An Integrated System Dynamics and Discrete Event Supply Chain Simulation Framework for Supply Chain Resilience with Non-Stationary Pandemic Demand

COVID-19 resulted in some of the largest supply chain disruptions in recent history. To mitigate the impact of future disruptions, we propose an integrated hybrid simulation framework to couple nonstationary demand signals from an event like COVID-19 with a model of an end-to-end supply chain. We first create a system dynamics susceptible-infected-recovered (SIR) model, augmenting a classic epidemiological model to create a realistic portrayal of demand patterns for oxygen concentrators (OC). Informed by this granular demand signal, we then create a supply chain discrete event simulation model of OC sourcing, manufacturing, and distribution to test production augmentation policies to satisfy this increased demand. This model utilizes publicly available data, engineering teardowns of OCs, and a supply chain illumination to identify suppliers. Our findings indicate that this coupled approach can use realistic demand during a disruptive event to enable rapid recommendations of policies for increased supply chain resilience with controlled cost

A Dynamic Adaptive Simulations Approach for Microgrid Control and Optimization

During the past two decades, the power systems witnessed vital changes in terms of centralized paradigm versus more decentralized and market driven approaches; technical advances on communications and computation; and generation technologies which collectively lead to the advancement of microgrids (MGs). In this thesis, a novel dynamic adaptive simulations (DAS) approach is introduced for addressing major challenges in the operation and control of MGs, such as solving the economic and environmental load dispatch problem, achieving a sophisticated autonomous control of microgrids, and promoting the cooperation between individual microgrids to increase the power network reliability and energy surety. Initially, a first version of dynamic adaptive simulation was designed, namely DAS-EELD, for the efficient real-time economic and environmental load dispatching . The DAS-EELD framework was illustrated and validated via a modified IEEE-30 bus test system and as the experiments revealed, it is capable of reducing the computational resource usage for the reliable power dispatch without compromising the quality of the solution. Moreover, for the operation and control of MGs a second version of DAS was developed, namely DAS-CONTROL, in order to speed up significantly the real-time computation of the resource allocation and control decisions to optimize the operational cost, energy surety, and emissions. For validating the DAS-CONTROL framework a realistic MG was utilized to prove that DAS-CONTROL significantly reduces the computational burden of a considerably complex multi-objective problem. Finally, a third version of DAS was developed, namely DAS-SH, to provide distributed microgrids with a protocol of self-healing, both when they are operating collaboratively and competitively (in an isolated mode) while increasing the reliability of the network by pledging energy surety. DASSH framework was applied to a realistic case study that includes three microgrids and has been tested under four different emergency incidents. The results reveal that the cooperative collection of distributed microgrids were able to meet the critical and priority loads to a higher extent at all times while sacrificing from the less important non-critical loads. With the combination of the results from the different dynamic adaptive simulation versions that were created, this thesis reveals that DAS is a promising method to model microgrid systems as it provides means to find the most efficient method to optimize and enhance the microgrids’ operation and control and attain several benefits

Online State Estimation of a Microgrid using Particle Filtering

Recent technical advances in power systems on communications, computation and generation technologies have collectively lead to the development of microgrids. However, these microgrids are still heavily challenged by the state estimation problem which traditionally exists in power grids. State estimation in these systems is especially crucial due to the impact it has to the power flow control and the security of the system. In this work, we introduce a novel algorithm for online state estimation of microgrids using particle filtering. The proposed algorithm is fed by a database receiving data from electrical and environmental sensors in real time. The performance of the proposed algorithm is first validated through synthetic experiments. Then, the experiments are conducted using real data obtained from a benchmark low voltage microgrid. The experiments reveal that the proposed algorithm is able to achieve state estimations that are very close to the actual states (in terms of power injections). This way, significant improvement is premised in the functional performance of microgrids while savings are encountered in computational resource utilization. As part of its future venues, proposed particle filter-based state estimation algorithm will be embedded into a dynamic data driven adaptive simulation framework that is being designed for the power control and management of microgrids

