A5001651510- Smart Grid Energy Management
- Optimal Power Flow Distribution
- Scheduling and Optimization Algorithms
- Electric Power System Optimization
- Advanced Manufacturing and Logistics Optimization
- Integrated Energy Systems Optimization
- Microgrid Control and Optimization
- Optimization and Packing Problems
- Hybrid Renewable Energy Systems
- Energy Load and Power Forecasting
- Advanced Control Systems Optimization
- Assembly Line Balancing Optimization
- Transportation Planning and Optimization
- Capital Investment and Risk Analysis
- Distributed Control Multi-Agent Systems
- Power System Optimization and Stability
- Manufacturing Process and Optimization
- Advanced Wireless Network Optimization
- Simulation Techniques and Applications
- Optimization and Search Problems
- Advanced Optimization Algorithms Research
- Energy Efficiency and Management
- Gene Regulatory Network Analysis
- Banking stability, regulation, efficiency
- Process Optimization and Integration
Rochester Institute of Technology
2019-2025
University of Connecticut
2013-2022
National Taiwan University
2022
University of California, Riverside
2022
Tsinghua University
2021
Huazhong University of Science and Technology
2020
Dalian Maritime University
2016
China University of Mining and Technology
2013
Qingdao Municipal Hospital
2013
Qingdao University
2013
To reduce energy costs and emissions of microgrids, daily operation is critical. The problem to commit dispatch distributed devices with renewable generation minimize the total emission cost while meeting forecasted demand. challenging because intermittent nature renewables. In this paper, photovoltaic (PV) uncertainties are modeled by a Markovian process. For effective coordination, other as Markov processes states depending on PV states. entire Markovian. This combinatorial solved using...
Many operation optimization problems such as scheduling and assignment of interest to the automation community are mixed-integer linear programming (MILP) problems. Because their combinatorial nature, effort required obtain optimal solutions increases drastically problem size increases. Such typically need be solved several times a day require short solving (e.g., 5, 10, or 20 min). The goal is, therefore, near-optimal with quantifiable quality in computationally efficient manner. Existing...
This paper summarizes the technical activities of IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. was established by Technology Innovation Subcommittee to first review state-of-the-art security-constrained unit commitment (SCUC) business model, its mathematical formulation, solution techniques solving electricity market clearing problems. The then investigated emerging challenges future problems presented efforts building...
Optimizing HVAC operation becomes increasingly important because of the rising energy cost and comfort requirements. In this paper, an innovative event-based approach is developed within Lagrangian relaxation framework to minimize HVAC's day-ahead cost. To solve optimization problem based on events challenging since with time-dependent uncertainties in weather, cooling load, etc., optimal policy not stationary. The nonstationary space extremely large, it time consuming find policy. overcome...
Unit Commitment is usually formulated as a Mixed Binary Linear Programming (MBLP) problem. When considering large number of units, state-of-the-art methods such branch-and-cut may experience difficulties. To address this, an important but much overlooked direction formulation transformation since if the problem constraints can be transformed to directly delineate convex hull in data pre-processing stage, then solution obtained by using linear programming without combinatorial In literature,...
Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) a promising alternative. However, directly good solutions difficult in view of the combinatorial nature UC. This paper synergistically integrates ML within our recent decomposition coordination method Surrogate Lagrangian Relaxation to learn...
Unit Commitment (UC) is an important problem in power system operations. It traditionally planned for 24 hours with one-hour time intervals. To accommodate the increasing net-load variability, sub-hourly UC has been suggested improved flexibility. Such a larger and more complicated than hourly because of increased number periods reduced unit ramping capabilities per period. The computational burden further exacerbated systems large numbers virtual transactions leading to dense transmission...
A distributed and asynchronous active fault management (DA-AFM) method is developed to manage networked microgrids' (NMs) performance under balanced or unbalanced grid faults. The DA-AFM aims (1) enable NMs' fast ride-through capabilities, (2) limit the total contributions by coordinating heterogeneous microgrids in NM system, (3) deploy software-defined networks (SDN) ensure highly resilient AFM. problem formulated an optimization form that can incorporate various objectives constraints a...
Job-shop scheduling is an important problem in planning and operation of manufacturing systems. For such difficult problems to be solved daily within short amounts time, the only practical goal obtain near-optimal solutions with quantifiable quality fast. Recent developments powerful Mixed-Integer Linear Programming (MILP) methods as branch-and-cut provide opportunity for a fresh perspective at new effective MILP formulation resolution problem. Moreover, tightening critically since if...
Job-shop scheduling is an important but difficult problem arising in low-volume high-variety manufacturing. It usually solved at the beginning of each shift with strict computational time requirements. To obtain near-optimal solutions quantifiable quality within limits, a direction to formulate them Integer Linear Programming (ILP) form so as take advantages widely available ILP methods such Branch-and-Cut (B&C). Nevertheless, requirements for on existing formulations are high. In this...
Many important optimization problems, such as manufacturing scheduling and power system unit commitment, are formulated Mixed-Integer Linear Programming (MILP) problems. Such problems generally difficult to solve because of their combinatorial nature, may subject strict computation time limitations. Recently, our decomposition-and-coordination method “Surrogate Absolute Value Lagrangian Relaxation” (SAVLR) exploits the exponential reduction complexity upon problem decomposition effectively...
<p>Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view increasing demand customized products, sizes are growing. A promising direction take advantage Machine Learning (ML). Direct learning predict job-shop scheduling, however, suffers from major difficulties when scales large. this paper, a Deep Neural Network (DNN)...
In recent years, Distributed Energy Systems (DESs) have been recognized as a good option for sustainable development of future energy systems. With growing environmental concerns, design optimization DESs through economic assessments only is not sufficient. To achieve long-run sustainability supply, the key idea this paper to investigate exergy in DES attain rational use resources while considering qualities supply and demand. By using low-temperature sources low-quality thermal demand,...
The increasing pressure to meet demand are forcing semiconductor manufacturers seek efficient scheduling methods. Lithography, with a limited number of expensive resources and the reentrant nature fabrication processes, is major bottleneck. This paper presents litho machine formulation for high-volume low-variety manufacturing over day, novel modeling resource setups, reticle expirations, future stacking layer load balancing. problem believed be NP hard. After linearization simplification,...
A streamlined parallel traffic management system (PtMS) is outlined that works alongside a redesigned intelligent transportation in Qingdao, China. The PtMS's structure provides enhanced control and support, with increased versatility for use real-world scenarios.
Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view increasing demand customized products, sizes are growing. A promising direction take advantage Machine Learning (ML). Direct learning predict job-shop scheduling, however, suffers from major difficulties when scales large. this paper, a Deep Neural Network (DNN)...