Privacy-Friendly Load Scheduling of Deferrable and Interruptible Domestic Appliances in Smart Grids

The massive integration of renewable energy sources in the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable and/or interruptible electrical appliances. However, communicating the users' needs about the usage of their appliances also leaks sensitive information about their habits and lifestyles, thus arising privacy concerns. This paper proposes a framework to allow the coordination of energy consumption without compromising the privacy of the users: the service requests generated by the domestic appliances are divided into crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol by a set of schedulers, which schedule the requests by directly operating on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained by means of an Integer Linear Programming formulation

Distributed demand side management with battery storage for smart home energy scheduling

Abstract: The role of Demand Side Management (DSM) with Distributed Energy Storage (DES) has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS) algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU) pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR) demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness

Privacy-friendly appliance load scheduling in smart grids

Abstract—The massive integration of renewable energy sources into the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable electrical appliances. However, communicating the users ’ needs about the usage of the electrical appliances leaks sensitive information about habits and lifestyles of the customers, thus arising privacy concerns. This paper proposes a privacy-preserving framework to allow the coordination of energy con-sumption without compromising the privacy of the users: the ser-vice requests generated by the domestic appliances are diveded in crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol based on Crowds by a set of schedulers, which schedule the requests operating directly on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained through an Integer Linear Programming formulation. I

Demand Dispatch Control for Balancing Load with Generation

There are different methods to implement demand management. In this thesis, a Demand Side Frequency Droop is proposed to calculate the require power reduction. Moreover, Demand Dispatch (DD) can provide ancillary service to the grid and maintains the power system frequency. Besides, to improve the operation of DD, the renewable resources and the storage devices are integrated to the DD. The proposed methods in this thesis have been validated through PSCAD software simulation and MATLAB

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

An Energy Management Service for the Smart Office

The evolution of the electricity grid towards the smart grid paradigm is fostering the integration of distributed renewable energy sources in smart buildings: a combination of local power generation, battery storage and controllable loads can greatly increase the energetic self-sufficiency of a smart building, enabling it to maximize the self-consumption of photovoltaic electricity and to participate in the energy market, thus taking advantage of time-variable tariffs to achieve economic savings. This paper proposes an energy management infrastructure specifically tailored for a smart office building, which relies on measured data and on forecasting algorithms to predict the future patterns of both local energy generation and power loads. The performance is compared to the optimal energy usage scheduling, which would be obtained assuming the exact knowledge of the future energy production and consumption trends, showing gaps below 10% with respect to the optimum

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

A privacy-friendly gaming framework in smart electricity and water grids

Serious games can be used to push consumers of common-pool resources toward socially responsible consumption patterns. However, gamified interactions can result in privacy leaks and potential misuses of player-provided data. In the Smart Grid ecosystem, a smart metering framework providing some basic cryptographic primitives can enable the implementation of serious games in a privacy-friendly manner. This paper presents a smart metering architecture in which the users have access to their own high-frequency data and can use them as the input data to a multi-party secure protocol. Authenticity and correctness of the data are guaranteed by the usage of a public blockchain. The framework enables a gaming platform to administer a set of team game activities aimed at promoting a more sustainable usage of energy and water. We discuss and assess the performance of a protocol based on Shamir secret sharing scheme, which enables the members of the teams to calculate their overall consumption and to compare it with those of other teams without disclosing individual energy usage data. Additionally, the protocol impedes that the game platform learns the meter readings of the players (either individual or aggregated) and their challenge objectives

Privacy-preserving Overgrid: Secure Data Collection for the Smart Grid

In this paper we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation ( extit{PP-Overgrid}). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of ``flexible'' energy consumption, i.e. the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildin

Privacy-preserving overgrid: Secure data collection for the smart grid

In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings