Frequency regulation in electric power systems using deferrable loads

Incluye bibliografía y anexosCon el advenimiento del paradigma de la red inteligente (Smart Grid) y las energías renovables, se hace necesario estudiar el almacenamiento de energía generada que no se consume al momento. En esta tesis, se indaga en el papel de un “load agregator” que administra un conjunto de cargas eléctricas y aprovecha la flexibilidad de las mismas para regular la frecuencia de una red. Se estudia el problema desde un punto de vista macroscópico, sin entrar en detalles de cargas individuales. Se propone un set de modelos ODE para predecir la evolución de la potencia consumida por el cluster de cargas y se diseñan controladores para estos modelos, con el fin de poder seguir las referencias de potencia externa. Finalmente, se sugieren algunos algoritmos posibles para implementar el control a cargas individuales. Las simulaciones muestran que este sistema podría proporcionar valiosos servicios a las redes eléctricas, si existiese suficiente infraestructura de comunicaciones.ANII - POS_NAC_2013_1_11675

Large-Scale Demand Management in Smart Grid

Future energy grids are expected to rely extensively on controlling consumers' demands to achieve an efficient system operation. The demand-side of the power network is usually constituted of a large number of low power loads, unlike energy production which is concentrated in a few numbers of high power generators. This research is concerned with supporting the management of numerous loads, which can be challenging from a computational point-of-view. A common approach to facilitate the management of a large number of resources is through resource aggregation (clustering). Therefore, the main objective of our research is to develop efficient load aggregation methodologies for two categories of demands: residential appliances and electric vehicles. The proposed methodologies are based on queueing theory, where each queue represents a certain category (class) of demand. Residential appliances are considered in the context of two demand management problems, where the first aims to minimize the energy consumption cost, while the second aims to reduce the magnitude of fluctuations in net demand, as a result of a large-scale integration of renewable energy sources (RESs). Existing models for residential demand aggregation suffer from two limitations:first, demand models ignore the inter-temporal demand dependence that is induced by scheduling deferrable appliances; Second, aggregated demand models for thermostatically-controlled loads are computationally inefficient to be used in DR problems that require optimization over multiple time intervals. Although the same aggregation methodology is applied to both problems, each one of them requires a different demand scheduling algorithm, due to the stochastic nature of RESs which is introduced in the second problem. The second part of our research focuses on minimizing the expected system time needed for charging electric vehicles (EVs). This target can be achieved by two types of decisions, the assignment of EVs to charging stations and the charging of EVs' batteries. While there exist aggregation models for batteries' charging, aggregation models for EVs' assignment are almost non-existent. In addition, aggregation models for batteries' charging assume that information about EVs' arrival times, departure times and their required charging energies are given in advance. Such assumption is non-realistic for a charging station, where vehicles arrive randomly. Hence, the third problem is concerned with developing an aggregation model for EVs' assignment and charging, while considering the stochastic nature of EVs' arrivals. Realistic models for residential demands and RES powers were used to develop the corresponding numerical results. The proposed scheduling algorithms do not require highly restrictive assumptions. The results proved that effectiveness of the proposed methodology and algorithms in achieving a significant improvement in the problems' objectives. On the other hand, the algorithm used in EV assignment requires restrictive Markovian assumptions. Hence, we needed to verify our proposed analytical model with a more realistic simulation model. The results showed a good compliance between both models. Our proposed methodology helped in improving the average system time significantly, compared to that of a near-station-assignment policy. This study is expected to have an important contribution from both research and application perspectives. From the research side, it will provide a tool for managing a large, diverse number of electric appliances by classifying them according to how much they can benefit the utility. From the application side, our work will help to include residential consumers in demand response (while current DR programs focus on the industrial sector only). It will also facilitate RESs and EVs on a large scale to help address environmental concerns

Large Scale Control of Deferrable Domestic Loads in Smart Grids

In this paper, we investigate a realistic and low- cost deployment of large scale direct control of inelastic home appliances whose energy demand cannot be shaped, but simply deferred. The idea is to exploit 1) some simple actuators to be placed on the electric plugs for connecting or disconnecting appliances with heterogeneous control interfaces, including non- smart appliances, and 2) the Internet connections of customers for transporting the activation requests from the actuators to a centralized controller. Our solution requires no interaction with home users: in particular, it does not require them to express their energy demand in advance. A queuing theory model is derived to quantify how many users should adopt this solution in order to control a significant aggregated power load without significantly impairing their quality of service

Scheduling Techniques for Operating Systems for Medical and IoT Devices: A Review

