Privacy Recommendations for Future Distributed Control Systems

As the role of privacy becomes more established in research, new questions and implementations trickle into the Distributed Control Systems (DCS) space focusing on privacy-preserving tools. In the near future, standards will have to include measures to protect the privacy of various objects, people, and systems in DCS plants. Building a privacy framework capable of meeting the needs of DCS applications and compatible with current standards to protect against intellectual theft and sabotage is the primary aspect for DCS. By identifying the lack of privacy protections in the current standards, detailing requirements for the privacy, and proposing suitable technologies we can provide guidelines for the next set of standards for DCS protections

OPTIMIZING THE USE OF ENERGY STORAGE AS A DEMAND RESPONSE TOOL

The renewable energies expansion over last years, due to the need to bring electricity production towards ever higher levels of green production and the increase of the demand, have brought further stability problems to the main grid. The handling of the integration of these alternative sources and the optimization of the electricity grid have given high attention on the role of demand response program as a key part for the target. The combination of battery storage units with real-time prices is part of the research effort that aims to reduce the instability of the grid and the energy costs of the users. Literature shows good potential for the control strategies as the relative wide range of technologies developed recently for the scope, even if for the residential customers usually the potential is constrained by the limited controllable loads and their significant share of consumption. However, the aspect of user comfort is not always fully considered leading to less realistic conclusions. The objective of the work described in the dissertation was then to obtain a reduction in residential energy costs through the optimal scheduling of user appliances supported by the use of battery storage, under a real-time price scheme, while limiting the discomfort for the customer. Although the first results of applying a real time pricing scheme based on the current variations in price observed in the Iberian wholesale market led only to small profits when not considering additional self-generation, they increased significantly if a small photovoltaic based production is considered, and reached significant cost savings (circa 70%) in periods of high solar generation. But, when applying a real time price following the fluctuations of the renewable energy supply, which produced much higher variations in price, the results improved considerably, reaching cost savings as high as 85%. The implemented model shows the true relevance of Demand Response and Energy Storage, producing meaningful savings if the supply costs change with the availability of renewable energy supply. With self-generation, the obtained value is even higher in the perspective of the individual customer, maximizing the cost-effectiveness of such investment

Getting Smart (Grids): An Efficiency Frontier Assessment

Information and communication technology are reshaping the electricity industry, with economic, environmental, and regulatory consequences. Smart grids allow the growing integration of renewable energy sources, a horizontalization of the roles of producers and consumers, a flatter demand profile which save investments intended to supply peaks of consumption, idle at great extent off-peaks. On the other hand, smart grids require important investments for modernizing technology. Concerning our objectives, firstly, we seek to understand the conceptual consequences of the irruption of smart grids on the electricity sector, and its importance for renewables adoption. Secondly, we discuss policies and regulations needed to accelerate the transformation of the electricity network in a smart grid, and to increase the renewables? share on total energy. Thirdly, our empirical approach runs a Data Envelopment Analysis (DEA) model to estimate the efficiency gains in the transition between traditional and smart grids. Our results show the efficiency levels of those countries whose objective is to deliver electricity with high levels of quality of services, and at the same time, using more renewables (with fewer carbon emissions), and low cost of supply. We conclude discussing the implications of our empirical model, the limitations, and next stages in polishing the results.Fil: Ferro, Gustavo Adolfo. Universidad del Cema; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Romero, Carlos Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; ArgentinaFil: Ramos, Maria Priscila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; ArgentinaLV Reunión Anual Asociación Argentina de Economía PolíticaCiudad Autónoma de Buenos AiresArgentinaAsociación Argentina de Economía Polític

Control of Electric Load Aggregations for Power System Services

In electrical power systems, when the supply from wind or solar-powered generation fluctuates, other resources adjust their power to maintain the system’s balance between demand and supply. Traditionally, gas, coal, and hydro-powered generators have provided this balancing service. In the future, as the proportion of renewable power generation increases, additional balancing resources will be needed. In this work, we develop methods that enable a new resource—aggregations of flexible loads—to provide energy balancing. Load aggregations are a promising resource for transmission-level energy balancing, but this service should not come at the expense of lower-level services and requirements. Specifically, an aggregator’s control should not compromise the loads’ service to the end-user and should not cause operational issues on the distribution network. Thermostatically controlled loads (TCLs), such as air conditioners and water heaters, have user-set temperature limits and cycling constraints that must be satisfied. Distribution networks have loading and voltage constraints to ensure reliable operation. When providing balancing services, aggregators partially synchronize loads, which can cause constraint violations on the distribution network. Third-party aggregators are unaware of conditions on the network and must coordinate with the distribution operator to ensure network reliability. The objective of this dissertation is to develop control methods by which a third-party aggregator can provide energy balancing without disrupting consumers and without causing unsafe conditions on the distribution network. Multiple methods are proposed for identifying and protecting distribution constraints that are at risk of violation due to load control. We conduct a simulation study of realistic distribution networks and find only a small subset of network constraints is at risk of violation. This result implies that network-safe control strategies may need to account for only a subset of network constraints, enhancing computational efficiency. We propose using a ``mode-count algorithm’’ to control a group of TCLs to minimize their impact on an at-risk network constraint. Results show that the mode-count algorithm can effectively reduce the variability of voltage at a constrained distribution node. Developing an online method to identify the set of at-risk constraints is non-trivial; towards this end, we propose an optimization-based method that identifies the network’s most at-risk individual constraint and provides a conservative, global safety constraint on power deviations caused by the aggregator. Because the method is computationally intensive, we develop techniques based on power-flow analysis to reduce the problem size; we are able to reduce the problem size by more than 60% for a test network. Two network-safe control strategies for energy balancing are proposed. Both strategies are hierarchical: the aggregator controls loads to track an energy-balancing signal, and the operator removes particular TCLs from the aggregator’s control when necessary for network safety. The strategies differ in terms of modeling and communication requirements. In a case study, the more complex strategy achieves a root-mean-square tracking error of 0.10% of the TCLs’ baseline power consumption while removing fewer than 1% of TCLs from the aggregator’s control; the other strategy achieves a 0.70% tracking error while removing approximately 15% of TCLs. The two strategies provide options — one better performing, one less costly — for operators and aggregators with different capabilities and preferences. Overall, these strategies enable third-party aggregators to control larger proportions of distribution-network load, enhancing competition in wholesale markets and providing the greater balancing capacities that will be needed by future, low-carbon power systems.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155144/1/sjcrock_1.pd