A Multilevel Optimization Method for the Design and Operation of Stand-Alone Hybrid Renewable Energy Systems for Multiple Remote Communities

Recently, there has been increased interest in designing stand-alone Hybrid Renewable Energy Systems (HRES) for remote communities. Several methodologies have been proposed to tackle the design optimization problem, to develop strategies for optimal operation/dispatch, or to address both problems concurrently. So far, however, these methods have been developed only for specific communities or system configurations (e.g., wind-diesel; PV-diesel). In this study, we propose a multilevel design optimization method that considers both optimal component selection and dispatch strategy that can be applied to any community regardless of the available renewable resources, thus overcoming the limitations of previous studies. The new approach considers a wide range of renewable and non-renewable energy technologies, a database of commercially available components, and leverages state-of-the-art methods for solving each optimization subproblem. The novel algorithm was evaluated with a set of meteorological conditions that emulate different remote communities. In addition, two pricing scenarios for diesel are studied to explore how the HRES design is influenced by this parameter.

Comparing High-Performance Computing Techniques for Modeling Structural Impact on Battery Cells

In this study, we have investigated feasibility of using commercial explicit finite element code LS-DYNA on massively parallel super-computing cluster for accurate modeling of structural impact on battery cells. Physical and numerical lateral impact tests have been conducted on cylindrical cells using a flat rigid drop cart in a custom-built drop test apparatus. The main component of cylindrical cell, jellyroll, is a layered spiral structure which consists of thin layers of electrodes and separator. Two numerical approaches were considered: (1) homogenized model of the cell and (2) heterogeneous (full) 3-D cell model. In the first approach, the jellyroll was considered as a homogeneous material with an effective stress-strain curve obtained through experiments. In the second model, individual layers of anode, cathode and separator were accounted for in the model, leading to extremely complex and computationally expensive finite element model. To overcome limitations of desktop computers, high-performance computing (HPC) techniques on a HPC cluster were needed in order to get the results of transient simulations in a reasonable solution time. We have compared two HPC methods used for this model is shared memory parallel processing (SMP) and massively parallel processing (MPP). Both the homogeneous and the heterogeneous models were considered for parallel simulations utilizing different number of computational nodes and cores and the performance of these models was compared. This work brings us one step closer to accurate modeling of structural impact on the entire battery pack that consists of thousands of cells.

Optimal Design of IC Engine Cooling Fins by Using Genetic Algorithm

The analysis for optimal design of an air-cooled internal combustion engine cooling fin array by using genetic algorithms (GA) is presented in this study. Genetic Algorithms are robust, stochastic search techniques which are also used for optimizing highly complex problems. In this study, the fin array is of the traditional circular fin type, which is subject to ambient convective heat transfer. The parameters (degrees of freedom) selected for the analysis include the cylinder wall thickness-to-radius ratio, fin thickness, fin length, the number of fins, and the local heat transfer coefficient. By using a single objective GA procedure, the heat transfer through the fin arrays is set as the objective function to be optimized with each parameter varied within the physical ranges. Proper population size is selected and the mutations, cross-over and selection are conducted in the GA procedure to arrive at the optimal set of parameters after a certain number of generations. The GA proves to be an effective optimization method in the thermal system component designs when the number of independent variables is large.

Mass Engine Cycle

A new thermodynamic cycle is proposed named mass engine cycle. In the proposed cycle, mass is transferred from a high mass concentration reservoir to the cycle, mass is rejected to a low mass concentration reservoir, and a net positive work is generated. This is similar to heat engine cycles where heat is transferred from a high temperature thermal reservoir (heat source) to the cycle; heat is rejected to a low temperature thermal reservoir (heat sink), and a net positive work is generated. The heat engine cycle uses heat exchangers to transfer heat between the cycle and the thermal reservoirs, while the mass engine cycle uses membrane mass exchangers which transfer mass between the cycle and the mass reservoir. These membrane mass exchangers transfer water through a semi-permeable membrane and reject other substances. The driving force for the mass transfer is the hydrostatic and osmotic pressure differences. Similar to Carnot limit of the thermal efficiency of the heat engine cycle, a theoretical limit is obtained for the proposed mass engine cycle under reversible thermodynamic conditions.

Thermodynamic Analysis and Working Fluid Optimization of a Combined ORC-VCC System Using Waste Heat From a Marine Diesel Engine

This paper examines through a thermodynamic analysis the feasibility of using waste heat from marine Diesel engines to drive a vapor compression refrigeration system. Several working fluids including propane, butane, isobutane and propylene are considered. Results showed that isobutane and Butane yield the highest performance, whereas propane and propylene yield negligible improvement compared to R134a for operating conditions considered.

A Web-Accessible Robotics Monitoring System Powered by a Thermoelectric Generator Connected to a Battery

In small source power generation scenarios in industrial or remote settings a viable small electrical supply for security and monitoring systems is often problematic due to the variability of the energy sources and the stability of the power generated. These small scale systems lack the advantages of a larger power grid. Therefore peak power requirements can be beyond the power generator necessitating energy storage such as batteries. The authors have developed and documented a reliable thermoelectric generator and a test bed. The generator was combined with a battery in order to meet peak power requirements beyond the unassisted range of the generator. This paper presents a test case result with the thermoelectric generator powering a complete web accessible mobile robot system. The robot system can be used for monitoring, physical manipulation of the environment, routine maintenance and in emergencies.