Software and Hardware synthesis are the major subtasks in the implementation of hardware/software systems. Increasing trend is to build SoCs/NoC/Embedded System for Implantable Medical Devices (IMD) and Internet of Things (IoT) devices, which includes multiple Microprocessors and Signal Processors, allowing designing complex hardware and software systems, yet flexible with respect to the delivered performance and executed application. An important technique, which affect the macroscopic system implementation characteristics is the scheduling of hardware operations, program instructions and software processes. This paper presents a survey of the various scheduling strategies in process scheduling. Process Scheduling has to take into account the real-time constraints. Processes are characterized by their timing constraints, periodicity, precedence and data dependency, pre-emptivity, priority etc. The affect of these characteristics on scheduling decisions has been described in this paper

Stability Analysis of Wholesale Electricity Markets under Dynamic Consumption Models and Real-Time Pricing

This paper analyzes stability conditions for wholesale electricity markets under real-time retail pricing and realistic consumption models with memory, which explicitly take into account previous electricity prices and consumption levels. By passing on the current retail price of electricity from supplier to consumer and feeding the observed consumption back to the supplier, a closed-loop dynamical system for electricity prices and consumption arises whose stability is to be investigated. Under mild assumptions on the generation cost of electricity and consumers' backlog disutility functions, we show that, for consumer models with price memory only, market stability is achieved if the ratio between the consumers' marginal backlog disutility and the suppliers' marginal cost of supply remains below a fixed threshold. Further, consumer models with price and consumption memory can result in greater stability regions and faster convergence to the equilibrium compared to models with price memory alone, if consumption deviations from nominal demand are adequately penalized.Comment: 8 pages, 7 Figures, accepted to the 2017 American Control Conferenc

Topics in Demand Response for Energy Management in Smart Grid

Future electricity grids will enable greater and more sophisticated demand side participation, which refers to the inclusion of mechanisms that enable dynamic modification of electricity demand into the operations of the electricity market, known as Demand Response (DR). The underlying information-flow infrastructures provided by the emerging smart grid enhance the interactions between customers and the market, by which DR will improve electricity grids in several aspects, e.g., by reducing peak demand and reducing need for expensive peaker plants, or by enabling demand to follow supply such as those from volatile renewable resources, etc. Many types of appliances provide flexibilities in power usage which can be viewed as demand response resources, and how to exploit such flexibilities to achieve the benefits offered by DR is a central challenge. In this dissertation, we design algorithms and architectures to bridge the gap between scheduling appliances and the benefits that DR can bring to electricity grid by utilizing the smart grid\u27s underlying information infrastructure. First, we focus on demand response within the consumer premise, where an energy management controller (EMC) schedules appliance operation on behalf of customers to save energy cost. We propose an optimization-based control scheme for the EMC in the building that integrates both the operational flexible appliances such as clothes washer/dryer, dish washer and plug-in electric vehicles (PEVs), but also the thermostatically controlled appliances such as HVAC (heating, ventilation, and air conditioning) systems together with the thermal mass of the building. Model predictive control is employed to account for uncertainty in electricity prices and weather information. Under time-varying pricing, scheduling appliances smartly using our scheme can incur notable energy cost saving for customers. As an alternative, we also propose a communication-based control approach which is a joint appliance access and scheduling scheme in which the control algorithms are embedded into the communication protocols used by appliances. The control scheme is based on a threshold maximum power consumption set by the EMC; and we discuss how this threshold can be chosen so that it integrates the availability of local distributed renewable energy resources.Then we investigate demand response in the retail market level which involves interactions between customers and utilities. Pricing-based control and direct load control (DLC) are two types of approaches that are used or envisioned for this level. To address pricing based control methods, we propose real-time pricing (RTP) signals that can be designed to work with customer premise EMCs. The interaction between these EMCs and the pricing-setting utilities is modeled as a Stackelberg game. We demonstrate that our proposed RTP scheme reduces peak load and alleviates rebound peaks that are the typical shortcomings in existing pricing approaches. To address DLC methods, we propose a distributed DLC scheme based on a two-layer communication network infrastructure for large-scale, aggregate DR implementations. In the proposed scheme, average consensus algorithms are employed to distributively allocate control tasks amongst EMCs so that local appliance scheduling within each home will eventually achieve the aggregated control task, i.e., to alleviate mismatch between electricity supply and demand.Finally, we study how demand response affects the wholesale electricity market. As is conventional when studying interactions between electricity generators, we employ the Cournot game model to analyze how DR aggregators may impact wholesale energy markets. To do so, we assume that DR aggregators employ a computationally efficient, centralized scheduling mechanism to manage deferrable load over a large aggregate set of consumers. The load reduction from deferrable load can be seen as `generation\u27 in terms of balancing the market and is compensated as such under current regulatory mandates. Thus, the DR aggregator competes with other generators in a Cournot-Nash manner to make a profit in the wholesale market; and electricity prices are consequently reduced. We provide equilibrium analysis of the wholesale market that includes DR aggregators and demonstrate that under certain conditions the equilibrium exists and is unique