This paper presents a mathematical sizing algorithms of a grid-connected photovoltaic-battery system for a residential house. The objective is to minimize the total storage capacity with cost of electricity. The proposed methodology is based on a Linear and Non-linear programming. We have presents results from a existing PV panel FS-4115-3 for the given climatic conditions and the electricity use profile. Measurements for whole household electricity consumption have been obtained over a period of two months. They were all obtained at one hour interval. The algorithm jointly optimizes the sizes of the photovoltaic and the battery systems by adjusting the battery charge and discharge cycles according to the availability of solar resource and a time-of-use tariff structure for electricity. The results show that jointly optimizing the sizing of battery and photovoltaic systems can significantly reduce electricity imports and the cost of electricity for the household.

Magíster en Ciencias de la Ingeniería, Mención Eléctrica. Ingeniero Civil Eléctrico / La creciente penetración de energías de carácter variable en los últimos años permite prever que los sistemas eléctricos de potencia requerirán la incorporación de nuevas tecnologías que flexibilicen su operación. Entre estas tecnologías, una de las más importantes son los sistemas de almacenamiento basados en baterías (BESS) los que han demostrado tener la capacidad de entregar un amplio abanico de servicios a los SEP. En particular, en la bibliografía se menciona que los BESS tienen capacidad de aportar a la estabilidad transitoria de ángulo de los SEP, pero pocos trabajos profundizan en esto. Más aún, la incorporación de BESS en el sistema abre el problema de localizarlos óptimamente, lo que ha sido abordado principalmente desde perspectivas de operación económica, reducción de pérdidas o regulación de tensión. En este trabajo se estudia como la interacción de los BESS con los sistemas eléctricos permite mejorar su desempeño dinámico en términos de estabilidad transitoria. Comprendido esto, se diseña una metodología de localización óptima de módulos BESS, considerando criterios de estabilidad transitoria. El estudio demuestra que el intercambio de potencia de los BESS permite elevar la capacidad de transferencia de potencia activa de las máquinas del sistema durante una perturbación. Esto reduce la aceleración de los ángulos de rotor durante y después de la contingencia, aumentando la capacidad del sistema de mantener una operación estable. Se estudia además que este aporte del BESS se puede ver manifestado en el criterio de las áreas iguales, que determina el margen de estabilidad del sistema como la diferencia entre el área desacelerante y el área acelerante. Este margen de estabilidad para sistemas de varias máquinas se puede determinar según el equivalente SIME del sistema (single machine equivalent) el cual condensa la dinámica de los ángulos de todas las máquinas en la dinámica de una máquina equivalente. Por otra parte, el problema de localización óptima es un problema discreto, combinatorial, no lineal, por lo que se utilizan algoritmos genéticos (GA) para explorar el espacio de soluciones factibles. Si bien los GA no garantizan la obtención de optimalidad global, se ha demostrado que tienen una notable capacidad para encontrar soluciones cercanas al óptimo en tiempos de computo reducidos. Para evaluar el desempeño dinámico de una distribución de BESS, el algoritmo evalúa el margen de estabilidad del equivalente SIME del sistema. La metodología se implementa en el IEEE 39 bus system, modificado para representar un escenario de alta penetración renovable. La metodología permite la obtención de una distribución de módulos BESS que mejora el desempeño transitorio del sistema, focalizando el aporte de los BESS en torno a aquellas contingencias con peores desempeños dinámicos. Se comprueba además la capacidad del GA de encontrar buenas soluciones en tiempos de cómputos razonables, requiriendo la exploración de una ínfima porción del espacio de soluciones factibles para encontrar una buena distribución. Se verifica también la pertinencia del margen de estabilidad del equivalente SIME como una buena medida del desempeño dinámico del sistema.

The purpose of this study is to analyse how a battery energy storage system (BESS) can support the frequency and voltage stability for an islanded microgrid containing a hydropower plant. Two different microgrids, both situated in Sweden, are evaluated. Modelling and dynamic simulations are conducted in the PowerFactory tool. The result shows that both the frequency and the voltage control can be improved with the BESS. However, with the allowed limit of ± 1 Hz, not all simulated scenarios including a BESS meets the requirement. A large difference between the BESS and generator capacity might be a possible cause for this. By dividing the larger loads so that smaller loads are attained, the frequency deviation might be reduced. Furthermore, by adjusting the systems PID-parameters according to the island mode operation, faster regulation can be attained. The system operates according to the Master slave control strategy, with the hydropower being the master unit with voltage control and the BESS being a slave unit with PQ control. The ability to operate an islanded microgrid can ensure the supply of electricity to inhabitants and vital functions in society. By utilizing a BESS for increasing electric stability, emission of CO2 is indirectly mitigated. As cost for BESS are expected to decrease rapidly, they will be accessible for utilization all over the world.

