reliability improvements with a perfect-foresight centralized DER controller that minimizes reliability violations to a local controller that minimizes consumer electricity cost. We find, for example, that by 2050 with local control, on average 81% of the transformers in a distribution grid experience violations, compared to 28% with centralized control, which simultaneously reduces peak load by 17%. These reductions are achieved with only 5.1% increase in electricity cost. These findings suggest that future incentives for DER adoption should include reliability coordination.

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arXiv2023

Dynamic locational marginal emissions via implicit differentiation

Locational marginal emissions rates (LMEs) estimate the rate of change in emissions due to a small change in demand in a transmission network, and are an important metric for assessing the impact of various energy policies or interventions. In this work, we develop a new method for computing the LMEs of an electricity system via implicit differentiation. The method is model agnostic; it can compute LMEs for any convex optimization-based dispatch model, including some of the complex dispatch models employed by system operators in real electricity systems. In particular, this method lets us derive LMEs for dynamic dispatch models, i.e., models with temporal constraints such as ramping and storage. Using real data from the U.S. electricity system, we validate the proposed method against a state-of-the-art merit-order-based method and show that incorporating dynamic constraints improves model accuracy by 8.2%. Finally, we use simulations on a realistic 240-bus model of WECC to demonstrate the flexibility of the tool and the importance of incorporating dynamic constraints. Namely, static LMEs and dynamic LMEs exhibit a normalized average RMS deviation of 28.40%, implying dynamic constraints are essential to accurately modeling emissions rates.

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arXiv2023

Detecting Neighborhood Gentrification at Scale via Street-level Visual Data

Neighborhood gentrification plays a significant role in shaping the social and economic well-being of both individuals and communities at large. While some efforts have been made to detect gentrification in cities, existing approaches rely mainly on estimated measures from survey data, require substantial work of human labeling, and are limited in characterizing the neighborhood as a whole. We propose a novel approach to detecting neighborhood gentrification at a large-scale based on the physical appearance of neighborhoods by incorporating historical street-level visual data. We show the effectiveness of the proposed method by comparing results from our approach with gentrification measures from previous literature and case studies. Our approach has the potential to supplement existing indicators of gentrification and become a valid resource for urban researchers and policy makers.

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arXiv2022

Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data

Current methods to determine the energy efficiency of buildings require on-site visits of certified energy auditors which makes the process slow, costly, and geographically incomplete. To accelerate the identification of promising retrofit targets on a large scale, we propose to estimate building energy efficiency from widely available and remotely sensed data sources only, namely street view, aerial view, footprint, and satellite-borne land surface temperature (LST) data. After collecting data for almost 40,000 buildings in the United Kingdom, we combine these data sources by training multiple end-to-end deep learning models with the objective to classify buildings as energy efficient (EU rating A-D) or inefficient (EU rating E-G). After evaluating the trained models quantitatively as well as qualitatively, we extend our analysis by studying the predictive power of each data source in an ablation study. We find that the end-to-end deep learning model trained on all four data sources achieves a macro-averaged F1 score of 64.64% and outperforms the k-NN and SVM-based baseline models by 14.13 to 12.02 percentage points, respectively. Thus, this work shows the potential and complementary nature of remotely sensed data in predicting energy efficiency and opens up new opportunities for future work to integrate additional data sources.

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arXiv2022

Effect of State of Charge Uncertainty on Battery Energy Storage Systems

Battery energy storage systems (BESSs) provide many benefits to the electricity grid, including stability, backup power, and flexibility in introducing more clean energy sources. As BESS penetration grows, knowledge of the uncertainty in the battery's state of charge (SOC) estimate is crucial for planning optimal BESS power injection trajectories. This paper proposes a framework for quantifying SOC estimation uncertainty based on battery rest periods. An uncertainty analysis is presented for a BESS participating in the frequency regulation market.

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arXiv2022

Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation

The rapid expansion of Advanced Meter Infrastructure (AMI) has dramatically altered the energy information landscape. However, our ability to use this information to generate actionable insights about residential electricity demand remains limited. In this research, we propose and test a new framework for understanding residential electricity demand by using a dynamic energy lifestyles approach that is iterative and highly extensible. To obtain energy lifestyles, we develop a novel approach that applies Latent Dirichlet Allocation (LDA), a method commonly used for inferring the latent topical structure of text data, to extract a series of latent household energy attributes. By doing so, we provide a new perspective on household electricity consumption where each household is characterized by a mixture of energy attributes that form the building blocks for identifying a sparse collection of energy lifestyles. We examine this approach by running experiments on one year of hourly smart meter data from 60,000 households and we extract six energy attributes that describe general daily use patterns. We then use clustering techniques to derive six distinct energy lifestyle profiles from energy attribute proportions. Our lifestyle approach is also flexible to varying time interval lengths, and we test our lifestyle approach seasonally (Autumn, Winter, Spring, and Summer) to track energy lifestyle dynamics within and across households and find that around 73% of households manifest multiple lifestyles across a year. These energy lifestyles are then compared to different energy use characteristics, and we discuss their practical applications for demand response program design and lifestyle change analysis.

