Matching Items (12)

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FROM SUBSISTENCE TO SURPLUS: HELPING FARMERS IN RURAL PERU TO INCREASE CROP PRODUCTION BY IMPROVING SOILS

Description

"Seventy five percent of the world's poor live in rural areas of developing countries, where most people's livelihoods rely directly on agriculture." (USAid, 2014) Reduced levels of crop production and

"Seventy five percent of the world's poor live in rural areas of developing countries, where most people's livelihoods rely directly on agriculture." (USAid, 2014) Reduced levels of crop production and the accompanying problems of malnourishment exist all over the world. In rural Peru, for example, 11 percent of the population is malnourished. (Global Healthfacts.org, 2012) Since the success in agriculture relies importantly on the fertility of the soil, it is imperative that any efforts at reversing this trend be primarily directed at improving the existing soils. This, in turn, will increase crop yields, and if done properly, will also conserve natural resources and maximize profits for farmers. In order to improve the lives of those at the bottom of the pyramid through agriculture, certain tools and knowledge must be provided in order to empower such persons to help themselves. An ancient method of soil improvement, known as Terra Preta do Indio (Indian dark earth), was discovered by Anthropologists in the 1800's. These dark, carbon-rich, soils are notable for their high fertility, high amounts of plant available nutrients, and their high moisture retention rates. The key to their long-lasting fertility and durability is the presence of high levels of biochar, a highly stable organic carbon \u2014 produced when organic matter (crop residues, food waste, manure, etc.) is burned at low temperatures in the absence of oxygen. Research has shown that when charcoal (biochar) and fertilizers are combined, it can yield as much as 880 percent more than when fertilizers are used by themselves. (Steiner, University of Bayreuth, 2004)

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Date Created
  • 2014-12

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MEASURING AIR QUALITY USING WIRELESS SELF-POWERED DEVICES

Description

High concentrations of carbon monoxide and particulate matter can cause respiratory disease, illness, and death in high doses. Air pollution is a concern in many urban areas of emerging markets

High concentrations of carbon monoxide and particulate matter can cause respiratory disease, illness, and death in high doses. Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the high prevalence of products of incomplete combustion resulting from open fires for cooking and heating. Monitoring air quality is an essential step to identifying these and other factors that affect air quality, and thereafter informing engineering and policy decisions to improve the quality of air. This study seeks to measure changes in air quality across spatial and temporal domains, with a specific focus on microclimates within an urban area. A prototype, low-cost air quality monitoring device has been developed to measure the concentrations of particulate matter, ozone, and carbon monoxide multiple times per minute. The device communicates data wirelessly via cell towers, and can run off-grid using a solar PV-battery system. The device can be replicated and deployed across urban regions for high-fidelity emissions monitoring to explore the effect of anthropogenic and environmental factors on intra-hour air quality. Hardware and software used in the device is described, and the wireless data communication protocols and capabilities are discussed.

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Date Created
  • 2015-05

Simulating Interdependent Infrastructure Vulnerability to Extreme Weather Events

Description

The objective of the research was to simulate interdependencies between municipal water-power distribution systems in a theoretical section of the Phoenix urban environment that had variable population density and highest

The objective of the research was to simulate interdependencies between municipal water-power distribution systems in a theoretical section of the Phoenix urban environment that had variable population density and highest ambient temperature. Real-time simulations were run using the Resilient Infrastructure Simulation Environment (RISE) software developed by Laboratory for Energy and Power Solutions (LEAPS) at ASU. The simulations were run at estimated population density to simulate urbanism, and temperature conditions to simulate increased urban heat island effect of Phoenix at 2020, 2040, 2060, and 2080 using the IEEE 13 bus test case were developed. The water model was simulated by extrapolated projections of increased population from the city of Phoenix census data. The goal of the simulations was that they could be used to observe the critical combination of system factors that lead to cascading failures and overloads across the interconnected system. Furthermore, a Resilient Infrastructure Simulation Environment (RISE) user manual was developed and contains an introduction to RISE and how it works, 2 chapters detailing the components of power and water systems, respectively, and a final section describing the RISE GUI as a user. The user manual allows prospective users, such as utility operators or other stakeholders, to familiarize themselves with both systems and explore consequences of altering system properties in RISE by themselves. Parts of the RISE User Manual were used in the online "help" guide on the RISE webpage.

