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DOE OSTI2012

Capacitated location of collection sites in an urban waste management system

Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the location of waste collection sites. In this paper, we propose an integer programming model that helps decision makers in choosing the sites where to locate the unsorted waste collection bins in a residential town, as well as the capacities of the bins to be located at each collection site. This model helps in assessing tactical decisions through constraints that force each collection area to be capacitated enough to fit the expected waste to be directed to that area, while taking into account Quality of Service constraints from the citizens' point of view. Moreover, we propose an effective constructive heuristic approach whose aim is to provide a good solution quality in an extremely reduced computational time. Computational results on data related to the city of Nardo, in the south of Italy, show that both exact and heuristic approaches provide consistently better solutions than that currently implemented, resulting in a lower number of activated collection sites, and a lower number of bins to be used.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Simultaneous personnel and vehicle shift scheduling in the waste management sector

Highlights: • We consider the problem of simultaneously schedule personnel and vehicle shifts in the waste management sector. • We propose both an optimization model and a heuristic approach. • Important monetary savings can be achieved in the waste collection operations. - Abstract: Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the definition of shift schedules for both personnel and vehicles. This activity has a great incidence on the tactical and operational cost for companies. In this paper, we propose an integer programming model to find an optimal solution to the integrated problem. The aim is to determine optimal schedules at minimum cost. Moreover, we design a fast and effective heuristic to face large-size problems. Both approaches are tested on data from a real-world case in Southern Italy and compared to the current practice utilized by the company managing the service, showing that simultaneously solving these problems can lead to significant monetary savings.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Optimization of municipal solid waste collection and transportation routes

Graphical abstract: Display Omitted - Highlights: • Profitable integrated solid waste management system. • Optimal municipal waste collection scheme between the sources and waste collection centres. • Optimal path calculation between waste collection centres and transfer stations. • Optimal waste routing between the transfer stations and processing plants. - Abstract: Optimization of municipal solid waste (MSW) collection and transportation through source separation becomes one of the major concerns in the MSW management system design, due to the fact that the existing MSW management systems suffer by the high collection and transportation cost. Generally, in a city different waste sources scatter throughout the city in heterogeneous way that increase waste collection and transportation cost in the waste management system. Therefore, a shortest waste collection and transportation strategy can effectively reduce waste collection and transportation cost. In this paper, we propose an optimal MSW collection and transportation scheme that focus on the problem of minimizing the length of each waste collection and transportation route. We first formulize the MSW collection and transportation problem into a mixed integer program. Moreover, we propose a heuristic solution for the waste collection and transportation problem that can provide an optimal way for waste collection and transportation. Extensive simulations and real testbed results show that the proposed solution can significantly improve the MSW performance. Results show that the proposed scheme is able to reduce more than 30% of the total waste collection path length.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Uncertainty Quantification in CO 2 Sequestration Using Surrogate Models from Polynomial Chaos Expansion

In this paper, surrogate models are iteratively built using polynomial chaos expansion (PCE) and detailed numerical simulations of a carbon sequestration system. Output variables from a numerical simulator are approximated as polynomial functions of uncertain parameters. Once generated, PCE representations can be used in place of the numerical simulator and often decrease simulation times by several orders of magnitude. However, PCE models are expensive to derive unless the number of terms in the expansion is moderate, which requires a relatively small number of uncertain variables and a low degree of expansion. To cope with this limitation, instead of using a classical full expansion at each step of an iterative PCE construction method, we introduce a mixed-integer programming (MIP) formulation to identify the best subset of basis terms in the expansion. This approach makes it possible to keep the number of terms small in the expansion. Monte Carlo (MC) simulation is then performed by substituting the values of the uncertain parameters into the closed-form polynomial functions. Based on the results of MC simulation, the uncertainties of injecting CO{sub 2} underground are quantified for a saline aquifer. Moreover, based on the PCE model, we formulate an optimization problem to determine the optimal CO{sub 2} injection rate so as to maximize the gas saturation (residual trapping) during injection, and thereby minimize the chance of leakage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics

Here, we propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. Conversely, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new data-driven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple count-based reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. Furthermore, we describe a framework in this work that paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Real-Time Corrective Switching in Response to Simultaneous Contingencies

The standard optimal power flow (OPF) problem is an economic dispatch (ED) problem combined with transmission constraints, which are based on a static topology. Prior research has demonstrated that cooptimization of generation dispatch and transmission topology results in cost savings. However, the computational complexity associated with topology control (TC) has been a major deterrent to its implementation. The proposed work investigates the application of a heuristic, a greedy algorithm, to improve the computational time for the TC problem while still maintaining the ability to find quality solutions. An expression is derived, which indicates the impact on the objective for a marginal change in the state of a transmission line. This expression is used to generate a priority list with potential candidate lines for switching, which may provide huge improvements to the system. The advantage of this method is that it is a fast heuristic as compared to using mixed integer programming (MIP) to find a solution. The heuristic is tested on real-time applications such as N-1 events, N - m events (m simultaneous contingencies), and cascading events. The proposed algorithm specifically targets cases that lead to load shedding in the system and provides a quick and efficient method to restore the loads by improving the deliverability of reserves via TC. Simulation results on the IEEE 73 bus test system and the IEEE 118 bus test system suggest a significant improvement in the amount of load served to the system with TC as opposed to without TC with minimal time and computation.

