Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution

With the increasing share of renewable and distributed generation in electrical distribution systems, Active Network Management (ANM) becomes a valuable option for a distribution system operator to operate his system in a secure and cost-effective way without relying solely on network reinforcement. ANM strategies are short-term policies that control the power injected by generators and/or taken off by loads in order to avoid congestion or voltage issues. Advanced ANM strategies imply that the system operator has to solve large-scale optimal sequential decision-making problems under uncertainty. For example, decisions taken at a given moment constrain the future decisions that can be taken and uncertainty must be explicitly accounted for because neither demand nor generation can be accurately forecasted. We first formulate the ANM problem, which in addition to be sequential and uncertain, has a nonlinear nature stemming from the power flow equations and a discrete nature arising from the activation of power modulation signals. This ANM problem is then cast as a stochastic mixed-integer nonlinear program, as well as second-order cone and linear counterparts, for which we provide quantitative results using state of the art solvers and perform a sensitivity analysis over the size of the system, the amount of available flexibility, and the number of scenarios considered in the deterministic equivalent of the stochastic program. To foster further research on this problem, we make available at http://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution networks of 5, 33, and 77 buses. These test beds contain a simulator of the distribution system, with stochastic models for the generation and consumption devices, and callbacks to implement and test various ANM strategies

Imitative learning for designing intelligent agents for video games

Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to control agents in these growing environments. Tasks such as world exploration, constrained pathfinding or team tactics and coordination just to name a few are now default requirements for contemporary video games. However, despite its recent advances, video game AI still lacks the ability to learn. In this work, we attempt to break the barrier between video game AI and machine learning and propose a generic method allowing real-time strategy (RTS) agents to learn production strategies from a set of recorded games using supervised learning. We test this imitative learning approach on the popular RTS title StarCraft II and successfully teach a Terran agent facing a Protoss opponent new production strategies

Exploiting the flexibility potential of water distribution networks: A pilot project in Belgium

peer reviewedFlexibility, and in particular, energy storage is expected to assume a key role in the efficient and secure operation of the power system, and thus, in the transition towards a carbonfree electricity sector. In this paper, we propose a methodology for exploiting the flexibility existing in water distribution systems from water storage in reservoirs. The methodology relies first on a modelling approach, from which an optimization problem is defined. The resolution of this optimization problem leads to an operating pattern for the pumps. The methodology assumes that all the electricity is bought on the day-ahead market, where the bids are placed by constructing and solving an optimization problem. The uncertain water consumption and the electricity market prices are predicted using machine learning techniques. The methodology is tested on a real-life water distribution network in Belgium and the results.7. Affordable and clean energ

Extended Equal Area Criterion Revisited: a direct method for fast transient stability analysis

For transient stability analysis of a multi-machine power system, the Extended Equal Area Criterion (EEAC) method applies the classic Equal Area Criterion (EAC) concept to an approximate One Machine Infinite Bus (OMIB) equivalent of the system to find the critical clearing angle. The system-critical clearing time can then be obtained by numerical integration of OMIB equations. The EEAC method was proposed in the 1980s and 1990s as a substitute for time-domain simulation for Transmission System Operators (TSOs) to provide fast, transient stability analysis with the limited computational power available those days. To ensure the secure operation of the power system, TSOs have to identify and prevent potential critical scenarios through offline analyses of a few dangerous ones. These days, due to increased uncertainties in electrical power systems, the number of these critical scenarios is increasing, substantially, calling for fast, transient stability analysis techniques once more. Among them, the EEAC is a unique approach that provides not only valuable information, but also a graphical representation of system dynamics. This paper revisits the EEAC but from a modern, functional point of view. First, the definition of the OMIB model of a multi-machine power system is redrawn in its general form. To achieve fast, transient stability analysis, EEAC relies on approximate models of the true OMIB model. These approximations are clarified, and the EAC concept is redefined with a general definition for instability, and its conditions. Based on the defined conditions and definitions, functions are developed for each EEAC building block, which are later put out together to provide a full-resolution, functional scheme. This functional scheme not only covers the previous literature on the subject, but also allows to introduce several possible new EEAC approaches and provides a detailed description of their implementation procedure. A number of approaches are applied to the French EHV network, and the approximations are examined

Active network management for electrical distribution systems: problem formulation and benchmark

In order to operate an electrical distribution network in a secure and cost-efficient way, it is necessary, due to the rise of renewable energy-based distributed generation, to develop Active Network Management (ANM) strategies. These strategies rely on short-term policies that control the power injected by generators and/or taken of by loads in order to avoid congestion or voltage problems. While simple ANM strategies would curtail the production of generators, more advanced ones would move the consumption of loads to relevant time periods to maximize the potential of renewable energy sources. However, such advanced strategies imply solving large-scale optimal sequential decision-making problems under uncertainty, something that is understandably complicated. In order to promote the development of computational techniques for active network management, we detail a generic procedure for formulating ANM decision problems as Markov decision processes. We also specify it to a 75-bus distribution network. The resulting test instance is available at http://www.montefiore.ulg.ac.be/~anm/ . It can be used as a test bed for comparing existing computational techniques, as well as for developing new ones. A solution technique that consists in an approximate multistage program is also illustrated on the test instance

Relaxations for multi-period optimal power flow problems with discrete decision variables

peer reviewedWe consider a class of optimal power flow (OPF) applications where some loads offer a modulation service in exchange for an activation fee. These applications can be modeled as multi-period formulations of the OPF with discrete variables that define mixed-integer non-convex mathematical programs. We propose two types of relaxations to tackle these problems. One is based on a Lagrangian relaxation and the other is based on a network flow relaxation. Both relaxations are tested on several benchmarks and, although they provide a comparable dual bound, it appears that the constraints in the solutions derived from the network flow relaxation are significantly less violated.GREDO

