WSC 2009 Final Abstracts

Integrating Agent-Based Simulation and System Dynamics to Support Product Strategy Decisions in the Automotive Industry
Karsten Kieckhäfer and Grit Walther (Technische Universität Braunschweig), Joachim Axmann (Volkswagen AG) and Thomas Stefan Spengler (Technische Universität Braunschweig)

Abstract:
Especially in the European Union both, regulatory requirements regarding the CO2 emissions of new vehicles and the short-age of crude oil force car manufacturers to introduce alternative fuel and powertrain concepts. Due to high investments and long development times as well as the parallel offer of conventional and alternative technologies, an appropriate product strategy is required. Car manufacturers have to decide, which powertrain to introduce at which time in which vehicle class. Hence, the aim of this paper is to develop a framework for the analysis of product strategies in the automotive industry with special regard to alternative fuel and powertrain technologies. The framework integrates System Dynamics and Agent-based Simulation in a simulation environment. On basis of this analysis recommendations can be deduced concerning the implementation of different product portfolios.

Simulation and Optimization of Material and Energy Flow Systems
Andreas Moeller (Leuphana University Lueneburg), Martina Prox (ifu Hamburg GmbH) and Mario Schmidt and Hendrik Lambrecht (University of Applied Science Pforzheim)

Abstract:
Material flow analysis (MFA) becomes more and more an important instrument to support environmental protection and sustainable development. Often, a special approach, life-cycle assessment (LCA), is put on the same level with material flow analysis. Based on the industrial ecology or industrial metabolism paradigm (cf. Fischer-Kowalski 1998, Fischer-Kowalski and H�ttler 1999), we interpret material flow analysis in a broader sense. That makes it possible to simulate, to analyze and, as a subsequent step, to optimize multi-product systems. This contribution shows how simulation and optimization can utilized in the field of material flow analysis. It can be shown that the different approaches are not alternatives. Rather, it is possible to combine them in order to provide a flexible MFA tool-chain.

Combination of Job Oriented Simulation with Ecological Material Flow Analysis as Integrated Analysis Tool for Business Production Processes
Philip Joschko and Bernd Page (University of Hamburg) and Volker Wohlgemuth (HTW Berlin, University of Applied Sciences)

Abstract:
This paper outlines the application of a special Environmental Management Information System (EMIS) as combination of discrete event simulation with ecological material flow analysis for a selected production process. The software tool serves as decision aid for economic as well as ecological business problems. A combined view of the material flow as well as job oriented view on an enterprise allows for a unified and efficient model building process. This contribution summarizes the underlying concepts and the experiences with the development and utilization of a suitable software tool following this integrated view and describes the concrete problems and solutions at the example of modeling a complex semiconductor fabrication.

Sustainability Toolkit for Simulation-Based Logistic Decisions
Michael E. Kuhl and Xi Zhou (Rochester Institute of Technology)

Abstract:
As sustainability related issues, such as energy consumption and environmental impact issues are becoming a more integrated part of operational and long-term planning decisions, simulation modeling and analysis tools are needed to aid in the decision making process. In this paper we introduce the concept of a simulation-based sustainability toolkit and present a prototype of one portion of the toolkit that is being developed for modeling and simulating sustainability aspects of logistics and transportation systems. The sustainability simulation toolkit contains a flexible framework to enable the simulation modeling and analysis of sustainability related factors and performance measures. This toolkit is designed for ease of implementation, so decision-makers can have sustainability measures as readily available as traditional performance measures when making logistics and transportation decisions.

