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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-T-540: Comprehensive Fluence Delivery Optimization with Multileaf Collimation

Purpose: Multileaf collimator (MLC) leaf sequencing is performed via commercial black-box implementations, on which a user has limited to no access. We have developed an explicit, generic MLC sequencing model to serve as a tool for future investigations of fluence map optimization, fluence delivery optimization, and rotational collimator delivery methods. Methods: We have developed a novel, comprehensive model to effectively account for a variety of transmission and penumbra effects previously treated on an ad hoc basis in the literature. As the model is capable of quantifying a variety of effects, we utilize the asymmetric leakage intensity across each leaf to deliver fluence maps with pixel size smaller than the narrowest leaf width. Developed using linear programming and mixed integer programming formulations, the model is implemented using state of the art open-source solvers. To demonstrate the versatility of the algorithm, a graphical user interface (GUI) was developed in MATLAB capable of accepting custom leaf specifications and transmission parameters. As a preliminary proof-ofconcept, we have sequenced the leaves of a Varian 120 Leaf Millennium MLC for five prostate cancer patient fields and one head and neck field. Predetermined fluence maps have been processed by data smoothing methods to obtain pixel sizes of 2.5 cm{sup 2}. The quality of output was analyzed using computer simulations. Results: For the prostate fields, an average root mean squared error (RMSE) of 0.82 and gamma (0.5mm/0.5%) of 91.4% were observed compared to RMSE and gamma (0.5mm/0.5%) values of 7.04 and 34.0% when the leakage considerations were omitted. Similar results were observed for the head and neck case. Conclusion: A model to sequence MLC leaves to optimality has been proposed. Future work will involve extensive testing and evaluation of the method on clinical MLCs and comparison with black-box leaf sequencing algorithms currently used by commercial treatment planning systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Next-Generation Solvers for Mixed-Integer Nonlinear Programs: Structure, Search, and Implementation (Final Report)

The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems. This research project added to the understanding of this area by making a number of fundamental advances. First, the work demonstrated many novel, strong, tractable relaxations designed to deal with non-convexities arising in mathematical formulation. Second, the research implemented the ideas in software that is available to the public. Finally, the work demonstrated the importance of these ideas on practical applications and disseminated the work through scholarly journals, survey publications, and conference presentations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Multi-parametric analysis for mixed integer linear programming: An application to transmission upgrade and congestion management

Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. Here, we compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Analysis of Modeling Assumptions used in Production Cost Models for Renewable Integration Studies

Renewable energy integration studies have been published for many different regions exploring the question of how higher penetration of renewable energy will impact the electric grid. These studies each make assumptions about the systems they are analyzing; however the effect of many of these assumptions has not been yet been examined and published. In this paper we analyze the impact of modeling assumptions in renewable integration studies, including the optimization method used (linear or mixed-integer programming) and the temporal resolution of the dispatch stage (hourly or sub-hourly). We analyze each of these assumptions on a large and a small system and determine the impact of each assumption on key metrics including the total production cost, curtailment of renewables, CO2 emissions, and generator starts and ramps. Additionally, we identified the impact on these metrics if a four-hour ahead commitment step is included before the dispatch step and the impact of retiring generators to reduce the degree to which the system is overbuilt. We find that the largest effect of these assumptions is at the unit level on starts and ramps, particularly for the temporal resolution, and saw a smaller impact at the aggregate level on system costs and emissions. For each fossil fuel generator type we measured the average capacity started, average run-time per start, and average number of ramps. Linear programming results saw up to a 20% difference in number of starts and average run time of traditional generators, and up to a 4% difference in the number of ramps, when compared to mixed-integer programming. Utilizing hourly dispatch instead of sub-hourly dispatch saw no difference in coal or gas CC units for either start metric, while gas CT units had a 5% increase in the number of starts and 2% increase in the average on-time per start. The number of ramps decreased up to 44%. The smallest effect seen was on the CO2 emissions and total production cost, with a 0.8% and 0.9% reduction respectively when using linear programming compared to mixed-integer programming and 0.07% and 0.6% reduction, respectively, in the hourly dispatch compared to sub-hourly dispatch.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A mixed-integer conic programming formulation for computing the flexibility index under multivariate gaussian uncertainty

