PERSONAL DATA PROTECTION RULES! GUIDELINES FOR PRIVACY-FRIENDLY SMART ENERGY SERVICES

Privacy-friendly processing of personal data is proving to be increasingly challenging in today’s energy systems as the amount of data grows. Smart energy services provide value creation and co-creation by processing sensible user data collected from smart meters, smart home devices, storage systems, and renewable energy plants. To address this challenge, we analyze key topics and develop design requirements and design principles for privacy-friendly personal data processing in smart energy services. We identify these key topics through expert interviews, text-mining, and topic modelling techniques based on 149 publications. Following this, we derive our design requirements and principles and evaluate these with experts and an applicability check with three real-world smart energy services. Based on our results and findings, we establish a further research agenda consisting of five specific research directions

Resilient Data Collection in Smart Grid

Sensors and measurement devices are widely deployed in Smart Grid (SG) to monitor the health of the system. However, these devices are subject to damage and attack so that they cannot deliver sensing data to the control center. In tree-based data collection schemes, a relay failure can further lead to unresponsiveness of all the devices in its sub-tree. In this paper, we study the resiliency issue in collecting data from SG measurement devices. We first design a protocol that guarantees successful data collection from all non-faulty devices in a backup-enabled tree structure. Then, we formulate the tree construction problem to optimize data collection time. Since the formulated problem is NP-hard, we propose a heuristic algorithm to solve it. We evaluate our algorithm using a real utility network topology. The experiment results show that our algorithm performs well in large scale networks.CREDCOpe

SEEV4City INTERIM 'Summary of the State of the Art' report

This report summarizes the state-of-the-art on plug-in and full battery electric vehicles (EVs), smart charging and vehicle to grid (V2G) charging. This is in relation to the technology development, the role of EVs in CO2 reduction, their impact on the energy system as a whole, plus potential business models, services and policies to further promote the use of EV smart charging and V2G, relevant to the SEEV4-City project

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

Improving Electricity Distribution System State Estimation with AMR-Based Load Profiles

The ongoing battle against global warming is rapidly increasing the amount of renewable power generation, and smart solutions are needed to integrate these new generation units into the existing distribution systems. Smart grids answer this call by introducing intelligent ways of controlling the network and active resources connected to it. However, before the network can be controlled, the automation system must know what the node voltages and line currents defining the network state are.Distribution system state estimation (DSSE) is needed to find the most likely state of the network when the number and accuracy of measurements are limited. Typically, two types of measurements are used in DSSE: real-time measurements and pseudomeasurements. In recent years, finding cost-efficient ways to improve the DSSE accuracy has been a popular subject in the literature. While others have focused on optimizing the type, amount and location of real-time measurements, the main hypothesis of this thesis is that it is possible to enhance the DSSE accuracy by using interval measurements collected with automatic meter reading (AMR) to improve the load profiles used as pseudo-measurements.The work done in this thesis can be divided into three stages. In the first stage, methods for creating new AMR-based load profiles are studied. AMR measurements from thousands of customers are used to test and compare the different options for improving the load profiling accuracy. Different clustering algorithms are tested and a novel twostage clustering method for load profiling is developed. In the second stage, a DSSE algorithm suited for smart grid environment is developed. Simulations and real-life demonstrations are conducted to verify the accuracy and applicability of the developed state estimator. In the third and final stage, the AMR-based load profiling and DSSE are combined. Matlab simulations with real AMR data and a real distribution network model are made and the developed load profiles are compared with other commonly used pseudo-measurements.The results indicate that clustering is an efficient way to improve the load profiling accuracy. With the help of clustering, both the customer classification and customer class load profiles can be updated simultaneously. Several of the tested clustering algorithms were suited for clustering electricity customers, but the best results were achieved with a modified k-means algorithm. Results from the third stage simulations supported the main hypothesis that the new AMR-based load profiles improve the DSSE accuracy.The results presented in this thesis should motivate distribution system operators and other actors in the field of electricity distribution to utilize AMR data and clustering algorithms in load profiling. It improves not only the DSSE accuracy but also many other functions that rely on load flow calculation and need accurate load estimates or forecasts

