OPERATIONAL DECISION MAKING IN COMPOUND ENERGY SYSTEMS USING MULTI-LEVEL MULTI PARADIGM SIMULATION BASED OPTIMIZATION
AuthorMazhari, Esfandyar M.
MULTI PARADIGM SIMULATION
OPERATIONAL DECISION MAKING
SIMULATION BASED OPTIMIZATION
Systems & Industrial Engineering
COMPOUND ENERGY SYSTEMS
MULTI-LEVEL MULTI PARADIGM SIMULATION
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractA two level hierarchical simulation and decision modeling framework is proposed for electric power networks involving PV based solar generators, various storage, and grid connection. The high level model, from a utility company perspective, concerns operational decision making and defining regulations for customers for a reduced cost and enhanced reliability. The lower level model concerns changes in power quality and changes in demand behavior caused by customers' response to operational decisions and regulations made by the utility company at the high level. The higher level simulation is based on system dynamics and agent-based modeling while the lower level simulation is based on agent-based modeling and circuit-level continuous time modeling. The proposed two level model incorporates a simulation based optimization engine that is a combination of three meta-heuristics including Scatter Search, Tabu Search, and Neural Networks for finding optimum operational decision making. In addition, a reinforcement learning algorithm that uses Markov decision process tools is also used to generate decision policies. An integration and coordination framework is developed, which details the sequence, frequency, and types of interactions between two models. The proposed framework is demonstrated with several case studies with real-time or historical for solar insolation, storage units, demand profiles, and price of electricity of grid (i.e., avoided cost). Challenges that are addressed in case studies and applications include 1) finding a best policy, optimum price and regulation for a utility company while keeping the customers electricity quality within the accepted range, 2) capacity planning of electricity systems with PV generators, storage systems, and grid, and 3) finding the optimum threshold price that is used to decide how much energy should be bought from sold to grid to minimize the cost. Mathematical formulations, and simulation and decision modeling methodologies are presented. A grid-storage analysis is performed for arbitrage, to explore if in future it is going to be beneficial to use storage systems along with grid, with future technological improvement in storage and increasing cost of electrical energy. An information model is discussed that facilitates interoperability of different applications in the proposed hierarchical simulation and decision environment for energy systems.
Degree ProgramGraduate College
Systems & Industrial Engineering
Degree GrantorUniversity of Arizona
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DEVELOPMENT AND IMPLEMENTATION OF THE MULTI-RESOLUTION AND LOADING OF TRANSPORTATION ACTIVITIES (MALTA) SIMULATION BASED DYNAMIC TRAFFIC ASSIGNMENT SYSTEM, RECURSIVE ON-LINE LOAD BALANCE FRAMEWORK (ROLB)Chiu, Yi-Chang; Villalobos, Jorge Alejandro; Hickman, Mark; Mirchandani, Pitu; Head, Larry; Chiu, Yi-Chang (The University of Arizona., 2011)The Multi-resolution Assignment and Loading of Transport Activities (MALTA) system is a simulation-based Dynamic Traffic Assignment model that exploits the advantages of multi-processor computing via the use of the Message Passing Interface (MPI) protocol. Spatially partitioned transportation networks are utilized to estimate travel time via alternate routes on mega-scale network models, while the concurrently run shortest path and assignment procedures evaluate traffic conditions and re-assign traffic in order to achieve traffic assignment goals such as User Optimal and/or System Optimal conditions.Performance gain is obtained via the spatial partitioning architecture that allows the simulation domains to distribute the work load based on a specially designed Recursive On-line Load Balance model (ROLB). The ROLB development describes how the transportation network is transformed into an ordered node network which serves as the basis for a minimum cost heuristic, solved using the shortest path, which solves a multi-objective NP Hard binary optimization problem. The approach to this problem contains a least-squares formulation that attempts to balance the computational load of each of the mSim domains as well as to minimize the inter-domain communication requirements. The model is developed from its formal formulation to the heuristic utilized to quickly solve the problem. As a component of the balancing model, a load forecasting technique is used, Fast Sim, to determine what the link loading of the future network in order to estimate average future link speeds enabling a good solution for the ROLB method.The runtime performance of the MALTA model is described in detail. It is shown how a 94% reduction in runtime was achieved with the Maricopa Association of Governments (MAG) network with the use of 33 CPUs. The runtime was reduced from over 60 minutes of runtime on one machine to less than 5 minutes on the 33 CPUs. The results also showed how the individual runtimes on each of the simulation domains could vary drastically with naïve partitioning methods as opposed to the balanced run-time using the ROLB method; confirming the need to have a load balancing technique for MALTA.