We present a methodology for computing the flexibility index when uncertainty is characterized using multivariate Gaussian random variables. Our approach computes the flexibility index by solving a mixed-integer conic program (MICP). This methodology directly characterizes ellipsoidal sets to capture correlations in contrast to previous methodologies that employ approximations. We also show that, under a Gaussian representation, the flexibility index can be used to obtain a lower bound for the so-called stochastic flexibility index (i.e., the probability of having feasible operation). Our results also show that the methodology can be generalized to capture different types of uncertainty sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Optimizing district energy systems by integrating Borehole Thermal Energy Storage Using a Mixed-Integer Linear Programming g-function framework with a Multi-Timescale Rolling Horizon method

Shallow geothermal has gained increasing attention in recent years; however, a reliable framework for its accurate incorporation into large-scale energy system optimization remains lacking. This study proposes a Mixed-Integer Linear Programming (MILP) framework combined with the g-function approach to integrate Borehole Thermal Energy Storage (BTES) technology into energy system optimization. Validation against a Modelica-based reservoir network simulation demonstrates that the proposed framework effectively captures the ground thermal response under varying energy loads and accurately estimates the borefield energy supply. To enhance scalability, a Rolling Horizon with Multi-Timescale (RH-MTS) method is further introduced, reducing computational time by 73 % for the 1-year optimization model with only minor loss of optimality. The framework is demonstrated through the case study of the UC Berkeley campus. Results indicate that BTES is a cost-effective and low-carbon solution: two borefields comprising 382 boreholes can meet 8.0 % and 6.6 % of the total campus heating and cooling demand, respectively, at an average energy rate of 0.70–0.77 USD/kWh and carbon intensity of 0.54 kg-CO2/kWh. Short-term analysis reveals a 35%–65% decline in BTES energy flow after 3–6 months of continuous heating/cooling operation, while long-term simulation shows that annual energy production of BTES can vary by up to 12.0 % after four years before stabilizing. Overall, this study develops a novel optimization framework that couples physics-based g-function method with MILP optimization framework, thereby advancing methodological development for shallow-geothermal integration and providing actionable guidance for BTES deployment in district-energy systems.

Yang, Jiahui↗
DOE OSTI2017

Optimal Allocation of Static Var Compensator via Mixed Integer Conic Programming

Shunt FACTS devices, such as, a Static Var Compensator (SVC), are capable of providing local reactive power compensation. They are widely used in the network to reduce the real power loss and improve the voltage profile. This paper proposes a planning model based on mixed integer conic programming (MICP) to optimally allocate SVCs in the transmission network considering load uncertainty. The load uncertainties are represented by a number of scenarios. Reformulation and linearization techniques are utilized to transform the original non-convex model into a convex second order cone programming (SOCP) model. Numerical case studies based on the IEEE 30-bus system demonstrate the effectiveness of the proposed planning model.

Zhang, Xiaohu↗
DOE OSTI2011

Final Report---Optimization Under Nonconvexity and Uncertainty: Algorithms and Software

the goal of this work was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems. The focus of the work done in the continuation was on Mixed Integer Nonlinear Programs (MINLP)s and Mixed Integer Linear Programs (MILP)s, especially those containing a great deal of symmetry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Water Security Toolkit User Manual Version 1.2.

