Enabling Innovation In The Energy System Transition

Innovation in the electric sector has the potential to drive job growth, decrease environmental impacts, reduce rate payer costs, and increase reliability and resiliency. However, the traditional electric system was built to deliver a controlled flow of energy from a centralized location with maximum reliability and minimum cost. As both customer expectations and generation technologies change, new avenues for grid innovation are being explored. Residential customers, commercial and industrial clients, and electric utilities must all find a way to balance goals for decarbonization and social justice with maintaining a least cost, reliable power grid. Grounded in Geel’s energy system transition framework, this dissertation explores how each of these three stakeholder groups is navigating the transition to renewables. The first study tests the idea that residential customers will be more inclined to change their behavior when altruistically contributing to a greater goal. Renewed Darwinian theory was explored to question the exclusive use of financial incentives in demand response programs, with evidence that enabling altruism may influence electricity demand even more effectively than traditional financial incentives. A difference in differences approach was designed to test the impact of the Burlington Electric Department’s Defeat the Peak program on residential energy use where the incentive was a group donation to a local charity. Results suggest utility savings of over $12 in energy supply costs for every$1 they invested in the program. Financial levers, however, can be quite effective in influencing electricity demand, and may result in cost-shifting from high to low demand consumers. The second study focused on rate design for commercial and industrial customers through an analysis of the utility demand charge. For over a century the demand charge has been a primary means to recover total cost-of-service including fixed, embedded, and overhead costs. Under the current system, most small commercial and residential customers do not receive a strong direct price signal to invest in storage, load shifting, or renewables. Larger commercial and industrial customers exercise some measure of control over their loads to reduce demand charges, but with only modest benefit or value to the system as a whole. The system costs are then redistributed to all customer classes, potentially falling disproportionately on low demand customers. To investigate, a regression analysis was conducted with cost and market characteristics from 447 US electric utilities. Results suggest that demand charges predict a significant degree of variability in residential pricing, confirming suspected cost shifting. Redesigning the demand charge could open up new markets for renewable energy entrepreneurs and lower grid costs and customer rates, supporting goals of decarbonization while also achieving reliable least-cost power. In the third study, an iterative approach was employed to understand why some utilities lean into the energy system transition while others take a more conservative stance. A database of 170 US electric utilities was constructed including a qualitative assessment of Integrated Resource Plans for renewability orientation. Institutional resource-based theory was utilized to take a striated approach to understanding firm heterogeneity, identifying factors at the individual manager level, firm level, and external environment that can influence a utility’s energy supply characteristics. Independent variables in a simultaneous regression analysis included CEO gender and tenure at the individual level, ownership structure and firm age at the firm level, and the impact of policies and state rurality at the inter-firm level. Results indicate that a significant amount of a utility’s commitment to the renewable energy transition can be predicted based on these firm characteristics

Microgrids and Resilience to Climate-Driven Impacts on Public Health

“Resilience” has burst into the lexicons of several policy areas in recent years, owing in no small part to climate change’s amplification of extreme events that severely disrupt the operation of natural, social, and engineered systems. Fostering resilience means anticipating severe disruptions and planning, investing, and designing so that such disruptions, which are certain to occur, are made shallower in depth and shorter in duration. Thus a resilient system or community can continue functioning despite disruptive events, return more swiftly to routine function following disruption, and incorporate new information so as to improve operations in extremis and speed future restorations. As different policy communities apply the concept of resilience to their respective missions, they emphasize different objectives. This article examines how the definitions adopted by the public health and electricity communities can, but do not necessarily, converge in responses to electricity outages so severe that they affect the operation of critical infrastructure, such as wastewater treatment and drinking water facilities, hospitals, and cooling centers. Currently, such outages cause a form of handoff from utilities to their customers: grid power fails and a small constellation of backup generators maintained by atomized campuses, facilities, or individual structures switch on, or fail to switch on, or were never purchased and so leave the location dark and its equipment inoperative. This handoff is operational, but it reflects legal obligations—and their limits. Enter the microgrid, a specially designed segment of the electricity distribution grid’s mesh that can either operate seamlessly as part of the wider grid, or as an independent “island” that serves some or all of the electricity users within its boundary even when the wider grid fails. Microgrids can, but do not necessarily, mitigate the adverse public health implications of the handoff that accompanies widespread and severe grid failure. To encourage the convergence of public health and electricity policy priorities in decisions about microgrid siting, design, and operation, this article makes several recommendations. Some of these should ideally be taken up at the federal level, but the bulk of the work they recommend should take place at the state-level, and would necessarily be implemented at the state and local levels