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Date Created
  • 2018-12

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Burkina Faso Hospital Microgrid Case Study

Description

This paper analyzes Burkina Faso’s Souro Sanou University Hospital Center’s energy needs and discusses whether or not solar panels are a good investment. This paper also discusses a way to

This paper analyzes Burkina Faso’s Souro Sanou University Hospital Center’s energy needs and discusses whether or not solar panels are a good investment. This paper also discusses a way to limit the damage caused by power outages. The hospital has a history of problems with power outages; in the summer they have power outages every other day lasting between one to four hours, and in the rainy season they have outages once every other week lasting the same amount of time.
The first step in this analysis was collecting relevant data which includes: location, electricity rates, energy consumption, and existing assets. The data was entered into a program called HOMER. HOMER is a program which analyzes an electrical system and determines the best configuration and usage of assets to get the lowest levelized cost of energy (LCOE). In HOMER, five different analyses were performed. They reviewed the hospital’s energy usage over 25 years: the current situation, one of the current situation with added solar panels, and another where the solar panels have single axis tracking. The other two analyses created incentives to have more solar panels, one situation with net metering, and one with a sellback rate of 0.03 $/kWh. The result of the analysis concluded that the ideal situation would have solar panels with a capacity of 300 kW, and the LCOE in this situation will be 0.153 $/kWh. The analysis shows that investing in solar panels will save the hospital approximately $65,500 per year, but the initial investment of $910,000 only allows for a total savings of $61,253 over the life of the project. The analysis also shows that if the electricity company, Sonabel, eventually buys back electricity then net metering would be more profitable than reselling electricity for the hospital.
Solar panels will help the hospital save money over time, but they will not stop power outages from happening at the hospital. For the outages to stop affecting the hospital’s operations they will have to invest in an uninterrupted power supply (UPS). The UPS will power the hospital for the time between when the power goes out and when their generators are turning on which makes it an essential investment. This will stop outages from affecting the hospital, and if the power goes out during the day then the solar panels can help supplement the energy production which will take some of the strain from their generators.
The results of this study will be sent to officials at the hospital and they can decide if the large initial investment justifies the savings. If the solar panels and UPS can save one life, then maybe the large initial investment is worth it.

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Date Created
  • 2019-05

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Model predictive control for resilient operation of hybrid microgrids

Description

This dissertation develops advanced controls for distributed energy systems and evaluates performance on technical and economic benefits. Microgrids and thermal systems are of primary focus with applications shown for residential,

This dissertation develops advanced controls for distributed energy systems and evaluates performance on technical and economic benefits. Microgrids and thermal systems are of primary focus with applications shown for residential, commercial, and military applications that have differing equipment, rate structures, and objectives. Controls development for residential energy heating and cooling systems implement adaptive precooling strategies and thermal energy storage, with comparisons made of each approach separately and then together with precooling and thermal energy storage. Case studies show on-peak demand and annual energy related expenses can be reduced by up to 75.6% and 23.5%, respectively, for a Building America B10 Benchmark home in Phoenix Arizona, Los Angeles California, and Kona Hawaii. Microgrids for commercial applications follow after with increased complexity. Three control methods are developed and compared including a baseline logic-based control, model predictive control, and model predictive control with ancillary service control algorithms. Case studies show that a microgrid consisting of 326 kW solar PV, 634 kW/ 634 kWh battery, and a 350 kW diesel generator can reduce on-peak demand and annual energy related expenses by 82.2% and 44.1%, respectively. Findings also show that employing a model predictive control algorithm with ancillary services can reduce operating expenses by 23.5% when compared to a logic-based algorithm. Microgrid evaluation continues with an investigation of off-grid operation and resilience for military applications. A statistical model is developed to evaluate the survivability (i.e. probability to meet critical load during an islanding event) to serve critical load out to 7 days of grid outage. Case studies compare the resilience of a generator-only microgrid consisting of 5,250 kW in generators and hybrid microgrid consisting of 2,250 kW generators, 3,450 kW / 13,800 kWh storage, and 16,479 kW solar photovoltaics. Findings show that the hybrid microgrid improves survivability by 10.0% and decreases fuel consumption by 47.8% over a 168-hour islanding event when compared to a generator-only microgrid under nominal conditions. Findings in this dissertation can increase the adoption of reliable, low cost, and low carbon distributed energy systems by improving the operational capabilities and economic benefits to a variety of customers and utilities.