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arXiv2021

NeuralProphet: Explainable Forecasting at Scale

We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferation of time series data, explainable forecasting remains a challenging task for business and operational decision making. Hybrid solutions are needed to bridge the gap between interpretable classical methods and scalable deep learning models. We view Prophet as a precursor to such a solution. However, Prophet lacks local context, which is essential for forecasting the near-term future and is challenging to extend due to its Stan backend. NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework. Local context is introduced with auto-regression and covariate modules, which can be configured as classical linear regression or as Neural Networks. Otherwise, NeuralProphet retains the design philosophy of Prophet and provides the same basic model components. Our results demonstrate that NeuralProphet produces interpretable forecast components of equivalent or superior quality to Prophet on a set of generated time series. NeuralProphet outperforms Prophet on a diverse collection of real-world datasets. For short to medium-term forecasts, NeuralProphet improves forecast accuracy by 55 to 92 percent.

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arXiv2021

Cloud Storage for Multi-Service Battery Operation (Extended Version)

We study a cloud storage operator who provides shared storage service for electricity end-users using the residual part of a multi-service grid-scale battery primarily used for high priority grid services. We design an optimal product offering, pricing and customer portfolio. A framework and solution approach for assessing and operating such multi-service battery operations with stochastic services and different priority levels is an open problem is proposed. The methodology consists in modelling the problem as a two-stage stochastic optimization between high priority stochastic grid services and low priority cloud storage for stochastic end users. We also propose the operational metrics of multiplexing gain and probability of blocking to assess the operation of multi-service multi-user battery. To address the computational challenge of solving the stochastic optimization with a large number of end-users, we propose effective capacity as a convex approximation that allows an analytical solution. We then provide an empirical analysis based on real grid congestion data from RTE France, and a large dataset of end-users' electricity consumption in California. Our empirical analysis shows (i) our proposed effective capacity is a close approximation, (ii) battery operation and profit are sensitive to the cost of external resources, number of end-users, and RTE's leasing price of the battery, and (iii) with only a slight discount of the leasing price, the profit of the third party from a stochastic residual battery can be the same as that of a deterministic one. Cloud storage as a low priority service can profitably exist alongside other high priority battery services, making integration of more storage in the grid economically viable, and allowing larger intermittent renewables, a key path towards reduced carbon emissions.

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arXiv2021

EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations

The nexus between transportation, the power grid, and consumer behavior is more pronounced than ever before as the race to decarbonize the transportation sector intensifies. Electrification in the transportation sector has led to technology shifts and rapid deployment of electric vehicles (EVs). The potential increase in stochastic and spatially heterogeneous charging load presents a unique challenge that is not well studied, and will have significant impacts on grid operations, emissions, and system reliability if not managed effectively. Realistic scenario generators can help operators prepare, and machine learning can be leveraged to this end. In this work, we develop generative adversarial networks (GANs) to learn distributions of electric vehicle (EV) charging sessions and disentangled representations. We show that this model structure successfully parameterizes unlabeled temporal and power patterns without supervision and is able to generate synthetic data conditioned on these parameters. We benchmark the generation capability of this model with Gaussian Mixture Models (GMMs), and empirically show that our proposed model framework is better at capturing charging distributions and temporal dynamics.

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arXiv2021

Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.

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arXiv2021

Large-Scale Scenarios of Electric Vehicle Charging with a Data-Driven Model of Control

Planning to support widespread transportation electrification depends on detailed estimates for the electricity demand from electric vehicles in both uncontrolled and controlled or smart charging scenarios. We present a modeling approach to rapidly generate charging estimates that include control for large-scale scenarios with millions of individual drivers. We model uncontrolled charging demand using statistical representations of real charging sessions. We model the effect of load modulation control on aggregate charging profiles with a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control with multiple electricity rate schedules, achieving small errors (2.5% to 4.5%), while accelerating computations by more than 4000 times. We illustrate the methodology by generating scenarios for California's 2030 charging demand including multiple charging segments and controls, with scenarios run locally in under 50 seconds, and for assisting rate design modeling the large-scale impact of a new workplace charging rate.

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