The Water Security Toolkit (WST) is a suite of open source software tools that can be used by water utilities to create response strategies to reduce the impact of contamination in a water distribution network . WST includes hydraulic and water quality modeling software , optimizati on methodologies , and visualization tools to identify: (1) sensor locations to detect contamination, (2) locations in the network in which the contamination was introduced, (3) hydrants to remove contaminated water from the distribution system, (4) locations in the network to inject decontamination agents to inactivate, remove, or destroy contaminants, (5) locations in the network to take grab sample s to help identify the source of contamination and (6) valves to close in order to isolate contaminate d areas of the network. This user manual describes the different components of WST , along w ith examples and case studies. License Notice The Water Security Toolkit (WST) v.1.2 Copyright c 2012 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive license for use of this work by or on behalf of the U.S. government. This software is distributed under the Revised BSD License (see below). In addition, WST leverages a variety of third-party software packages, which have separate licensing policies: Acro Revised BSD License argparse Python Software Foundation License Boost Boost Software License Coopr Revised BSD License Coverage BSD License Distribute Python Software Foundation License / Zope Public License EPANET Public Domain EPANET-ERD Revised BSD License EPANET-MSX GNU Lesser General Public License (LGPL) v.3 gcovr Revised BSD License GRASP AT&T Commercial License for noncommercial use; includes randomsample and sideconstraints executable files LZMA SDK Public Domain nose GNU Lesser General Public License (LGPL) v.2.1 ordereddict MIT License pip MIT License PLY BSD License PyEPANET Revised BSD License Pyro MIT License PyUtilib Revised BSD License PyYAML MIT License runpy2 Python Software Foundation License setuptools Python Software Foundation License / Zope Public License six MIT License TinyXML zlib License unittest2 BSD License Utilib Revised BSD License virtualenv MIT License Vol Common Public License vpykit Revised BSD License Additionally, some precompiled WST binary distributions might bundle other third-party executables files: Coliny Revised BSD License (part of Acro project) Dakota GNU Lesser General Public License (LGPL) v.2.1 PICO Revised BSD License (part of Acro project) i Revised BSD License Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Sandia National Laboratories nor Sandia Corporation nor the names of its con- tributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IM- PLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUD- ING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ii Acknowledgements This work was supported by the U.S. Environmental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The material in this document has been subject to technical and policy review by the U.S. EPA, and approved for publication. The views expressed by individual authors, however, are their own, and do not necessarily reflect those of the U.S. Environmental Protection Agency. Mention of trade names, products, or services does not convey official U.S. EPA approval, endorsement, or recommendation. The Water Security Toolkit is an extension of the Threat Ensemble Vulnerability Assessment-Sensor Place- ment Optimization Tool (TEVA-SPOT), which was also developed with funding from the U.S. Environ- mental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The authors acknowledge the following individuals for their contributions to the devel- opment of TEVA-SPOT: Jonathan Berry (Sandia National Laboratories), Erik Boman (Sandia National Laboratories), Lee Ann Riesen (Sandia National Laboratories), James Uber (University of Cincinnati), and Jean-Paul Watson (Sandia National Laboratories). iii Acronyms ATUS American Time-Use Survey BLAS Basic linear algebra sub-routines CFU Colony-forming unit CVAR Conditional value at risk CWS Contamination warning system EA Evolutionary algorithm EDS Event detection system EPA U.S. Environmental Protection Agency EC Extent of Contamination ERD EPANET results database file GLPK GNU Linear Programming Kit GRASP Greedy randomized adaptive sampling process HEX Hexadecimal HTML HyperText markup language INP EPANET input file LP Linear program MC Mass consumed MILP Mixed integer linear program MIP Mixed integer program MSX Multi-species extension for EPANET NFD Number of failed detections NS Number of sensors NZD Non-zero demand PD Population dosed PE Population exposed PK Population killed TAI Threat assessment input file TCE Tailed-conditioned expectation TD Time to detection TEC Timed extent of contamination TEVA Threat ensemble vulnerability assessment TSB Tryptic soy broth TSG Threat scenario generation file TSI Threat simulation input file VAR Value at risk VC Volume consumed WST Water Security Toolkit YML YAML configuration file format for WST iv Symbols Notation Definition Example { , } set brackets { 1,2,3 } means a set containing the values 1,2, and 3. [?] is an element of s [?] S means that s is an element of the set S . [?] for all s = 1 [?] s [?] S means that the statement s = 1 is true for all s in set S . P summation P n i =1 s i means s 1 + s 2 + * * * + s n . \ set minus S \ T means the set that contains all those elements of S that are not in set T . %7C given %7C is used to define conditional probability. P ( s %7C t ) means the prob- ability of s occurring given that t occurs. %7C ... %7C cardinality Cardinality of a set is the number of elements of the set. If set S = { 2,4,6 } , then %7C S %7C = 3. v

Klise, Katherine A.↗
DOE OSTI2017

Robust Reconfiguration of A Distribution System

In this paper, a robust reconfiguration approach based on Mixed Integer Programming (MIP) is proposed to minimize loss in distribution systems. A Depth-First Search (DFS) algorithm to enumerate possible loops provides radiality constraint. This provides a general solution to the radiality constraint for distribution system reconfiguration/expansion problems. Still, imprecision and ambiguity in net loads, i.e. load minus renewable generation, due to lack of sufficient measurements and high utilization of demand response programs and renewable resources, creates challenges for effective reconfiguration. Deterministic optimization of reconfiguration may no lead to optimal/feasible results. Two methods to address these uncertainties are introduced in this paper: one, based on a stochastic MIP (SMIP) formulation and two, based on a fuzzy MIP (FMIP) formulation. Case studies demonstrate the robustness and efficiency of the proposed reconfiguration methods.