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Created

Date Created
  • 2019

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Modeling and Large Signal Stability Analysis of A DC/AC Microgrid

Description

The concept of the microgrid is widely studied and explored in both academic and industrial societies. The microgrid is a power system with distributed generations and loads, which is intentionally

The concept of the microgrid is widely studied and explored in both academic and industrial societies. The microgrid is a power system with distributed generations and loads, which is intentionally planned and can be disconnected from the main utility grid. Nowadays, various distributed power generations (wind resource, photovoltaic resource, etc.) are emerging to be significant power sources of the microgrid.

This thesis focuses on the system structure of Photovoltaics (PV)-dominated microgrid, precisely modeling and stability analysis of the specific system. The grid-connected mode microgrid is considered, and system control objectives are: PV panel is working at the maximum power point (MPP), the DC link voltage is regulated at a desired value, and the grid side current is also controlled in phase with grid voltage. To simulate the real circuits of the whole system with high fidelity instead of doing real experiments, PLECS software is applied to construct the detailed model in chapter 2. Meanwhile, a Simulink mathematical model of the microgrid system is developed in chapter 3 for faster simulation and energy management analysis. Simulation results of both the PLECS model and Simulink model are matched with the expectations. Next chapter talks about state space models of different power stages for stability analysis utilization. Finally, the large signal stability analysis of a grid-connected inverter, which is based on cascaded control of both DC link voltage and grid side current is discussed. The large signal stability analysis presented in this thesis is mainly focused on the impact of the inductor and capacitor capacity and the controller parameters on the DC link stability region. A dynamic model with the cascaded control logic is proposed. One Lyapunov large-signal stability analysis tool is applied to derive the domain of attraction, which is the asymptotic stability region. Results show that both the DC side capacitor and the inductor of grid side filter can significantly influence the stability region of the DC link voltage. PLECS simulation models developed for the microgrid system are applied to verify the stability regions estimated from the Lyapunov large signal analysis method.

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Agent

Created

Date Created
  • 2018

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Self-organizing Coordination of Multi-Agent Microgrid Networks

Description

This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations

This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.

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Created

Date Created
  • 2019

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Optimal Scheduling of Home Energy Management System with Plug-in Electric Vehicles Using Model Predictive Control

Description

With the growing penetration of plug-in electric vehicles (PEVs), the impact of the PEV charging brought to the utility grid draws more and more attention. This thesis focused on the

With the growing penetration of plug-in electric vehicles (PEVs), the impact of the PEV charging brought to the utility grid draws more and more attention. This thesis focused on the optimization of a home energy management system (HEMS) with the presence of PEVs. For a household microgrid with photovoltaic (PV) panels and PEVs, a HEMS using model predictive control (MPC) is designed to achieve the optimal PEV charging. Soft electric loads and an energy storage system (ESS) are also considered in the optimization of PEV charging in the MPC framework. The MPC is solved through mixed-integer linear programming (MILP) by considering the relationship of energy flows in the optimization problem. Through the simulation results, the performance of optimization results under various electricity price plans is evaluated. The influences of PV capacities on the optimization results of electricity cost are also discussed. Furthermore, the hardware development of a microgrid prototype is also described in this thesis.