Microgrids and Resilience to Climate-Driven Impacts on Public Health

“Resilience” has burst into the lexicons of several policy areas in recent years, owing in no small part to climate change’s amplification of extreme events that severely disrupt the operation of natural, social, and engineered systems. Fostering resilience means anticipating severe disruptions and planning, investing, and designing so that such disruptions, which are certain to occur, are made shallower in depth and shorter in duration. Thus a resilient system or community can continue functioning despite disruptive events, return more swiftly to routine function following disruption, and incorporate new information so as to improve operations in extremis and speed future restorations. As different policy communities apply the concept of resilience to their respective missions, they emphasize different objectives. This article examines how the definitions adopted by the public health and electricity communities can, but do not necessarily, converge in responses to electricity outages so severe that they affect the operation of critical infrastructure, such as wastewater treatment and drinking water facilities, hospitals, and cooling centers. Currently, such outages cause a form of handoff from utilities to their customers: grid power fails and a small constellation of backup generators maintained by atomized campuses, facilities, or individual structures switch on, or fail to switch on, or were never purchased and so leave the location dark and its equipment inoperative. This handoff is operational, but it reflects legal obligations – and their limits. Enter the microgrid, a specially designed segment of the electricity distribution grid’s mesh that can either operate seamlessly as part of the wider grid, or as an independent “island” that serves some or all of the electricity users within its boundary even when the wider grid fails. Microgrids can, but do not necessarily, mitigate the adverse public health implications of the handoff that accompanies widespread and severe grid failure. To encourage the convergence of public health and electricity policy priorities in decisions about microgrid siting, design, and operation, this article makes several recommendations. Some of these should ideally be taken up at the federal level, but the bulk of the work they recommend should take place at the state-level, and would necessarily be implemented at the state and local levels

Machine Learning Applications for Load Predictions in Electrical Energy Network

In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques are systematically investigated for electrical energy prediction and correlating other impacting parameters. The k-Nearest Neighbour (kNN), Random Forest (RF) and Linear Regression (LR) are analysed and evaluated both by using continuous and vertical time approach. It is observed that for 30 minutes predictions the RF Regression has the best results, shown by a mean absolute percentage error (MAPE) in the range of 1-2 %. kNN show best results for the day-ahead forecasting with MAPE of 2.61 %. The presented vertical time approach outperforms the continuous time approach. To enhance pre-processing stage, refined techniques from the domain of statistics and time series are adopted in the modelling. Reducing the dimensionality through principal components analysis improves the predictive performance of Recurrent Neural Networks (RNN). In the case of Gated Recurrent Units (GRU) networks, the results for all the seasons are improved through principal components analysis (PCA). This work also considers abnormal operation due to various instances (e.g. random effect, intrusion, abnormal operation of smart devices, cyber-threats, etc.). In the results of kNN, iforest and Local Outlier Factor (LOF) on urban area data and from rural region data, it is observed that the anomaly detection for the scenarios are different. For the rural region, most of the anomalies are observed in the latter timeline of the data concentrated in the last year of the collected data. For the urban area data, the anomalies are spread out over the entire timeline. The frequency of detected anomalies where considerably higher for the rural area load demand than for the urban area load demand. Observing from considered case scenarios, the incidents of detected anomalies are more data driven, than exceptions in the algorithms. It is observed that from the domain knowledge of smart energy systems the LOF is able to detect observations that could not have detected by visual inspection alone, in contrast to kNN and iforest. Whereas kNN and iforest excludes an upper and lower bound, the LOF is density based and separates out anomalies amidst in the data. The capability that LOF has to identify anomalies amidst the data together with the deep domain knowledge is an advantage, when detecting anomalies in smart meter data. This work has shown that the instance based models can compete with models of higher complexity, yet some methods in preprocessing (such as circular coding) does not function for an instance based learner such as k-Nearest Neighbor, and hence kNN can not option for this kind of complexity even in the feature engineering of the model. It will be interesting for the future work of electrical load forecasting to develop solution that combines a high complexity in the feature engineering and have the explainability of instance based models.publishedVersio