Effect of information on household water and energy use, The

2014 Summer.Water and Energy Utilities are faced with growing demand at a time when supply expansion is increasingly costly, inconsistent and taxing on the environment. Given that supply expansion is limited, to meet future needs utilities need demand-side management policies to result in more reliable and consistent consumer responsiveness. Currently, most households do not have access to the level or type of information needed to respond to price signals in a reliable and effective way. Advanced information technology solutions exist and are being increasingly adopted, but we need to know more about how the informational setting affects decision-making, consumption levels and price responsiveness. This research analyzes the effect of information on household water and energy consumption, which is a decision-making environment characterized by uncertainty and imperfect information. This study also analyzes additional complexities stemming from infrequent billing, non-linear pricing structures, and combined utility bills, each of which may dampen price signals. I first develop a theoretical model of decision-making under uncertainty. I use this model to illustrate the effect of more frequent information, which eliminates uncertainty about past decisions, on remaining decisions within the billing period. The model emphasizes the role of risk preferences and the realization of the uncertain quantity. On average, risk averse consumers will increase consumption when uncertainty is reduced; risk seeking consumers will do the opposite. Introduction of a non-linear rate structure induces behavior that makes individuals appear as if they are risk averse or risk seeking, despite their actual risk preferences. This model highlights the importance of modeling multiple decisions within a billing period and accounting for a spectrum of risk preferences. In Chapter 3, I create a computerized laboratory experiment designed to generate data used to test some of the hypotheses formulated in the theoretical model presented in Chapter 2. Results from the experiment show that, on average, individuals consume more when provided with more frequent information that resolves uncertainty about past decisions made within a single billing period. This result is driven by the fact that the majority of participants are risk averse or risk neutral. Risk seeking participants instead reduce use when facing less uncertainty. Also as predicted by the theoretical model in Chapter 2, combining behavior driven by risk preferences with the presence of an increasing block rate structure results in behavior that looks like consumers are targeting the block boundary. This experiment shows that providing more information may not lead to reduced use without other incentives, goal-setting, or mechanisms designed to help individuals process the information. In Chapter 4, I empirically analyze a ten-year household-level panel data set of monthly utility bills. A single utility provides electricity, natural gas and water services to its customers and therefore bills through a single utility bill. I first show that price responsiveness varies by the number and combination of services subscribed to by a given household. Second, through a price salience model I show that households are more responsive to the price of water when the water portion of the total bill is greater. When multiple services are contained on a single bill, the salience of any individual price signal is dampened. This study confirms that households are inelastic though not unresponsive to water prices. In order to make pricing policies more effective, utilities need to acknowledge that households may be responding to total utility costs (i.e., may respond to a high utility bill by reducing electricity use despite the true driver of the high bill) and will need to find ways to make quantity and price information more salient to their customers. Chapter 5 concludes this dissertation by summarizing the contributions of the research and possible extensions for future work. By improving the informational environment surrounding household water and energy use, there will be great capacity for households to use water and energy more efficiently and ultimately make choices that reduce residential water/energy consumption and yield benefits for customers, utilities, and the environment

Using stochastic frontier analysis to measure the impact of weather on the efficiency of electricity distribution businesses in developing economies

This paper analyses the influence of weather variables on the efficiency of electricity distribution companies in Argentina, Brazil, Chile and Peru, covering 82 firms which represent more than 90 per cent of the distribution market of energy delivered for the period 1998-2008. Stochastic frontier analysis (SFA) is applied using a translog input distance function. Two different approaches are evaluated: weather in the production function and weather in the inefficiency term. The efficacy of one over the other is determined using nested models. Weather data are collected from meteorological stations (429) and NASA (3,423 coordinates). A geographic information system (GIS) is used for locating the firms’ service areas and their weather conditions. A combination of cost only and cost-quality models is proposed. For cost only models, the results suggest that on average there is a significant increase in measured efficiency when weather is incorporated in the production function. Under the cost-quality models, on average the effect of weather is much lower. This suggests that firms have internalised the effects of weather and have adapted their networks to the environment in which they operate. A company-level analysis indicates that across models a significant number of companies are affected by weather. Regulators are advised to make proper adjustments of efficiency scores when specific firms face important efficiency changes due to weather.This research was supported by RCUK ESRC (Grant Number: RES-152-25-1002). The views expressed herein are those of the authors and do not reflect the views of EPRG

Opportunities and Risks of Digitalization for Climate Protection in Switzerland

Information and Communication Technology (ICT) is an important enabler for a low-carbon economy in Switzerland. ICT has the potential to avoid up to 3.37 times more greenhouse gas (GHG) emissions than the amount of emissions caused by the production, operation and disposal of ICT devices and infrastructures used in Switzerland in 2025. In absolute terms, ICT will enable the Swiss economy to save up to 6.99 Mt CO2-equivalents (CO2e) per year, with an own carbon footprint of 2.08 Mt CO2e per year. This opportunity for the ICT sector to contribute to climate protection, however, can only be realized under optimistic assumptions. In particular, it is necessary that the existing technological and economic potentials are systematically exploited by taking ambitious and targeted actions. Such actions can be especially effective in the transportation, building and energy sectors, which have the highest potential for ICT-enabled (“smart”) solutions to reduce GHG emissions. At the same time, the carbon footprint of the ICT sector itself must be reduced by 17%, which is technologically and economically feasible due to efficiency gains

Reconsidering the calculation and role of environmental footprints

Following the recent Copenhagen Climate Change conference, there has been discussion of the methods and underlying principles that inform climate change targets. Climate change targets following the Kyoto Protocol are broadly based on a production accounting principle (PAP). This approach focuses on emissions produced within given geographical boundaries. An alternative approach is a consumption accounting principle (CAP), where the focus is on emissions produced globally to meet consumption demand within the national (or regional) economy1. Increasingly popular environmental footprint measures, including ecological and carbon footprints, attempt to measure environmental impacts based on CAP methods. The perception that human consumption decisions lie at the heart of the climate change problem is the impetus driving pressure on policymakers for a more widespread use of CAP measures. At a global level of course, emissions accounted for under the production and consumption accounting principles would be equal. It is international trade that leads to differences in emissions under the two principles. This paper, the second in this special issue of the Fraser Commentary, examines how input-output accounting techniques may be applied to examine pollution generation under both of these accounting principles, focussing on waste and carbon generation in the Welsh economy as a case study. However, we take a different focus, arguing that the ‘domestic technology assumption’, taken as something of a mid-point in moving between production and consumption accounting in the first paper, may actually constitute a more useful focus for regional policymakers than full footprint analyses