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Created

Date Created
  • 2018

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Electric Power Infrastructure Vulnerabilities to Heat Waves from Climate Change

Description

Electricity infrastructure vulnerabilities were assessed for future heat waves due to climate change. Critical processes and component relationships were identified and characterized with consideration for the terminal event of service

Electricity infrastructure vulnerabilities were assessed for future heat waves due to climate change. Critical processes and component relationships were identified and characterized with consideration for the terminal event of service outages, including cascading failures in transmission-level components that can result in blackouts. The most critical dependency identified was the increase in peak electricity demand with higher air temperatures. Historical and future air temperatures were characterized within and across Los Angeles County, California (LAC) and Maricopa County (Phoenix), Arizona. LAC was identified as more vulnerable to heat waves than Phoenix due to a wider distribution of historical temperatures. Two approaches were developed to estimate peak demand based on air temperatures, a top-down statistical model and bottom-up spatial building energy model. Both approaches yielded similar results, in that peak demand should increase sub-linearly at temperatures above 40°C (104 °F) due to saturation in the coincidence of air conditioning (AC) duty cycles. Spatial projections for peak demand were developed for LAC to 2060 considering potential changes in population, building type, building efficiency, AC penetration, appliance efficiency, and air temperatures due climate change. These projections were spatially allocated to delivery system components (generation, transmission lines, and substations) to consider their vulnerability in terms of thermal de-rated capacity and weather adjusted load factor (load divided by capacity). Peak hour electricity demand was projected to increase in residential and commercial sectors by 0.2–6.5 GW (2–51%) by 2060. All grid components, except those near Santa Monica Beach, were projected to experience 2–20% capacity loss due to air temperatures exceeding 40 °C (104 °F). Based on scenario projections, and substation load factors for Southern California Edison (SCE), SCE will require 848—6,724 MW (4-32%) of additional substation capacity or peak shaving in its LAC service territories by 2060 to meet additional demand associated with population growth projections.

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Created

Date Created
  • 2018

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Diffuse radiation calculation methods

Description

Measuring and estimating solar resource availability is critical for assessing new sites for solar energy generation. This includes beam radiation, diffuse radiation, and total incident radiation. Total incident radiation is

Measuring and estimating solar resource availability is critical for assessing new sites for solar energy generation. This includes beam radiation, diffuse radiation, and total incident radiation. Total incident radiation is pertinent to solar photovoltaic (PV) output and low-temperature solar thermal applications whereas beam radiation is used for concentrating solar power (CSP). Global horizontal insolation (GHI) data are most commonly available of any solar radiation measurement, yet these data cannot be directly applied to solar power generator estimation because solar PV panels and solar CSP collectors are not parallel to the earth’s surface. In absence of additional measured data, GHI data may be broken down into its constituent parts—diffuse radiation and beam radiation—using statistical techniques that incorporate explanatory variables such as the clearness index. This study provides a suite of methods and regression models to estimate diffuse radiation as a function of various explanatory variables using both piecewise and continuous fits. Regression analyses using the clearness index are completed for seven locations in the United States and four locations in other regions of the world. The multi-site analysis indicates that models developed using training data for a single location perform best in that location, yet general models can be created that perform reasonably well across any locality and then applied to estimate solar resource availability in new locations around the world. Results from the global and site-specific models perform better than the existing models in literature and indicate that models perform different in different sky conditions e.g. clear or cloudy sky. Results also show that continuous models perform equivalent or better than the piecewise models. Newly generated piecewise models showed improvement over some intervals in the clearness index. A combination of fits from this study and existing literature was used to improve overall performance of modeling techniques used in diffuse radiation estimation. Germany was selected for more detailed studies of a single case study using the clearness index, ambient temperature, relative humidity, and absolute humidity as explanatory variables. Clearness index is the most important variable for diffuse radiation calculation whereas the relative humidity and the temperature are the secondary variable for improving calculation. Absolute humidity plays similar role as temperature in improving the calculation on the other hand relative humidity improves it very slightly over the absolute humidity and temperature.

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Agent

Created

Date Created
  • 2016