Multi-objective agent-based modeling of single-stream recycling programs

•We develop a planning and evaluation framework for single and dual stream recycling programs.•An agent-based simulation modeling approach is developed.•Multi-objective optimization is built in a realistic simulation environment.•Framework has an associated structured database and a resource allocation optimization module.•Framework is demonstrated for the single and dual stream recycling in Florida. In this research, our goal is to develop an agent-based simulation-based decision making framework for the effective planning of single-stream recycling (SSR) programs. The proposed framework is comprised of two main modules: a structured database and a simulation module. The database houses data necessary for simulating the entire solid waste management (SWM) system. The simulation module performs two major tasks. First, it identifies the various sources of system uncertainties and incorporates them into the SSR simulation model. Second, it compares and evaluates the alternatives of SSR (i.e., dual-stream recycling) with respect to cost, bottleneck facilities, and types and capacities of the processing facilities needed. For demonstration purposes, the proposed framework is applied to the state of Florida which has set a goal of reaching a 75% recycling rate by 2020. The proposed framework is a powerful tool that can be used by stakeholders for the evaluation of several “what-if” scenarios in their system before reaching a conclusion and making a decision

An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization

In this paper, we present a novel sequential sampling methodology for solving multi-objective optimization problems. Random sequential sampling is performed using the information from within the non-dominated solution set generated by the algorithm, while resampling is performed using the extreme points of the non-dominated solution set. The proposed approach has been benchmarked against well-known multi-objective optimization algorithms that exist in the literature through a series of problem instances. The proposed algorithm has been demonstrated to perform at least as good as the alternatives found in the literature in problems where the Pareto front presents convexity, nonconvexity, or discontinuity; while producing very promising results in problem instances where there is multi-modality or nonuniform distribution of the solutions along the Pareto front

DDDAMS-Based Dispatch Control in Power Networks

Electricity networks need robust decision making mechanisms that enable the system to respond swiftly and effectively to any type of disruption or anomaly in order to ensure reliable electricity flow. Electricity load dispatch is concerned with the production of reliable electricity at the lowest costs, both monetary and environmental, within the limitations of the considered network. In this study, we propose a novel DDDAMS-based economic load dispatching framework for the efficient and reliable real-time dispatching of electricity under uncertainty. The proposed framework includes 1) a database fed from electrical and environmental sensors of a power grid, 2) an algorithm for online state estimation of the considered electrical network using particle filtering, 3) an algorithm for effective culling and fidelity selection in simulation considering the trade-off between computational requirements, and the environmental and economic costs attained by the dispatch, and 4) data driven simulation for mimicking the system response and generating a dispatch configuration which minimizes the total operational and environmental costs of the system, without posing security risks to the energy network. Components of the proposed framework are first validated separately through synthetic experimentation, and then the entirety of the proposed approach is successfully demonstrated for different scenarios in a modified version of the IEEE-30 bus test system where sources of distributed generation have been added. The experiments reveal that the proposed work premises significant improvement in the functional performance of the electricity networks while reducing the cost of dynamic computations

A Voronoi-based Genetic Algorithm for Waste Collection Vehicle Routing Problem

In this paper, we present a genetic optimization algorithm embedding a Voronoi diagram to address the vehicle routing problem in waste collection. The proposed approach seeks to determine a set of delivery routes for the waste collection vehicles starting from the origin (the hauler's service center), leading to the generation nodes, picking up and transferring wastes to the treatment or disposal facilities, and finally, returning to the origin in a way which would minimize the total distance covered by the entire fleet. Furthermore, generation units are clustered into groups, for an easier and more convenient routing design of collection vehicles. Once a collection vehicle crosses an intersection of roads, it is obliged to continue and collect from the generation units it encounters in the next intersection. This fact gives the opportunity for clustering all the generation units of the same street between two consecutive intersections. In order to assign all generation units to the generation points in the most efficient manner, a Voronoi diagram is utilized to divide spaces into a number of regions called Voronoi cells. Lastly, an efficient combination of variables is determined through an embedded genetic optimization mechanism which starts with the initial population of candidate solutions subject to the overall time, capacity, and operational constraints

Dynamic Data Driven Adaptive Simulation Framework for Automated Control in Microgrids

In this paper, we introduce a novel dynamic data driven adaptive simulation framework for the operation and control of microgrids (MGs) that significantly accelerates the real-time computation of the resource allocation, and controls decisions to optimize the operational cost, energy surety, as well as emissions per MW. The proposed framework includes a database receiving input from electrical and environmental sensors, a fault detection algorithm that discovers liabilities and potential hazards within the MG, an agent-based simulation of the MG system, an optimal computing budget allocation-based control selection algorithm that uses the agent-based simulation to decide the best control design of the MG, and a multiobjective algorithm for optimizing the decisions of the MG given the best control design. For validating our framework, we use the structure of a realistic MG that is simulated using real-historical data. The experiments reveal that the proposed framework significantly reduces the computational burden of a considerably complex multiobjective problem