Juan Pablo Sáenz

Electric utility resource planning using Continuous-Discrete Modular Simulation and Optimization (CoDiMoSO)

Electric utility resource planning traditionally focuses on conventional energy supplies such as coal, natural gas, and oil. Nowadays, planning of renewable energy generation as well as its side necessity of storage capacities have become equally important due to the increasing growth in energy demand, insufficiency of natural resources, and newly established policies for low carbon footprint. In this study, we propose to develop a comprehensive simulation based decision making framework to determine the best possible combination of resource investments for electric power generation and storage capacities. The proposed tool involves a combined continuous-discrete modular modeling approach for processes of different nature that exist within this complex system, and will help the utility companies conduct resource planning via employed multiobjective optimization techniques in a realistic simulation environment. The distributed power system considered here has four major components including (1) energy generation via a solar farm, a wind farm, and a fossil fuel power station, (2) storage via compressed air energy storage system, and batteries, (3) transmission via a bus and two main substations, and (4) demand of industrial, commercial, residential and transportation sectors. The proposed approach has been successfully demonstrated for the electric utility resource planning at a scale of the state of Florida.

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 DDDAMS framework for real-time load dispatching in power networks

The economic environmental load dispatch problem in power networks aims at producing electricity at the lowest financial and environmental costs. In this paper, we propose a novel real-time dynamic data driven adaptive multi-scale simulation framework (RT-DDDAMS) for efficient real-time dispatching of electricity. The framework includes 1) a discovery procedure where the network is split into sub-networks and prospective fidelities are identified, 2) an RT-DDDAMS platform involving algorithms for state estimation, fidelity selection, and multi-objective optimization alongside with a system simulation; and 3) databases for storing sub-network topologies, fidelities, and selective measurements. The best compromise load dispatch obtained from this framework is then sent to the considered power network for deployment. The proposed framework is illustrated and validated via a modified IEEE-30 bus test system. The experiments reveal that the proposed framework significantly reduces the computational resource usages needed for the reliable power dispatch without compromising the quality of the solutions.

Optimization of distributed generation penetration based on particle filtering

Distributed generation is small scale power cogeneration within an integrated energy network, that provides system wide and environmental benefits. Network benefits include enhancements to reliability, reduction of peak power requirements, improved power quality and enhanced resilience. Environmental benefits include better land use for transmission and distribution, and reduced ecological impact. Deploying distributed generation affects the power loss in the system and has an associated cost. Therefore, optimization of the penetration level of the distributed generation should consider both goals of minimizing total power loss and minimizing total operational costs. In this study, we propose a novel multi-objective optimization framework based on particle filtering to evaluate the effects of adding distributed generation to a networked system in terms of power loss and operational costs, simultaneously. The proposed framework has been demonstrated on the IEEE-30 bus system yielding to minimal power losses of 2.075 MW and minimal costs of

An Evolutionary Sequential Sampling Algorithm for Multi-Objective Optimization

547.51 per hour.

SIMULATION-BASED INTERACTIVE MODELLING OF BARRIER OPTION PRICING1

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.

An agent-based model of social networks for evaluating asthma control interventions on reducing the emergency department visits

Abstract The pricing of barrier options is a unique problem faced by the financial world since the options depend on the path taken by their underlying asset’s spot price. The methods of pricing these exotic options include computationally heavy analytical models that often require specific considerations for each of the different possible option types, as well as approximate correction factors, that often make them impractical. To determine the fair price of a barrier option in a practical, computationally efficient setting, in this study, we propose to develop a simulation-based discrete-event modelling framework considering the impacts of inherent interactions between an option’s underlying asset and the market. Considering this, the underlying asset’s price is split into two components where the first one represents the behaviour of the market and the second one represents the behaviour of the underlying asset that is independent from the market. The proposed framework has been employed to establish the fair price of options on the stocks of BHP, POT and RIO. It has been demonstrated that 45.5%, 32% and 38% of the price of the option is driven solely by the behaviour of the market for options on BHP, POT and RIO, respectively.

Two-stage economic and environmental load dispatching framework using particle filtering

Asthma has become a leading cause of childhood disability and school absenteeism in the United States. While asthma is a manageable chronic disease, the cost of its management is on the rise, especially because asthma-related emergency department (ED) visits cost five times more than primary care visits. Nonetheless, the costs of asthma management can be significantly decreased using effective management strategies via social network analyses and finding ways to reduce the asthma triggers that may cause ED visits. In this study, a social network analysis model is developed, which evaluates the impact of asthma management interventions by the number of ED visits from asthmatic children. Simulation results show that the implementation of an early symptom identification strategy for asthmatic children and their parents decrease the average number of annual ED visits for an asthmatic crisis from 0.156 visits to 0.042 visits per 1000 patients diagnosed with asthma. In addition, the simulation results reveal that the implementation of an asthma awareness programme in schools targeting teachers and staff members reduces the annual ED visits for asthmatic crises (per 1000 patients diagnosed with the disorder) to 0.108 visits per year. Asthma awareness campaign in school children would lead to a drop in the annual ED visits for an asthmatic crisis (per 1000 patients diagnosed with the disorder) to 0.103 visits per year. The use of a public asthma awareness campaign leads to a change in the annual ED visits for an asthmatic crisis (per 1000 patients diagnosed with the disorder) from 0.156 visits to 0.144 visits per year.