Representing the Water-Energy Nexus With Decision Matrices

The global water nexus is still in the formative stages as a area of study. The needs are mostly clear: people need adequate water for drinking, for growing food, for cooling steam-based power plants, and for sustaining the natural habitats that keep the carbon and hydrologic cycles functioning properly. What has emerged is a growing awareness of how finite the earth’s water resources are and how this creates a complex set of interconnected challenges in both developed and developing nations. What has also emerged are predictions with increasing urgency for water and energy crises in the next 20–50 years, especially if these concerns are left unaddressed. The Water-Nexus is not new, but its emerging importance now is driven primarily by population growth, climate change, and our growing awareness of societal impact on ecosystems. Providing energy for buildings, homes, and transportation is an increasingly difficult task for the growing population and aging infrastructure. Most individual issues within the Water-Energy Nexus are fairly well known with quantifiable water impacts. What is lacking is a clear representation of the Nexus relationships that show how changes in one sector impact another. What is needed is a compact way to represent the interrelationships that provide both insight and perspective on how much influence one proposed change has compared to another. Such an understanding should surface the most strategic, viable methods for simultaneously meeting water and energy needs while being a good steward of finances and natural resources. We propose the use of decision matrices from engineering design to represent the interconnected relationships that form the Water-Energy Nexus. The customers in this case are water-centric stakeholders such as government and corporate decision makers, educators, and water-oriented development agencies. Both quantitative and qualitative research methods are used to integrate the nexus topics into the decision matrix. Both positive and negative correlations in water impacts are indicated with their relative level of influence. Common units are used when possible to quantify water consumption or savings. Decision matrices are presented for transportation fuels and utility power generation. The transportation fuels matrix includes evaluation criteria for water impact, sustainability, convenience, emissions, public opinion, and geographic considerations. The utility power decision matrix has similar evaluation criteria except capacity factor is considered instead of convenience. These criteria are intended to aid policy makers in strategically navigating the legislative and policy generation process to emphasize or reduce emphasis on different fuel types. Recommendations are provided for strategic, viable methods to mitigate future effects of the Water-Energy crisis.

Characteristics of Water-in-Diesel Emulsions in a Single Cylinder Compression Ignition Engine

The objective of this project was to develop both engine performance and emission profiles for two test fuels — a 6% water-in-diesel oil emulsion (DOE-6) fuel and a neat diesel (D100) fuel. The testing was performed on a single cylinder, direct-injection, water-cooled diesel engine coupled to an eddy current dynamometer. Output parameters of the engine were used to calculate Brake Specific Fuel Consumption (BSFC) and Engine Efficiency (η) for each test fuel. DOE-6 fuels generated a 24% reduction in NOX and a 42% reduction in Carbon Monoxide emissions over the tested operating conditions. DOE-6 fuels presented higher ignition delays — between 1°-4°, yielded 1%–12% lower peak cylinder pressures and produced up to 5.5% lower exhaust temperatures. Brake Specific Fuel consumption increased by 6.6% for the DOE-6 fuels as compared to the D100 fuels. This project is the first research done by a New Zealand academic institution on water-in-diesel emulsion fuels.

Design Optimization of Batteryless Photovoltaic-Powered Reverse Osmosis Water Desalination in Remote Areas

Reverse osmosis (RO) is one of the main technologies for water desalination, which can be used in locations with water resources that have high salinity content (such as saline ground water or seawater) to produce fresh water. Energy requirement for RO is less than other desalination processes, but is in the form of electric power, which can be scarce as fresh water in in remote areas not connected to the grid. Fortunately, many areas with fresh water shortage due to lack of rainfall have abundant sunshine. The combination of solar power and RO desalination is attractive, but remote areas usually requires small modular units, which favors photovoltaic (PV) solar energy harvesting. It is important to consider the net cost-effectiveness of the system when designing the PV-RO desalination plant. Adding battery storage to a PV-RO system has the advantage of steadier operation, but is an additional cost whose real benefit is only realized with a larger PV array that can harvest more energy during daytime. This paper compares the net unit cost of fresh water for realistic scenarios of PV-RO systems with and without battery storage. A multi-level optimization approach previously developed by the authors for time-variant power PV-RO systems is adopted; a “sub-loop” optimization determines the operating pressure and flow rate given a fixed system configuration and instantaneous power input, while an “outer loop” optimizes the configuration of the desalination plant. The sub-loop optimization is done via an enumeration approach, while the outer loop is optimized via a mixed real-coded genetic algorithm (GA). A demonstration study shows a batteryless system being approx. 30% more expensive per unit fresh water production than a fully optimized battery-backed system. However, most of the cost of a batteryless system is in initial investment, which with 7% less annual operating cost, can present a plausible design choice for remote areas.

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like non-linear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like artificial neural networks, need a large number of experimental data. Both are difficult to gain. Support vector machine (SVM), a novel computational learning method with excellent performance, seems to be a good choice for gas path fault diagnostic of gas turbine without engine model. In this paper, SVM is employed to diagnose a deteriorated gas turbine. And the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data, and can be employed to gas path fault diagnostic of gas turbine. Additionally, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnostic based on small sample.