Active Network Management for Electrical Distribution Systems

With the increasing share of renewable and distributed generation in electrical distribution systems, Active Network Management (ANM) has become a valuable option for a distribution system operator to operate his system in a secure and cost-effective way without relying solely on network reinforcement. ANM strategies are short-term policies that control the power injected by generators and/or taken off by loads in order to avoid congestion or voltage issues. While simple ANM strategies consist of curtailing temporary excess generation, more advanced strategies instead attempt to move the consumption of loads to anticipated periods of high renewable generation. Such advanced strategies mean that the system operator has to solve large-scale optimal sequential decision-making problems under uncertainty. The problems are sequential for several reasons. For example, decisions taken at a given moment constrain the future decisions that can be taken, and decisions should be communicated to the system’s actors sufficiently in advance to give them enough time for implementation. Uncertainty must be explicitly accounted for because neither demand nor generation can be accurately forecasted. This dissertation presents various research contributions about ANM for distribution systems. These contributions range from the motivation of using a framework of sequential decision-making under uncertainty to the study of computational methods that implement ANM strategies. A particular emphasis is placed on the formulation of the problem, which ultimately falls within the class of Markov decision processes. The modeling of stochasticity is explored and a novel approach that relies on a Gaussian Mixture Model is presented. Computational methods including several relaxations and approximations of multi-period and multi-scenario extensions of the optimal power flow problem with discrete decision variables were considered.Avec la part croissante de production renouvelable et distribuée dans les réseaux électriques de distribution, la gestion active des réseaux de distribution devient une option crédible pour permettre aux gestionnaires de réseaux de distribution d’opérer leurs systèmes électriques. Les stratégies de gestion active sont des politiques de contrôle à court terme qui modulent la puissance injectée par les générateurs et/ou consommée par les charges afin d’éviter des problèmes de congestion ou de tension. Si les stratégies les plus simples se contentent de réduire les excès temporaires de production, d’autres plus complexes visent plutôt à anticiper les périodes de forte production renouvelable pour y déplacer la consommation des charges. De telles stratégies signifient que le gestionnaire de réseau doit résoudre des problèmes de prise de décisions séquentielles sous incertitude et de grande taille. Ces problèmes sont séquentiels pour plusieurs raisons. Par exemple, certaines décisions prises à un instant donné contraignent les décisions qui peuvent être prises dans le futur. Les décisions doivent également être communiquées suffisamment à l’avance aux acteurs du système pour leur laisser le temps de les implémenter. L’incertitude doit être explicitement prise en compte à cause de l’imprécision des prévisions de consommation et de production. Cette dissertation présente des contributions de recherche en gestion active des réseaux électriques de distribution. Ces contributions abordent notamment la motivation du cadre de décisions séquentielles sous incertitude et l’étude des méthodes de calcul qui implémentent les stratégies de gestion active. Une attention particulière est portée sur la formulation du problème, qui est finalement présenté comme un processus de décision markovien. Une approche originale reposant sur un modèle de mélange gaussien est décrite pour représenter l’incertitude. Des méthodes de calcul sont également considérées, en particuliers différentes relaxations et approximations d’extensions multi-périodes et multi-scénarios du problème d’écoulement de puissance optimal avec des variables entières

Active network management: planning under uncertainty for exploiting load modulation

peer reviewedThis paper addresses the problem faced by a distribution system operator (DSO) when planning the operation of a network in the short-term. The problem is formulated in the context of high penetration of renewable energy sources (RES) and distributed generation (DG), and when flexible demand is available. The problem is expressed as a sequential decision-making problem under uncertainty, where, in the first stage, the DSO has to decide whether or not to reserve the availability of flexible demand, and, in the subsequent stages, can curtail the generation and modulate the available flexible loads. We analyze the relevance of this formulation on a small test system, discuss the assumptions made, compare our approach to related work, and indicate further research directions.GREDO

Towards the Minimization of the Levelized Energy Costs of Microgrids using both Long-term and Short-term Storage Devices

peer reviewedThis chapter falls within the context of the optimization of the levelized energy cost (LEC) of microgrids featuring photovoltaic panels (PV) associated with both long-term (hydrogen) and short-term (batteries) storage devices. First, we propose a novel formalization of the problem of building and operating microgrids interacting with their surrounding environment. Then we show how to optimally operate a microgrid using linear programming techniques in the context where the consumption and the production are known. It appears that this optimization technique can also be used to address the problem of optimal sizing of the microgrid, for which we propose a robust approach. These contributions are illustrated in two different settings corresponding to Belgian and Spanish data

A Gaussian mixture approach to model stochastic processes in power systems

peer reviewedProbabilistic methods are emerging for operating electrical networks, driven by the integration of renewable generation. We present an algorithm that models a stochastic process as a Markov process using a multivariate Gaussian Mixture Model, as well as a model selection technique to search for the adequate Markov order and number of components. The main motivation is to sample future trajectories of these processes from their last available observations (i.e. measurements). An accurate model that can generate these synthetic trajectories is critical for applications such as security analysis or decision making based on lookahead models. The proposed approach is evaluated in a lookahead security analysis framework, i.e. by estimating the probability of future system states to respect operational constraints. The evaluation is performed using a 33-bus distribution test system, for power consumption and wind speed processes. Empirical results show that the GMM approach slightly outperforms an ARMA approach.GREDO