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In deregulated electricity markets, profit driven electricity retailers compete to supply cheap reliable electricity to electricity consumers, and the electricity consumers have free will to switch between the electricity retailers. The need to maximize the profits of the electricity retailers while minimizing the electricity costs of the electricity consumers has therefore seen a drastic increase in the research of electricity markets. One of the factors that affect the profits of the electricity retailers and the energy cost of the consumers in electricity retail markets is the supply and demand. During high-supply and low-demand periods, the excess electricity if not managed, is wasted. During low-supply high-demand periods, the deficit supply can lead to electricity blackouts or costly electricity because of the volatile electricity wholesale spot market prices. Research studies have shown that electricity retailers can achieve significant profits and reduced electricity costs for their electricity consumers by minimizing the excess electricity and deficit electricity. Existing studies developed load forecasting models that aimed to match electricity supply and electricity demand. These models reached excellent accuracy levels, however due to the high volatility character of load demand and the rise of new electricity consumers, load forecasting alone is unable to mitigate excess and deficit electricity. In other studies, researchers proposed charging the electricity consumers’ batteries with excess electricity during high-supply low-demand periods and supplying their deficit electricity during low-supply high-demand periods. Electricity consumers’ incorporating batteries resulted in minimized excess and deficit electricity, in turn, maximizing the profits for the electricity retailers and minimizing the electricity costs for the electricity consumers. However, the batteries are consumer centric and only provide battery energy for the battery-owned consumer. Electricity consumers without battery energy during low-supply highdemand periods have electricity blackouts or require costly electricity from the electricity wholesale spot market. The peer-to-peer (P2P) energy sharing framework which allows electricity consumers to share their energy resources with one another is a viable solution to allow electricity consumers to share their battery energy. P2P energy sharing is a hot topic in research because of its potential to maximize the electricity retailers’ profits and minimize the electricity consumers’ electricity costs. Due to the increased profits for the electricity retailer and reduced electricity costs for the electricity consumers from implementing battery charging and P2P energy sharing, this dissertation proposes a day-ahead electricity retail market structure in which the electricity retailer supplies consumers’ batteries with excess electricity during high-supply low-demand periods, and during low-supply highdemand periods the electricity retailer discharges the consumers’ batteries to supply their deficit supply or supply their peers’ deficit supply. The electricity retailer aims to maximize its profits and minimize the electricity cost of the electricity consumers in its electricity retail market, by minimizing the excess and deficit electricity. The problem is formulated as a non-linear optimization model and solved using game theory. This dissertation compares the profits of the electricity retailer and electricity costs of the consumers that charge their batteries with excess electricity, discharge their batteries and purchase electricity from their peers to supply their deficit supply, with consumers that only charge their batteries with excess electricity but do not share their battery energy with their peers, consumers that only purchase electricity from their peers to supply their deficit supply but do not employ a battery, and consumers that neither employ a battery nor purchase electricity from their peers to supply their deficit supply. The results show that the consumers that charge their batteries with excess electricity, discharge their batteries and purchase electricity from their peers to supply their deficit supply achieved the lowest electricity cost and highest profits for the electricity retailer. / Dissertation (MEng)--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

With the advent of information explosion age, larger capacity disk drives are used to store data and powerful devices are used to process big data. As the scale and complexity of computer systems increase, we expect these systems to provide dependable and energy-efficient services and computation. Although hard drives are reliable in general, they are the most commonly replaced hardware components. Disk failures cause data corruption and even data loss, which can significantly affect system performance and financial losses. In this dissertation research, I analyze different manifestations of disk failures in production data centers and explore data mining techniques combined with statistical analysis methods to discover categories of disk failures and their distinctive properties. I use similarity measures to quantify the degradation process of each failure type and derive the degradation signature. The derived degradation signatures are further leveraged to forecast when future disk failures may happen. Meanwhile, this dissertation also studies energy efficiency of high performance computers. Specifically, I characterize the power and energy consumption of Haswell processors which are used in multiple supercomputers, and analyze the power and energy consumption of Legion, a data-centric programming model and runtime system, and Legion applications. We find that power and energy efficiency can be improved significantly by optimizing the settings and runtime scheduling of processors, and Legion runtime performs well for larger-scale computation in terms of power and energy consumption.

This paper examines the possibility of adding a photovoltaic(PV) power station to an already planned wind park in terms of profitability. At this time, southern Sweden’s grid is facing a number of challenges and is hurting economic development. Hybrid parks have showed to be able to tackle some of those challenges. This study has used a two-scaled methodology to analyse which solar PV size is the optimal to be co-located to the wind park of 24MW[Office1] . The results show that the 21MW size is the ideal one. In addition, to complement the findings, an analysis has been conducted to determine which battery size would be the optimal size to be added to the hybrid system. The results showed that a 1MW/1MWh battery storage would be the ideal size, however, it is possible that a 5MW/MWh battery storage might produce better results if peak shaving is included. All the scenarios in this study have been analysed in terms of IRR.

Evaluation of an On-Farm Water Storage (OFWS) system as a Best Management Practice (BMP) for nutrient and sediment loading control and irrigation in East Mississippi has shown that the system can effectively reduce sediment and nutrient loading as it was able to capture 46 tons of sediment and 558 kg of phosphorus over the monitoring period. The system was also able to decrease nitrogen loading as shown from the nitrogen concentration in the captured storm runoff events although an accurate estimate could not be made using AnnAGNPS because adequate model input data was not available. The system was able to provide about 63 million gallons of water for irrigation as a result of which increased corn and soybean yield was also obtained in irrigated fields when compared to non-irrigated fields. Water from the storage pond used for irrigation did not have adequate nutrient recycling to reduce commercial fertilizer application.

The rising use of fossil fuels and the resulting rise in environmental harm have fueled the advancement of automobiles that are fuel-efficient. A severe existential challenge facing the planet earth has given rise to hybrid electric vehicles (HEVs), which have developed from their incipient stage and are shown promise as a solution. Additionally, when needed to produce peaking power, batteries' efficiency is reduced. Instead, supercapacitors have smaller energy storage capacity but can withstand peaking power. Designing a clever method to manage the energy balance between a supercapacitor and a battery is the main goal of this research. Different topologies are used to study the battery-supercapacitor energy storage system in great detail. Nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and other harmful gases are less released when a battery-supercapacitor energy storage system is integrated. Additionally, it can lower the load on the battery, extending its life and improving its performance in HEVs.

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