Nurcin Celik

Hybrid simulation and optimization-based design and operation of integrated photovoltaic generation, storage units, and grid

Abstract Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control extremely difficult and requires storage units as an additional concern. The goal of this research is to design and develop a flexible tool, which will allow us to obtain (1) an optimal capacity of an integrated photovoltaic (PV) system and storage units and (2) an optimal operational decision policy considering the current and future market prices of the electricity. The proposed tool is based on hybrid (system dynamics model and agent-based model) simulation and meta-heuristic optimization. In particular, this tool has been developed for three different scenarios (involving different geographical scales), where PV-based solar generators, storage units (compressed-air-energy-storage (CAES) and super-capacitors), and grid are used in an integrated manner to supply energy demands. Required data has been gathered from various sources, including NASA and TEP (utility company), US Energy Information Administration, National Renewable Energy Laboratory, commercial PV panel manufacturers, and publicly available reports. The constructed tool has been demonstrated to (1) test impacts of several factors (e.g. demand growth, efficiencies in PV panel and CAES system) on the total cost of the integrated generation and storage system and an optimal mixture of PV generation and storage capacity, and to (2) demonstrate an optimal operational policy.

DDDAS-based multi-fidelity simulation framework for supply chain systems

Dynamic-Data-Driven Application Systems (DDDAS) is a new modeling and control paradigm which adaptively adjusts the fidelity of a simulation model. The fidelity of the simulation model is adjusted against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective date update. To this end, comprehensive system architecture and methodologies are first proposed, where the components include a real-time DDDAS simulation, grid modules, a web service communication server, databases, various sensors and a real system. Abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation are enabled through the embedded algorithms developed in this work. Grid computing is used for computational resources management and web services are used for inter-operable communications among distributed software components. The proposed DDDAS is demonstrated on an example of preventive maintenance scheduling in a semiconductor supply chain.

MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling

Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.

Continuous-discrete simulation-based decision making framework for solid waste management and recycling programs

Solid waste produced as a by-product of our daily activities poses a major threat to societies as populations grow and economic development advances. Consequently, the effective management of solid waste has become a matter of critical importance for communities. However, solid waste management systems are inherently large-scale, diverse, and subject to many uncertainties, and must serve numerous stakeholders with divergent objectives. In this study, we propose a simulation-based decision-making and optimization framework for the analysis and development of effective solid waste management and recycling programs. The proposed solution includes a database and two main modules: an assessment module and a resource allocation optimization module. The assessment module identifies the sources of uncertainties in the system, which are then parameterized and incorporated into the resource allocation optimization module. The resource allocation optimization module involves a novel discrete-continuous model of the system under consideration, in which the continuous nature of decision variables is maintained while inherently discrete processing and transfer operations are accurately captured. The model operates with respect to the waste types and characteristics, costs, environmental impacts, types, location and capacities of processing facilities, and their technological capabilities. Then, an optimization mechanism embedded in the resource allocation optimization module solves the multi-criteria problem of the allocation of limited resources by simultaneously optimizing all relevant decision variables, evaluating performance in real-time via the model. Here, the optimum solution is considered as the combination of parameters that will lead to the highest recycling rate with minimum cost. The proposed framework has been successfully demonstrated for the Miami-Dade County Solid Waste Management System in the State of Florida.

Hybrid simulation and optimization-based capacity planner for integrated photovoltaic generation with storage units

Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control difficult and requires storage units. The goal of this research is to develop a flexible capacity planning tool, which will allow us to obtain a most economical mixture of capacities from solar generation as well as storage while meeting reliability requirements against fluctuating demand and weather conditions. The tool is based on hybrid (system dynamics and agent-based models) simulation and meta-heuristic optimization. In particular, the proposed tool has been developed for scenarios, where photovoltaic generators and storage units (compressed-air-energy-storage and super-capacitors) are used to supply energy demands in a region characterized by different house-holds considering different times and seasons. The constructed tool has been used to test impact of several factors (e.g. demand growth, efficiencies in PV panel and storage techniques) on the total cost of the system. Initial results look quite promising.

Efficient multi-fidelity simulation optimization

Simulation models of different fidelity levels are often available for a complex system. High-fidelity simulations are accurate but time-consuming. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are faster and can evaluate a large number of solutions. But their results may contain significant bias and variability. We propose an Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) framework to exploit the benefits of high- and low-fidelity simulations to efficiently identify a (near) optimal solution. MO2TOS uses low-fidelity simulations for all solutions and then assigns a fixed budget of high-fidelity simulations to solutions based on low-fidelity simulation results. We show the benefits of MO2TOS via theoretical analysis and numerical experiments with deterministic simulations and stochastic simulations where noise is negligible with sufficient replications. We compare MO2TOS to Equal Allocation (EA) and Optimal Computing Budget Allocation (OCBA). MO2TOS consistently outperforms both EA and OCBA.

Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development

Abstract The development of alliance-based software requires the collaboration of many stakeholders. These different stakeholders across multiple organizations form a complex social network. The goal of this paper is to develop a novel modeling framework, which will help task managers devise optimal workforce assignments considering both short-term and long-term aspects of the software development process. The proposed framework is composed of an assignment module and a prediction module. For a given task, the assignment module first selects a candidate workforce mix. Based on the candidate workforce mix, the prediction module then predicts the short-term performance (productivity) as well as the long-term performance (workforce training and robustness of the organization) of the organization. Then, the assignment module selects another candidate mix, and this iteration continues until an optimal workforce mix is found. The prediction module and the assignment module are based on an agent-based simulation method and a multi-objective optimization model, respectively. The proposed modeling framework is illustrated with a software enhancement request process in Kuali, an alliance-based open source software development project involving 12 organizations. The constructed framework is executed with varying parameters to demonstrate its use and benefit in the software enhancement process.

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.