An Agent-Based Simulation Model for the Market Diffusion of a Second Generation Biofuel
Elmar Kiesling, Markus Guenther, Christian Stummer, and Lea M. Wakolbinger (University of Vienna)

Abstract:
Second generation biofuels are widely considered a promising energy alternative to conventional (fossil) fuels. Although they will not completely replace fossil fuels (e.g., due to the limited availability of biomass), these high-quality biofuels can contribute to reducing emissions and strengthening a country's energy autonomy. In Austria, a team at the Vienna University of Technology is developing a biomass-to-liquid technology based on the Fischer-Tropsch synthesis. While the remaining technical obstacles are expected to be overcome in due time, the market introduction of the novel biofuel also requires substantial investments. In this context, we developed an agent-based simulation model that can provide potential investors with forecasts for the biofuel's market diffusion. This paper describes the model and presents simulation results.

Optimal Generation Expansion Planning Via the Cross-Entropy Method
Rishabh P. Kothari (Stanford University) and Dirk P. Kroese (University of Queensland)

Abstract:
The Generation Expansion Planning~(GEP) problem is a highly constrained, large-scale, mixed integer nonlinear programming problem. The objective of the GEP problem is to evaluate the least cost investment plan for addition of power generating units over a planning period subject to demand, availability, and security constraints. In this paper, a GEP model is presented and the Cross-Entropy (CE) optimization method is developed to solve the problem. The CE method is an effective algorithm for solving large combinatorial optimization problems. The main advantage of the CE method over other metaheuristic techniques is that it does not require decomposition of the problem into a master problem and operation subproblems, greatly reducing the computational complexity. This method also provides a fast and reliable convergence to the optimal solution.

A Monte Carlo Knowledge Gradient Method for Learning Abatement Potential of Emissions Reduction Technologies
Ilya O Ryzhov and Warren Powell (Princeton University)

Abstract:
Suppose that we have a set of emissions reduction technologies whose greenhouse gas abatement potential is unknown, and we wish to find an optimal portfolio (subset) of these technologies. Due to the interaction between technologies, the effectiveness of a portfolio can only be observed through expensive field implementations. We view this problem as an online optimal learning problem with correlated prior beliefs, where the performance of a portfolio of technologies in one project is used to guide choices for future projects. Given the large number of potential portfolios, we propose a learning policy which uses Monte Carlo sampling to narrow down the choice set to a relatively small number of promising portfolios, and then applies a one-period look-ahead approach using knowledge gradients to choose among this reduced set. We present experimental evidence that this policy is competitive against other online learning policies that consider the entire choice set.

Analysis of Wind Penetration and Network Reliability Through Monte Carlo Simulation
Lindsay Anderson (Cornell University) and Judith Cardell (Smith College)

Abstract:
Generating electricity from wind resources has many environmental and economic advantages over traditional fossil-fueled generation. As a result, there is little doubt that energy from wind will be a significant contribution to the electricity portfolio of the future. Due to the sensitivity of the network and the volatility of the wind resource, analysis of power system operations using expected wind generation is not representative of actual system operations. In order to account for this fundamental uncertainty in wind generation, a Monte Carlo simulation model is developed based on an Optimal Power Flow model, and tested on the IEEE 39-bus test system. The results of these simulations indicate that while the average cost of serving load decreases with increasing wind penetration, the reliability of the system is likely to be highly sensitive to the ability of other generators on the system to ramp production either up or down on short timescales.

Hybrid Simulation and Optimization-based Capacity Planner for Integrated Photovoltaic Generation with Storage Units
Esfandyar Mazhari, Jiayun Zhao, Nurcin Celik, Seungho Lee, Young-Jun Son, and Larry Head (The University of Arizona)

Abstract:
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.

A Simulation Solution of the Integration of Wind Power Into an Electricity Generating Network
Thomas Brady (Purdue University North Central)

Abstract:
To effectively harness the power of wind electricity generation, significant infrastructure challenges exist. First, the individ-ual wind turbines must be sited and constructed as part of a wind farm. Second, the wind farm must be connected to the electricity grid infrastructure and the power generated managed accordingly. Due to its stochastic nature, wind energy cannot be controlled; it must be managed. The integration and management of wind power within the highly complex, interconnected electricity infrastructure of the United States presents numerous policy making and decision analysis scenarios. The purpose of this paper is to develop a simulation framework for analyzing the integration of wind power into the generation system of a utility company. An analysis of public domain wind data is presented and specifics of how to transform this data into a compact, efficient representation suitable for simulation modeling is presented. Finally, performance metrics relative to wind effects are discussed.