Insights from the Inventory of Smart Grid Projects in Europe: 2012 Update

By the end of 2010 the Joint Research Centre, the European Commission’s in-house science service, launched the first comprehensive inventory of smart grid projects in Europe1. The final catalogue was published in July 2011 and included 219 smart grid and smart metering projects from the EU-28 member states, Switzerland and Norway. The participation of the project coordinators and the reception of the report by the smart grid community were extremely positive. Due to its success, the European Commission decided that the project inventory would be carried out on a regular basis so as to constantly update the picture of smart grid developments in Europe and keep track of lessons learnt and of challenges and opportunities. For this, a new on-line questionnaire was launched in March 2012 and information on projects collected up to September 2012. At the same time an extensive search of project information on the internet and through cooperation links with other European research organizations was conducted. The resulting final database is the most up to date and comprehensive inventory of smart grids and smart metering projects in Europe, including a total of 281 smart grid projects and 90 smart metering pilot projects and rollouts from the same 30 countries that were included in the 2011 inventory database. Projects surveyed were classified into three categories: R&D, demonstration or pre-deployment) and deployment, and for the first time a distinction between smart grid and smart metering projects was made. The following is an insight into the 2012 report.JRC.F.3-Energy securit

Electric Power Infrastructure Vulnerabilities to Heat Waves from Climate Change

abstract: 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.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Concepts and Practices for Transforming Infrastructure from Rigid to Adaptable

abstract: Infrastructure are increasingly being recognized as too rigid to quickly adapt to a changing climate and a non-stationary future. This rigidness poses risks to and impacts on infrastructure service delivery and public welfare. Adaptivity in infrastructure is critical for managing uncertainties to continue providing services, yet little is known about how infrastructure can be made more agile and flexible towards improved adaptive capacity. A literature review identified approximately fifty examples of novel infrastructure and technologies which support adaptivity through one or more of ten theoretical competencies of adaptive infrastructure. From these examples emerged several infrastructure forms and possible strategies for adaptivity, including smart technologies, combined centralized/decentralized organizational structures, and renewable electricity generation. With institutional and cultural support, such novel structures and systems have the potential to transform infrastructure provision and management.Dissertation/ThesisMasters Thesis Civil, Environmental and Sustainable Engineering 201

A Technical and Systems Analysis of Hydrogen Fuel in Renewable Energy Systems

Within the next century, we must tackle the dual challenges of continuing to meet the increasing global demand for energy services while stabilizing global temperatures to mitigate the effects of anthropogenic climate change. Doing so will require a major restructuring of all energy services on a global scale. Here, we contribute to the understanding of the role of hydrogen fuel in net-zero emissions systems from both a technical and systems perspective. From the technical perspective, we evaluate the activation mechanism of an electrodeposited cobalt selenide hydrogen evolution reaction (HER) catalyst using operando Raman spectroscopy. During this activation process these films, which originally show no catalytic activity toward HER, undergo a compositional change in which selenium in the form of loose, polymeric chains is electrochemically reduced from the material. This work provides a facile method towards investigating catalytic materials under operando conditions, elucidates the changes that occur in this cobalt selenide material during the activation step, and offers potential paths toward the improvement of the cobalt selenide catalyst. At the systems level, we use hourly weather data over multiple decades and historical electricity demand data to analyze the gaps between wind and solar supply and electricity demand for California (CA) and the Western Interconnect (WECC). We quantify the occurrence of resource droughts when the daily power from each resource was less than half of the 39-year daily mean for that day of the year. Using a macro-scale electricity model, we then evaluate the potential for both long-term storage (in the form of power-to-gas-to-power) and more geographically diverse generation resources to minimize system costs. For wind-solar-battery electricity systems, meeting California demand with WECC generation resources reduces the cost by 9% compared to constraining resources entirely to California. Adding long-duration storage lowers system costs by 21% when treating California as an island. This data-driven analysis quantifies rare weather-related events and provides an understanding that can be used to inform stakeholders in future electricity systems.</p