Depth-First Search (DFS)↗
DOE OSTI2021

Disjunctive linear separation conditions and mixed-integer formulations for aircraft conflict resolution

In this paper, we address the aircraft conflict resolution problem in air traffic control. We introduce new mixed-integer programming formulations for aircraft conflict resolution with speed, heading and altitude control which are based on disjunctive linear separation conditions. We first examine the two-dimensional aircraft conflict resolution problem with speed and heading control represented as continuous decision variables. We show that the proposed disjunctive linear separation conditions are equivalent to the classical nonlinear conditions for aircraft separation. Further, we characterise conflict-free trajectories based on aircraft velocity bounds and propose a simple pre-processing algorithm to identify aircraft pairs which are either always conflict-free, or which cannot be separated using speed and heading control only. We then incorporate altitude control and propose a lexicographic optimisation formulation that aims to minimise the number of flight level changes before resolving outstanding conflicts via two-dimensional velocity control. The proposed mixed-integer programming formulations are nonconvex, and we propose convex relaxations, decomposition methods and constraint generation algorithms to solve the two-dimensional and lexicographic optimisation formulations to guaranteed optimality. Numerical experiments on four types of conflict resolution benchmarking instances are conducted to test the performance of the proposed mixed-integer formulations. Further, the proposed method is compared against two benchmarks based on state-of-the-art approaches for the aircraft conflict resolution problem. Our numerical results show that the proposed method largely outperforms both benchmarks in terms of runtime and is able to solve significantly more instances to global optimality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis.

In order to effectively plan the management and modernization of its large and diverse fleet of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) commissioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thorough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis (Version 2.0.2).

In order to effectively plan the management and modernization of their large and diverse fleets of vehicles, Program Executive Office Ground Combat Systems (PEO GCS) and Program Executive Office Combat Support and Combat Service Support (PEO CS&CSS) commis- sioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thor- ough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation. This paper, which is an update to the original CPAT formulation document published in 2015 (SAND2015-3487), covers the formulation of important new CPAT features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An L$_0$L$_1$-norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming

First-principles based lattice models allow the modeling of ab initio thermodynamics of crystalline mixtures for applications such as the construction of phase diagrams and the identification of ground state atomic orderings. The recent development of compressive sensing approaches for the construction of lattice models has further enabled the systematic construction of sparse physical models without the need for human intuition other than requiring the compactness of effective cluster interactions. However, conventional compressive sensing based on L1-norm regularization is strictly only applicable to certain classes of optimization problems and is otherwise not guaranteed to generate optimally sparse and transferable results, so that the method can only be applied to some materials science applications. In this paper, we illustrate a more robust L0L1-norm compressive-sensing method that removes the limitations of conventional compressive sensing and generally results in sparser lattice models that are at least as predictive as those obtained from L1-norm compressive sensing. Apart from the theory, a practical implementation based on state-of-the-art mixed-integer quadratic programming (MIQP) is proposed. The robustness of our methodology is illustrated for four different transition-metal oxides with relevance as battery cathode materials: Li2xTi2(1-x)O2, Li2xNi2yO2, MgxCr2O4, and NaxCrO2. This method provides a practical and robust approach for the construction of sparser and more predictive lattice models, improving on the compressive sensing paradigm and making it applicable to a much broader range of applications.

Huang, W↗
DOE OSTI2024

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimal, centralized dynamic curbside parking space zoning

In this paper we formulate a dynamic mixed integer program for optimally zoning curbside parking spaces subject to transportation policy-inspired constraints and regularization terms. First, we illustrate how given some objective of curb zoning valuation as a function of zone type (paid parking, bus stop, etc.), dynamically rezoning involves unrolling this optimization program over a fixed time horizon. Second, we implement two different solution methods given an example curb zoning valuation. In the first method, we solve long horizon dynamic zoning problems via approximate dynamic programming. In the second method, we employ Dantzig-Wolfe decomposition to break-up the mixed-integer program into a master problem and several sub-problems that can be solved in parallel. This speeds up the computational solve-time of the MIP considerably. We present simulation results and comparisons of the different employed techniques on vehicle arrival-rate data obtained for a neighborhood in downtown Seattle, Washington, USA.

Nazir, Mohammad Nawaf↗