Modelling District Heating in a Renewable Electricity System

With the decarbonisation of electricity generation, large scale heat pumps are becoming increasingly viable for district heating combined with thermal energy storage, district heating can provide flexibility to the electricity grid by decoupling demand from supply. This thesis examines how district heating with heat pumps and thermal energy storage can integrate with and provide a benefit to an electricity system with predominantly renewable generation. The scope of work comprises three interlinked models underpinned by the same set of meteorology data, fundamentally coupling supply and demand. First, heat load data are surveyed, and an hourly demand profile is simulated. Disaggregation of district heating loads from the national demand is accomplished via segmentation of the building stock to model heat demand at high spatiotemporal resolution. Second, a novel method of pricing hourly electricity in a zero carbon, capital-intensive renewable system with electricity storage is developed and applied to a dispatch simulation to generate hourly electricity prices. Third, a dynamic model of district heating is constructed to simulate the meeting of heat loads with different design configurations using electricity as the energy source. Model predictive control is applied with varying forecast horizons so as to minimise the cost of electricity to meet the heat demand given a time series of hourly prices and consequently optimising the capacity of thermal energy storage. It was found that a thermal energy storage capacity equivalent to 1.3% of annual demand is sufficient to minimise operating costs. Finally, the potential impact of district heating on balancing the electricity system is analysed and an equivalence between thermal and electric storage is examined. While this is highly dependent on annual conditions, it can be as much as 3.5 units of thermal storage for every unit of electrical grid storage on the system. This could potentially reduce the investment in grid storage by £36 billion, underlining the significant financial benefits of thermal storage to the whole system. The research highlights the important potential of district heating to the UK’s energy system strategy

Sparse Representation of High Dimensional Data for Classification

In this thesis we propose the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification. Sparse transformation reduces the data volume/dimensionality without loss of critical information, so that it can be processed efficiently and assimilated by a human. We obtained sparse representation of high dimensional dataset using Sparse Principal Component Analysis (SPCA) and Direct formulation of Sparse Principal Component Analysis (DSPCA). Later we performed classification using k Nearest Neighbor (kNN) Method and compared its result with regular PCA. The experiments were performed on hyperspectral data and various datasets obtained from University of California, Irvine (UCI) machine learning dataset repository. The results suggest that sparse data representation is desirable because sparse representation enhances interpretation. It also improves classification performance with certain number of features and in most of the cases classification performance is similar to regular PCA

Learning Computational Thinking Using Open-Source Hardware-based Programming

One of the first most fundamental skills that freshman engineering students learn is computational thinking. Computation thinking is the thought process carried out to solve problems. To develop this skill set usually computer programming fundamentals are introduced using a specific programming language. This approach falls short in sustaining the students’ interest in engineering. To rekindle the students’ interest in engineering, we proposed the utilization of the open-source electronics prototyping platform “Arduino”. Introducing the students to hardware programming and having them use project-based approaches to develop their computational thinking skill set increased their interest in the subject matter and significantly improved their performance

Learning Computational Thinking Using Open-Source Hardware-based Programming

Presentation given at the STEM Teaching and Learning Conference 2017, Savannah, GA

WHAT IS YOUR UNDERSTANDING OF SPINAL AND EPIDURAL ATTEMPT?

Background: The practice of spinal and epidural anaesthesia is well established the world over for a number of years. Sighting of spinal or epidural is conducted through various approaches at various levels of the spinal column. The number of attempts has its correlation with the post-spinal and epidural complications.Aim: The aim is to gather information about the understanding among the anaesthetists about the spinal/epidural attempt.Materials and Methods: A pro forma comprising of nine closed-loop questions was distributed to all the participants in the study, and they were requested to fill it anonymously and placed it back in a designated sealed box in anaesthetic  office.Results: A total of 20 pro formas were distributed, and all of them received back with 100% responses. All the participants accepted universally that attempting through another space makes it a second attempt. One of the respondents thought any backward movement means 2nd attempt, the majority of the responders thought it does not count as an attempt. Almost everyone considered another attempt if a needle is completely withdrawn and enters through another puncture site whether through a midline or paramedian approach.Conclusion: Most of the complications after neuraxial blockade are associated with the number of attempts alongside other factors that may play a role. A universal definition of a spinal and epidural attempt may decrease the complications associated with the central neuraxial blockade.Key words: Epidural, single attempt, spina

