Réseau électrique intelligent

Un réseau électrique intelligent, ou smart grid en anglais, est un réseau de distribution d'électricité qui favorise la circulation d’information entre les fournisseurs et les consommateurs afin d’ajuster le flux d’électricité en temps réel et d'en permettre une gestion plus efficace. Ce type de réseaux intelligents utilise des techniques informatiques pour optimiser la production, la distribution, la consommation et éventuellement le stockage de l'énergie afin de mieux coordonner l'ensemble des mailles du réseau électrique, du producteur au consommateur final. Il améliore l'efficacité énergétique de l'ensemble en minimisant les pertes en ligne et en optimisant le rendement des moyens de production utilisés, en rapport avec la consommation instantanée. Les techniques informatiques, associées à des dispositifs de stockage d'énergie et éventuellement d'économies d'énergie, permettent notamment de lisser et tamponner les pointes de production et de consommation, en diminuant les capac

On Smart-Buildings and their Integration into the Smart-Grid

Olivier Valentin Henri Van Cutsem

Today's electrical grid is undergoing deep changes, resulting from the large integration of distributed Renewable Energy Sources (RES) in an effort to decarbonize the generation of electrical energy. In addition to the emergence of this volatile electricity production, the worldwide demand for electricity increases due to a growing population and the intensified electrification of buildings. Smart-buildings represent promising assets for supporting the electrical grid in balancing demand with a supply based on non-dispatchable RES. A smart-building denotes a building equipped with sensor/actuator hardware connected to a federating Building Data Management System (BDMS) which enables high-level applications and services. Tapping into the flexibility inherent to its various entities (load, storage, and generation), a smart-building can provide Demand Response (DR) functionality through the optimization of its energy profile in response to varying electricity prices or commands from the grid.This PhD thesis provides a set of tools, algorithms, and frameworks, revolving around the notion of smart-buildings that foster an enhanced Building-to-Grid (BtG) integration. The tools developed here aim to fill the gap encountered in the literature created by the recent rollout of BDMSs and the ubiquitous Internet of Things (IoT). Furthermore, the mismatch between current DR and the future RES-based smart-grid opens the way to the development of innovative algorithms and frameworks to manage the flexibility offered by smart-buildings for grid-side agents. Built upon BDMSs, two open-source tools have been developed. Firstly, an integrated high-speed emulation and simulation software, dubbed Virtualization Engine (vEngine), allows the simulation of non-existing components of a building directly on-site. The multi-threaded, light architecture of vEngine permits efficient simulations, in a modular environment conceived for developers. Secondly, we describe Open Energy Management System (OpenEMS), a platform that seamlessly connects to any existing BDMS and provides its users with an environment to create their own energy management algorithms, with a focus on Model Predictive Control (MPC). Simulations using a realistic Swiss residential building model demonstrate the effectiveness and modularity of both tools. Additionally, we propose a multi-state load profile identification algorithm tailored to Non-Intrusive Load Monitoring (NILM). Applied to energy disaggregation, it shows promising results for enhanced energy feedbacks to the occupants. To attain daily energy balance within the smart-grid, we propose several algorithms and energy management frameworks, using smart-buildings. An incremental MPC formulation is derived to better balance monthly costs associated to energy and peak demand of large commercial buildings. Simulations data show substantial benefits, for both the building's owner and the grid. Furthermore, we present a decentralized framework for autonomously managing the energy in a community of smart-buildings, with RES. Based on blockchain technology and smart-contracts, the framework optimizes an objective common to the whole community without the need for a central agent. Finally, we suggest a unified BtG model that could benefit grid-side aggregators in both microgrids and electricity markets.