Simulating the Effect on the Energy Efficiency of Smart Grid Technologies
Albert Molderink, Maurice G.C. Bosman, Vincent Bakker, Johann L. Hurink, and Gerard J.M. Smit (University of Twente)

Abstract:
The awareness of the greenhousegas effect and rising energy prices lead to initiatives to improve energy efficiency. These initiatives range from micro-generation, energy storage and efficient appliances to controllers with optimization objectives. Although these technologies are promising, their introduction may rise further questions. The implementation of such initiatives may have a severe impact on the electricity infrastructure. If several of these initiatives are introduced in a combined way, it is difficult to analyse their overall impact. In this paper a model is defined and a developed simulator is described to analyse the impact of different combinations of micro-generators, energy buffers, appliances and control algorithms on the energy efficiency, both within the house and on larger scale. The simulator is easily adaptable to new types of micro-generators, controllers and other supported devices. Simulation of two case studies with the simulator shows that the achieved results are promising.

Macro-System Model: A Federated Object Model for Cross-Cutting Analysis of Hydrogen Production, Delivery, Consumption and Associated Emissions
Mark Ruth and Victor Diakov (NREL) and Michael E. Goldsby and Timothy J. Sa (Sandia National Laboratory)

Abstract:
The introduction of hydrogen as an energy carrier for light-duty vehicles involves concomitant technological progress in several directions, such as production, delivery, consumption and related emissions. To analyze each of these, a suite of corresponding models have been developed by the DOE, involving inputs from several national laboratories. The macro-system model (MSM) is being developed as a cross-cutting analysis tool which combines a set of hydrogen technology analysis models. Within the MSM, federated simulation framework is used for consistent data transfer between the component models. The framework is built to suit cross-model as well as cross-platform data exchange and will involve features of �over-the-net� computation.

Development of a 25MW Geothermal Power Pant Full Scope Simulator Based on a Control System Graphical Modeling
Guillermo Romero-Jimenez, Fernando Fermin Jimenez-Fraustro, and Jose Antonio Tavira-Mondragon (Instituto De Investigaciones Electricas) and Heriberto Avalos-Valenzuela (Comision Federal De Electricidad)

Abstract:
This paper describes the development of an Operator Training Simulator (OTS) for the Geothermal Training Simulation Center (GTSC) of the Mexico�s Federal Commission of Electricity (CFE). The Department of Simulation (DS) of the Electrical Research Institute (IIE) developed this simulator using as reference the Unit IV of the Geothermal Power Plant Cerro Prieto, located at the north of Mexico. The Cerro Prieto 25MW simulator was finished and delivered to GTSC in June 2007. This simulator is a high-fidelity real time dynamic simulator built and tested for accurate operation over the entire load range. The Cerro Prieto-IV simulator is a replica of the real power plant thanks to main characteristics of the Graphical Model of the Distributed Control System (DCS), and was used primarily for operator training although it has been used for procedure development and evaluation of plant transients.

Duopoly Electricity Markets with Accurate and Inaccurate Market Goals
Zhi Zhou, Wai Kin (Victor) Chan, and Joe H Chow (Rensselaer Polytechnic Institute) and Serhiy Kotsan (New York Independent System Operator)

Abstract:
Electricity markets are complex systems due to their deregulation and restructuring. We develop an agent-based simulation model for a stylized electricity pool market and simulate the market as a repeated game. An online hill climbing with adjustment algorithm is applied to generator agents to guide them to bid strategically to reach their expected market share. It is observed that accurate (or genial) expected market goals lead to collusive behavior of generator agents with an equilibrium where their total profit is maximized. On the other hand, it is also found that inaccurate (or malicious) market goals could result in price war with an equilibrium where their profits are minimized.