Focusing on Writing to Learn Approach to increase engagement and performance in Digital Design Lab

In an effort to help students in the discipline build on their writing skills throughout the undergraduate curriculum, Georgia Southern University initiated a quality enhancement plan (QEP) with a focus on writing across the Electrical Engineering curriculum. As part of this plan, the Digital Design Lab course, offered at the sophomore level in the curriculum, implemented several strategies to help students build on their previous writing skills, and in the process improved their technical vocabulary, the ability to communicate using it, increased students’ engagement, collaboration, and performance in the course. In this work, the effect of deliberately engaging students in their writing skills as a process to learn the content material and communicate it effectively is presented. Several strategies were used like faculty instruction, using rubrics as a guide for assessment, peer reviewing and engaging a student writing fellow to assist students in this process. The effectiveness of these strategies was verified using multiple statistical assessment methods and the students’ performance before and after the intervention was compared with emphasis on the writing-to-learn process. Qualitative data is also presented to assess the benefit of the intervention for students learning the course content

A novel method for forecasting electricity prices in a system with variable renewables and grid storage

In future UK energy scenarios with a high level of electrification, a large share of electricity is expected to be generated from renewable sources. To accommodate the variability of renewable generation, flexibility in the network is vital. An important flexibility option is grid scale electricity storage. A simulation is made of the electricity system with variable renewable generation, electricity storage and flexible high carbon generators, assumed to be gas CCGT, for various UK scenarios. The simulation uses historical hourly meteorology to drive models of demand and renewable variation, and the consequent input/output operation of storage and dispatchable generation to balance differences between demand and renewables. A marginal cost method is devised to calculate the storage, renewable and dispatching capacity and operational costs incurred in each hour. These cost structures can form a transparent economic base for informing market design and setting prices for use in energy system models. Results show that while marginal costs for renewable generation are relatively low, reliance on battery storage for backup particularly during peak periods can result in high electricity prices and without a significant increase in projected fossil fuel or carbon prices, traditional high carbon electricity generators will still be cheaper to operate. This work will be used to analyse the interaction between district heating with thermal energy storage and heat pumps, and the electricity system

Sri Lanka's drone pioneers

The International Water Management Institute in Sri Lanka has begun to experiment with drone technology to support a wide range of studies like crop monitoring, disaster mitigation and disease prevention

A New Risk-Based Method in Decision Making to Create Dust Sources Maps: A Case Study of Saudi Arabia

Dust storms are one of the major causes of the destruction of natural ecosystems and human infrastructure worldwide. Therefore, the identification and mapping of susceptible regions to dust storm formation (SRDSFs) is of great importance. Determining SRDSFs by considering the concept of risk in the decision-making process and the kind of manager’s attitude and planning can be very valuable in dedicating financial resources and time to identifying and controlling the negative impacts of SRDSFs. The purpose of this study was to present a new risk-based method in decision making to create SRDSF maps of pessimistic and optimistic scenarios. To achieve the purpose of this research, effective criteria obtained from various sources were used, including simulated surface data, satellite products, and soil data of Saudi Arabia. These effective criteria included vegetation cover, soil moisture, soil erodibility, wind speed, precipitation, and absolute air humidity. For this purpose, the ordered weighted averaging (OWA) model was employed to generate existing SRDSF maps in different scenarios. The results showed that the wind speed and precipitation criteria had the highest and lowest impact in identifying dust centers, respectively. The areas identified as SRDSFs in very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 85,950, 168,275, 255,225, 410,000, and 596,500 km2, respectively. The overall accuracy of very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 84.1, 83.3, 81.6, 78.2, and 73.2%, respectively. The very pessimistic scenario can identify the SRDSFs in the study area with higher accuracy. The overall accuracy of the results of these scenarios compared to the dust sources obtained from the previous studies were 92.7, 94.2, 95.1, 88.4, and 79.7% respectively. The dust sources identified in the previous studies have a higher agreement with the results of the neutral scenario. The proposed method has high flexibility in producing a wide range of SRDSF maps in very pessimistic to very optimistic scenarios. The results of the pessimistic scenarios are suitable for risk-averse managers with limited financial resources and time, and the results of the optimistic scenarios are suitable for risk-taking managers with sufficient financial resources and time.Peer Reviewe