Bi-Level Optimization for Electricity Transaction in Smart Community With Modular Pump Hydro Storage

Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.

Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios

Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.

Design and Development of Hardware to Analyze and Categorize the Condition of Batteries With the Aim of Enabling Their Re-Use

This paper covers the design and implementation of an accurate, yet flexible test system for Lithium-Ion batteries. The system makes use of linear charge and discharge circuitry to ensure a low noise control, and can support the simultaneous and independent testing of six cells. The system is controlled and data collected by specialist MatLab© software with a user-friendly GUI. Experimental data is processed within the same environment to obtain the desired information. The system makes use of Full Cycle Coulomb Counting and Pulsed DC Load Analysis to obtain estimates for the State of Health (SoH) and State of Charge (SoC) of various cells, and to examine the effect of different use cases on cell performance through repeated testing.

Switching From Petroleum- to Bio-Based Plastics: Visualization Tools to Screen Sustainable Material Alternatives During the Design Process

The recent development in technology has made bio-based plastics an increasingly attractive alternative to petroleum-based plastics to tackle plastic pollution. However, currently, bio-based plastics have not been widely adopted in the design and manufacturing of new products. To advocate the use of bio-based plastics, this paper proposes two visualization-based tools to educate designers and engineers about the availabilities and the properties of different bio-based plastics. After analyzing the literature on visual tools for sustainable design and material selection, two new prototype tools for screening bio-plastic alternatives are designed with the advice and support of the engineers of a major U.S. manufacturer of agricultural equipment. Surveys and focus groups with the manufacturer’s engineers are conducted to improve the tools, and a first case study is completed to examine their usefulness.

Alignment of a Collaborative Resistance Model With a Change Management Process in Industry: A Case Study on Production Automation

Current research and literature lack the discussion of how production automation is introduced to existing lines from the perspective of change management. This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen semi-structured interviews of eight engineers from three functional backgrounds, a process model was created to understand how the company of study introduced a new automation system into their existing production line, while also noting obstacles identified in the process. This process model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in literature to understand critical elements found within change management. The process that was revealed in the case study appeared to contain some elements of a design process as compared to traditional change management processes found in literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the objective analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the process.

Monitoring Method for Laser Via Hole Processing of Printed Circuit Boards Based on Two-Color Method With a High-Speed Video Camera

A build-up process is used to manufacture printed wiring boards (PWBs) for high-density circuits. Presently, CO2 laser beams are used to drill blind via holes (BVHs) that connect copper foils. The Cu-direct drilling process has received considerable attention but is problematic because it produces a copper overhang due to the complex processing phenomena. This report focuses on monitoring scattered matter by Cu-direct laser drilling with a high-speed camera and clarifying the factors related to processing quality while verifying the results by CFD (Computational Fluid Dinamics) analysis. Previous research has shown that processing progress can be made from temperature information using the two-color image method that can measure temperature without contact. However, the two-color image method generates noise in the temperature range (500–3000 °C) which is treated in this research. Filtering was possible by using the RGB data of each pixel on the image. By focusing on laser fluence, it became possible to estimate the laser irradiation time that can guarantee the quality in the drilled hole (BVH) in single pulse continuous irradiation.

Milling Simulation-Based Method to Evaluate Manufacturability of Machine Parts

The designers of mechanical products are generally not experts in machining. Therefore, they often design parts with inherent machining difficulties. Although various design for manufacturability tools have been developed to avoid such problems, their use in practice remains limited due to their lack of versatility. We develop a novel piece of software that can automatically detect difficult-to-machine shapes in a part. Using this software, the designer can determine which shapes are difficult to produce using conventional cutting by themselves, and can modify the shape on the spot. In the Internet-based part manufacturing business, the same software can be used to check whether the given part can be produced using the standard milling operations predetermined in a company. Our system is based on “milling simulation”. It detects any shapes that cannot be produced using the prepared cutting tools by executing the milling simulations with the tools, and then visualizing shapes that remain unmachined after all simulations. In this study, the acceleration of the processing is realized using graphics processing unit technology, and it is possible to extract difficult-to-machine shapes in several minutes using a standard PC.

Injury Prevention by Design: Measuring Greenhouse Worker Social Sustainability for Redesigned Equipment

The greenhouse industry is a multibillion-dollar sector of U.S. agricultural production. Greenhouse workers often experience hazardous working conditions placing them at risk for injury. These injuries include but are not limited to mechanized operations causing machine and tool related injuries, on-site shipping and loading practices placing excessive strain on a worker’s body, working from height leading to slips and falls, and a strenuous indoor working environment exceeding workers’ physical capabilities. This project focused on identifying greenhouse worker injury trends using workers’ compensation data from the Midwest region and observing and interviewing workers at one specific greenhouse company host site. Physical exertion, lifting and handling, and falls were all high value workers’ compensation problems for Midwestern regional greenhouses. A new piece of equipment and process was designed to prevent worker injury identified within the host site. The baseline risk from the original equipment was compared to the new equipment using a newly proposed indicator of social sustainability based on a validated safety professional tool, the risk assessment matrix (RAM), was utilized. The RAM found a reduction in risk between the original and new equipment. The new equipment design and process exhibited improvement in six out of the eleven hazards identified in the RAM. These improvements addressed lifting and handling concerns. Combining workers’ compensation data analysis, on-site observation, and worker interviews together was an effective method to rapidly deploy and design safer and thus more socially sustainable equipment for greenhouse workers.

Environmental and Economic Impacts of Nitrogen Trifluoride at an Idaho Semiconductor Facility

Nitrogen Trifluoride (NF3) is an inorganic compound widely used in the electronics industry for manufacturing various products, such as semiconductors, solar panels, and touch screens. However, NF3 emissions that accumulate in the atmosphere have 17,200 times the global warming potentials of CO2 over a 100-year time horizon. The abatement efficiency for NF3 is high, although some amount of NOx is generated. This study aims to provide economic and environmental impact assessments on the use of NF3. Life cycle assessment method is applied for evaluating environmental aspects. Additionally, a real case study for the Idaho semiconductor facility is used to assess the method and demonstrate the application of this study. The results show that slight reductions in abatement efficiency have dramatic impacts on mitigating greenhouse gas (GHG) emissions. Even small unplanned releases of NF3, either through mechanical failure or human error, have significant environmental impacts, and every reasonable effort should be taken to avoid such incidents. The results also indicate that the cost of abatement is the primary driver of economic impact. It is found that the considerable distance from the chemical plant in Arizona to the point of use in Idaho contributed a minor portion of GHGs associated with the use of NF3. It is also found that contaminants (e.g., SF6 and CF4) within the cylinder potentially have more substantial GHG impacts than the actual NF3, owing in part to the relative ease with which NF3 is destroyed through abatement.

Decision-Based Design Method for Computing Marginal Cost of Durability

Product durability impacts both the environment and the economy. Companies are changing their business models to the circular economy. In this model, the ownership of the product remains with the manufacturer. With this new paradigm, determining the life of the product becomes even more important for the success of the business model. The metric defined as the Marginal Cost of Durability (MCD) determines the cost to increase or decrease the life of the system. For a system to last longer, more materials are needed to counteract the fatigue damage. While this metric has been defined and used in studies throughout the literature, there is a need for a formal method of collecting this data. This paper presents a novel method for measuring the MCD aided by Metamodel-Based Optimization. A case study is presented to demonstrate this method when applied to a wind turbine tower. The results indicate that there is an increasing linear relationship between life and cost. A wind turbine tower designed for 80 years has 34% more mass and cost than a 20-year design.