Simulating the Multi-epoch Direct Detection Technique to Isolate the Thermal Emission of the Non-transiting Hot Jupiter HD187123bBuzard, Cam; Finnerty, Luke; Piskorz, Danielle; Pelletier, Stefan; Benneke, Björn; Bender, Chad F.; Lockwood, Alexandra C.; Wallack, Nicole L.; Wilkins, Olivia H.; Blake, Geoffrey A.; et al. (IOP PUBLISHING LTD, 2020-06-04)We report the 6.5 sigma detection of water from the hot Jupiter HD187123b with a Keplerian orbital velocity K-p of 53 +/- 13 km s(-1). This high-confidence detection is made using a multi-epoch, high-resolution, cross-correlation technique, and corresponds to a planetary mass of 1.4(-0.3)(1.05) M-J and an orbital inclination of 21 degrees +/- 5 degrees. The technique works by treating the planet/star system as a spectroscopic binary and obtaining high signal-to-noise, high-resolution observations at multiple points across the planet's orbit to constrain the system's binary dynamical motion. All together, seven epochs of Keck/NIRSPEC L-band observations were obtained, with five before the instrument upgrade and two after. Using high-resolution SCARLET planetary and PHOENIX stellar spectral models, we were able to drastically increase the confidence of the detection by running simulations that could reproduce, and thus remove, the nonrandom structured noise in the final likelihood space well. The ability to predict multi-epoch results will be extremely useful for furthering the technique. Here, we use these simulations to compare three different approaches to combining the cross correlations of high-resolution spectra and find that the Zucker log(L) approach is least affected by unwanted planet/star correlation for our HD187123 data set. Furthermore, we find that the same total signal-to-noise ratio (S/N) spread across an orbit in many, lower S/N epochs rather than fewer, higher S/N epochs could provide a more efficient detection. This work provides a necessary validation of multi-epoch simulations, which can be used to guide future observations and will be key to studying the atmospheres of farther separated, non-transiting exoplanets.
A Hardware-in-the-Loop Dynamic Data Driven Adaptive Multi-Scale Simulation (DDDAMS) System for Crowd Surveillance via Unmanned VehiclesSon, Young-Jun; Massahi Khaleghi, Amirreza; Son, Young-Jun; Hariri, Salim; Lien, Jyh-Ming; Liu, Jian (The University of Arizona., 2015)Planning and control of unmanned vehicles play a major role in multi-vehicle systems since accomplishing challenging missions requires not only an extensive decision-making process but it also demands execution of those decisions based on the received information from multiple sensors. In this dissertation, a simulation-based planning and control system is designed, developed and demonstrated for effective and efficient crowd surveillance via collaborative operation of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The dissertation research works involve three phases. At phase one, a dynamic data driven adaptive multi-scale simulation (DDDAMS)-based planning and control framework is designed and developed, where the major components include 1) integrated controller, 2) integrated planner, 3) decision module for DDDAMS, and 4) real system. Moreover, crowd detection, tracking, and motion planning modules are implemented in this framework to perform the crowd surveillance mission. This framework adopts dynamic data driven application system (DDDAS) paradigm, where the integrated planner is invoked on a temporal or event basis to incorporate dynamic data from onboard sensors of unmanned vehicles into the simulation and select the best control strategy. At phase two, a testbed is designed and constructed using agent-based hardware-in-the-loop simulation, which involves various hardware components (i.e. real UAVs and UGVs containing onboard sensors and computers) and software components (agent-based simulation and hardware interface). The agent-based simulation, a major component of this testbed, is developed by modeling the behavior of the unmanned vehicles while utilizing the terrain elevation data obtained from GIS. Moreover, a social force model is used to mimic the crowd dynamics in the simulated environment. The constructed testbed is used to evaluate the effectiveness and computational efficiency of the proposed planning and control framework. At phase three, a team formation approach is proposed for allocating unmanned vehicles to different crowd clusters using their geometry and available number of resources based on two different criteria (i.e. overall coverage of all clusters and uniform assignment of resources among clusters). This approach is used in crowd splitting scenarios when the crowd starts to divide into clusters, and the existing team of unmanned vehicles is not able to continue following all the clusters. Moreover, control strategies for team formation, information aggregation, and motion planning of unmanned vehicles are introduced, and a method for determining the value of the control strategy parameter for the information aggregation of UAVs and UGVs is proposed. In conclusion, we believe this work has a profound impact on both the research community and practitioners using unmanned vehicles. Also, the developed hardware-in-the-loop DDDAMS system has the potential to be deployed in real-world applications such as border patrol.