Model-based predictive control methods for distributed energy resources in smart grids.

This thesis develops optimization-based techniques for the control of distributed energy resources to provide multiple services to the power network. It is divided into three parts.

The first part of this thesis focuses on the development of a framework for the efficient control of a single resource that is subject to the effect of periodic stochastic uncertainties. More specifically, resources that can be described by the general class of periodic constrained linear systems are considered and a method, based on Stochastic MPC, to control the over-time-average constraint violations is developed. Finally, the effectiveness of the control framework is tested, by means of a simulation analysis, for the case of the climate control of a building.

The second part of the thesis introduces the required background for the electric power grid, energy markets, and distributed energy resources providing grid support services. First, the control problem of scheduling the operation of a set of energy resources offering multiple services to the grid is formally stated as a multi-stage uncertain optimization problem. In particular, the problem is designed so as to maximize the provision of a shared tracking service while enforcing the satisfaction of the operational constraints on both the individual resources, as well as on the hosting distribution network. Two computationally tractable approximated solution methods are then presented, which are based on robust-optimization techniques and on a linearization of the power flow equations around a general linearization point. A simulation-based analysis demonstrates the capability of the proposed framework to adapt to different levels of uncertainty acting on the overall system. Finally, a quantitative and qualitative comparison between the two approximation schemes is presented and general guidelines are given.

The last part of the thesis demonstrates the practical relevance of the control framework developed in Part II. In particular, the aggregation of an electrical battery system and of an office building is considered, and two case studies are investigated. The first deals with the provision of secondary frequency control in the Swiss market, whereas the second deals with the problem of dispatching the operation of an active distribution feeder characterized by the presence of stochastic prosumers. In both cases, we show how to adapt the general framework of Part II so as to accommodate the given application. Then, we design a hierarchical multi-timescale controller in order to reliably deliver the service by coordinating the controllable resources during real-time operation.

The results of both experimental campaigns demonstrate the effectiveness and robustness of the control methodology against the wide range of uncertainty involved. In fact, in both cases, high-quality tracking performance could be achieved without jeopardizing the occupants' comfort in the building nor violating the operational constraints of the battery. Finally, the results also show the benefit of combining resources with complementary technical capabilities as the building and the battery.

Advanced Frameworks to Aggregate and Unlock the Power Flexibility of Distributed Energy Resources Located in Active Distribution Networks

Mohsen Kalantar Neyestanaki

Environmental concerns are leading to a fast transition in electric power systems towards replacing fossil fuel and nuclear generation with renewable generation. This transition has two significant implications for electric power systems:1-The capacity of conventional dispatchable electric power plants, which are the main providers of power flexibility , is sharply falling. It is notable that the security of electric power systems can be preserved if and only if there is an adequate amount of power flexibility to guarantee the frequency stability, voltage regulation, power quality, and congestion management.2-The share of renewable energy sources (RESs) in the electric power generation portfolio is steeply soaring. It raises a significant stress on electric power systems due to the fact that the generated electricity from RESs is intrinsically intermittent and accompanied with uncertainties.The simultaneous realization of these two issues puts electric power systems in a turning point. This thesis devotes a deep attention to the power flexibility provision issue with the purpose of empowering transmission system operators (TSOs) and distribution system operators (DSOs) to steer electric power systems securely in this emerging architecture. To this end, it sets out to unlock the potential power flexibility of distributed energy resources (DERs) located in distribution networks. The first part of the thesis deals with the problem from TSO's perspective. It develops two decision making tools for TSOs to help them optimally quantify their required power flexibility from flexibility providers (including flexible distribution networks) in a decentralized energy and flexibility market structure. The first tool concentrates solely on active power flexibility and accordingly offers a framework to model aggregated active power flexibility of distribution networks from TSO's point of view. In contrast, the second tool introduces a holistic TSO-DSO coordination framework to enable TSO and DSO to exchange both active/reactive power flexibility. Both developed tools employ mathematical techniques to cast the TSO's decision making process as a two-stage linear stochastic optimization problem while accounting for risk associated to the uncertainties. The frameworks lay a ground for TSO and DSOs to exchange power flexibility without having to reveal their confidential grids data.The second part of the thesis deals with the problem from DSO's perspective. It first concentrates on an active distribution network (ADN) and constructs a set of linear scenario-based robust optimization problems to characterize the maximum active/reactive powers flexibility that the ADN can provide upon request at its point of common coupling (PCC) to the upper-layer grid. Second, it constructs a linear grid&uncertainties-cognizant ADN management method to determine the amount of active/reactive powers flexibility that should be provided by each DER during the real-time operation in such a way that the ADN can provide, with minimum deviation, the active/reactive powers flexibility requested by the upper-layer grid at the PCC.In addition to dealing with the problem from both TSO's and DSO's perspectives, a privileged feature of the thesis is that it offers linear tractable algorithms to deal with all above-mentioned tasks while considering grid's constraints and uncertainties stemming from the forecast errors of demand and renewable generation.

EPFL2022

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

A Two-Time-Scale Control Scheme for Fast Systems

Dominique Bonvin, Davide Buccieri, Sébastien Gros, Philippe Müllhaupt

Model predictive control (MPC) is a very effective approach to control nonlinear systems, especially when the systems are high dimensional and/or constrained. MPC formulates the problem of input trajectory generation as an optimization problem. However, due to model mismatch and disturbances, frequent re-calculation of the trajectories is typically called for. This paper proposes a two-time-scale control scheme that uses less frequent repeated trajectory generation in a slow loop and time-varying linear feedback in a faster loop. Since the fast loop reduces considerably the effect of uncertainty, trajectory generation can be done much less frequently. The problem of trajectory generation can be treated using either optimization-based MPC or flatness-based system inversion. As proposed, the method cannot handle hard constraints. Both MPC and the two-time-scale control scheme are tested via the simulation of a flying robotic structure. It is seen that the MPC scheme is too slow to be considered for real-time implementation on a fast system. In contrast, the two-time-scale control scheme is fast, effective, and robust.