Electric power systems↗
DOE OSTI2020

An Optimization-Based Planning Tool for On-Demand Mobility Service Operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

Strategic Placement and Sizing of Distributed Generation for Resilience Enhancement of Distribution Grids With Microgrid Formation

The rise in frequency and severity of extreme weather events highlights the need for resilient power distribution networks. Microgrids can help improve the resilience of distribution grids by providing continuous power supply using local distribution generation (DG) when the distribution grid fails. In this paper, we propose an approach for optimal placement and sizing of DG to form multiple microgrids throughout the distribution network by restoration actions such as switching operations in case of distribution grid outages caused by extreme weather events. Considering the randomness of damaged distribution lines, the DG placement and sizing problem is formulated as a two-stage stochastic mixed-integer program, with the first stage determining the placement and size of DG, and the second stage focusing on minimizing the amount of load shedding through network restoration and microgrid formations for each scenario. Due to the large number of scenarios, the sample average approximation (SAA) method is employed to solve the problem. The results of case studies on a modified IEEE 33 bus distribution grid demonstrate the effectiveness of the proposed DG placement and sizing strategy in improving the resilience of distribution grids by allowing the formation of multiple microgrids. In addition, the robustness and accuracy of the SAA method are validated through various case studies.

Distributed generation planning↗
DOE OSTI2020

A Privacy Preserving Model-Free Optimization and Control Framework for Demand Response from Residential Thermal Loads

We consider the problem of optimizing the cost of procuring electricity for a large collection of homes managed by a load serving entity, by pre-cooling or pre-heating the thermal inertial loads in the homes to avoid procuring power during periods of peak electricity pricing. We would like to accomplish this objective in a completely privacy-preserving and model-free manner, that is, without direct access to the state variables (temperatures or power consumption) or the dynamical models (thermal characteristics) of individual homes, while guaranteeing personal comfort constraints of the consumers. We propose a two-stage optimization and control framework to address this problem. In the first stage, we use a long short-term memory (LSTM) network to predict hourly electricity prices, based on historical pricing data and weather forecasts. Given the hourly price forecast and thermal models of the homes, the problem of designing an optimal power consumption trajectory that minimizes the total electricity procurement cost for the collection of thermal loads can be formulated as a large-scale integer program (with millions of variables) due to the on-off cyclical dynamics of such loads. We provide a simple heuristic relaxation to make this large-scale optimization problem model-free and computationally tractable. In the second stage, we translate the results of this optimization problem into distributed open-loop control laws that can be implemented at individual homes without measuring or estimating their state variables, while simultaneously ensuring consumer comfort constraints. We demonstrate the performance of this approach on a large-scale test case comprising of 500 homes in the Houston area and benchmark its performance against a direct model-based optimization and control solution.

Sivaranjani, S.↗
DOE OSTI2011

The Effects of a Dynamic Spectrum Access Overlay in LTE-Advanced Networks

As early as 2014, mobile network operators’ spectral capacity will be overwhelmed by the demand brought on by new devices and applications. To augment capacity and meet this demand, operators may choose to deploy a Dynamic Spectrum Access (DSA) overlay. The signaling and functionality required by such an overlay have not yet been fully considered in the architecture of the planned Long Term Evolution Advanced (LTE+) networks. This paper presents a Spectrum Accountability framework to be integrated into LTE+ architectures, defining specific element functionality, protocol interfaces, and signaling flow diagrams required to enforce the rights and responsibilities of primary and secondary users. We also quantify, through integer programs, the benefits of using DSA channels to augment capacity under a scenario in which LTE+ network can opportunistically use TV and GSM spectrum. The framework proposed here may serve as a guide in the development of future LTE+ network standards that account for DSA.