EPFL2020

Coordinated Optimization and Control for Smart Grids

Altug Bitlislioglu

In this thesis, we consider commercial buildings with available heating, ventilation and air conditioning (HVAC) systems, and develop methods to assess and exploit their energy storage and production potential to collectively offer ancillary services to the power grid. This demand response problem can be put in the generic framework of multi-agent optimization and control. In this setting, various agents interact through their objectives, constraints or dynamics over a network. In the example of demand response, individual buildings are connected to the power network and coupled via their common objective of providing service and the constraints of the power network. Within this generic multi-agent framework, we develop layers of abstractions that enables efficient coordination of the agents while making sure that the network constraints are satisfied, and the common goal of the agents is achieved. The first approach is based on quantifying the tracking capability of a local system using robust optimization. Different from a standard robust optimization problem, we modify and optimize over the uncertainty set that represents the set of reference trajectories the system is required to track while rejecting external disturbances. The method facilitates hierarchical control by using reference sets for coordinating many agents. In the second approach, we consider coordination of multiple agents by using local cost and constraint approximations. Specifically we consider decomposition of interior point methods in a multi-agent setting and analyze the computation and modeling task for the agents and the coordinator. We further consider decomposition of state of the art predictor-corrector type interior point methods and show that a naive implementation may result in excessive communication in a multi-agent setting. In order to remedy this issue, we propose a modification of the standard algorithm that uses decentralized predictions. We analyze convergence of the method and test the performance with numerical experiments. Finally, we look into applying decomposition based interior point methods in a distributed model predictive control problem that includes dynamic coupling between the agents. Instead of solving the problem to optimality, adding barrier functions to the objective enhances numerical performance significantly, an approach that is well-known in model predictive control (MPC) literature. We consider applying this method in economic MPC problems with terminal equilibrium constraints, which is suitable for decomposition due to the simplicity of terminal constraints. However in this case standard results for MPC with barrier functions do not apply. We propose iterative re-centering of the barriers, which allows interpreting them as a regularizing cost in the problem that penalizes deviation from open-loop predictions. We show that regularizing barrier functions not only improve the numerical performance and facilitate decomposition, but also enhance system theoretical properties.

EPFL2018

Cybersecurity Solutions for Active Power Distribution Networks

Teklemariam Tsegay Tesfay

An active distribution network (ADN) is an electrical-power distribution network that implements a real-time monitoring and control of the electrical resources and the grid. Effective monitoring and control is realised by deploying a large number of sensing and actuating devices and a communication network to facilitates the two-way transfer of data. The reliance of ADN operations on a large number of electronic devices and on communication networks poses a challenge in protecting the system against cyber-attacks. Identifying these challenges and commissioning appropriate solutions is of utmost importance to realize the full potential of a smart grid that seamlessly integrates distributed generation, such as renewable energy sources. As a first step, we perform a thorough threat analysis of a typical ADN. We identify potential threats against field devices, the communication infrastructure and servers at control centers. We also propose a check-list of security solutions and best practices that guarantee a distribution network's resilient operation in the presence of malicious attackers, natural disasters, and other unintended failures that could potentially lead to islanded communication zone. For the next step, we investigate the security of MPLS-TP, a technology that is mainly used for long-distance inter-domain communication in smart grid. We find that an MPLS-TP implementation in Cisco IOS has serious security vulnerabilities in two of its protocols, BFD and PSC. These two protocols control protection-switching features in MPLS-TP. In our test-bed, we demonstrate that an attacker who has physical access to the network can exploit the vulnerabilities in order to inject forged BFD or PSC messages that affect the network's availability. Third, we consider multicast source authentication for synchrophasor data communication in grid monitoring systems (GMS). Ensuring source authentication without violating the stringent real-time requirement of GMS is challenging. Through an extensive review of existing schemes, we identified a set of schemes that satisfy some desirable requirements for GMS. The identified schemes are ECDSA, TV-HORS and Incomplete- key-set. We experimentally compared these schemes using computation, communication and key management overheads as performance metrics. A tweak in ECDSA's implementation to make it use pre-generated tokens to generate signatures significantly improves the computation overhead of ECDSA, making it the preferred scheme for GMS. This finding is contrary to the generally accepted view that asymmetric cryptography is inapplicable for real-time systems. Finally, we studied a planning problem that arises when a utility wants to roll out a software patch that requires rebooting to all PMUs while maintaining system observability. The problem we address is how to find a partitioning of the set of the deployed PMUs into as few subsets as possible such that all the PMUs in one subset can be patched in one round while all the PMUs in the other subsets provide full observability. We show that the problem is NP-complete in the general case and and formulated it as binary integer linear programming (BILP) problem. We have also provided an heuristic algorithm to find an approximate solution. Furthermore, we have identified a special case of the problem where the grid is a tree and provided a polynomial-time algorithm that finds an optimal patching plan that requires only two rounds to patch the PMUs.

EPFL2017