42 ENGINEERING↗
DOE OSTI2016

Solving Open MIP Instances with ParaSCIP on Supercomputers Using up to 80,000 Cores

This paper describes how we solved 12 previously unsolved mixed-integer programming (MIP) instances from the MIPLIB benchmark sets. To achieve these results we used an enhanced version of ParaSCIP, setting a new record for the largest scale MIP computation: up to 80,000 cores in parallel on the Titan supercomputer. In this paper we describe the basic parallelization mechanism of ParaSCIP, improvements of the dynamic load balancing and novel techniques to exploit the power of parallelization for MIP solving. We give a detailed overview of computing times and statistics for solving open MIPLIB instances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Data-Enabled Building Energy Savings (D-E BES)

Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building's physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. Herein this paper illustrates that creating an affinity between a building resident's thermal preferences and a building apartment's unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents' thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building's physical characteristics as well as the variation in residents' thermal preferences. The work further demonstrates that both the unregulated temperature and the resident's preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by the apartment's geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Inverse Calculation of Burden Distribution Matrix Using B-spline Model Based PDF control in Blast Furnace Burden Charging Process

The inverse calculation of burden distribution matrix (BDM) is one of the most important challenges in the blast furnace operation in iron-making processes. In general, blast furnace consumes 65% of the total energy for the whole steel-making. Focusing on this practical challenge, this article proposes a new burden distribution spatial model in calculating burden charging process, and develops a B-spline approximation-based probability density function (PDF) control algorithm to assign the expected thickness distribution of burden layer and, thus, develops a new method for the required inverse calculation of BDM. First, a novel method for the thickness distribution of burden layer is given using B-spline model to produce an expected distribution shape subjected to a desired tracking within a specific spatial constraint. Then, according to the coexistence of continuous and bounded discrete variables in BDM, a novel hybrid optimization control method by combining integer programming and PDF tracking is further established for the effective inverse calculation of BDM. Finally, the proposed PDF-based iterative inverse calculation of BDM using B-spline models are tested using various data from industrial examples. Furthermore, the simulation results show that the proposed method is well suited to solve the BDM inverse calculation problem in practice.

42 ENGINEERING↗
DOE OSTI2015

A Decomposition Approach for Solving Seasonal Transmission Switching

Economic transmission switching has been proposed as a new control paradigm to improve the economics of electric power systems. In practice, the transmission switching operation itself is a disruptive action to the system. Frequently switching lines into or out of service can create undesirable effects on the security and reliability of power systems and may require new investments in the automation and control systems. In this paper, we formulate an economic seasonal transmission switching model where transmission switching occurs once at the beginning of a time period (season) and then the transmission topology remains unchanged during that period. The proposed seasonal transmission switching model is a large-scale mixed integer programming problem. The objective of the optimization model is to minimize the total energy generation cost over the season subject to loads and N-1 reliability requirements. We develop a novel decomposition method that decomposes the seasonal problem into one-hour problems which are then solved efficiently. We demonstrate our model and the decomposition approach on the 14-bus, 39-bus, and 118-bus power systems and show potential cost savings in each case.

Decomposition↗
DOE OSTI2015

Capacity Adequacy and Revenue Sufficiency in Electricity Markets With Wind Power

We present a computationally efficient mixed-integer program (MIP) that determines optimal generator expansion decisions, as well as periodic unit commitment and dispatch. The model is applied to analyze the impact of increasing wind power capacity on the optimal generation mix and the profitability of thermal generators. In a case study, we find that increasing wind penetration reduces energy prices while the prices for operating reserves increase. Moreover, scarcity pricing for operating reserves through reserve shortfall penalties significantly impacts the prices and profitability of thermal generators. Without scarcity pricing, no thermal units are profitable, however scarcity pricing can ensure profitability for peaking units at high wind penetration levels. Capacity payments can also ensure profitability, but the payments required for baseload units to break even increase with the amount of wind power. The results indicate that baseload units are most likely to experience revenue sufficiency problems when wind penetration increases and new baseload units are only developed when natural gas prices are high and wind penetration is low.

capacity adequacy↗
DOE OSTI2021

Resilience-Oriented DG Siting and Sizing Considering Stochastic Scenario Reduction

In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. Here, the case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience.

42 ENGINEERING↗
DOE OSTI2018

Microgrids for Service Restoration to Critical Load in a Resilient Distribution System

icrogrids can act as emergency sources to serve critical loads when utility power is unavailable. This paper proposes a resiliency-based methodology that uses microgrids to restore critical loads on distribution feeders after a major disaster. Due to limited capacity of distributed generators (DGs) within microgrids, dynamic performance of the DGs during the restoration process becomes essential. In this paper, the stability of microgrids, limits on frequency deviation, and limits on transient voltage and current of DGs are incorporated as constraints of the critical load restoration problem. The limits on the amount of generation resources within microgrids are also considered. By introducing the concepts of restoration tree and load group, restoration of critical loads is transformed into a maximum coverage problem, which is a linear integer program (LIP). The restoration paths and actions are determined for critical loads by solving the LIP. A 4-feeder, 1069-bus unbalanced test system with four microgrids is utilized to demonstrate the effectiveness of the proposed method. The method is applied to the distribution system in Pullman, WA, resulting in a strategy that uses generators on the Washington State University campus to restore service to the Hospital and City Hall in Pullman.

Xu, Yin (ORCID:0000000209459024)↗