Efthymios Karangelos

Active network management: Planning under uncertainty for exploiting load modulation

This 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.

Whither probabilistic security management for real-time operation of power systems?

This paper investigates the stakes of introducing probabilistic approaches for the management of power systems security. In real-time operation, the aim is to arbitrate in a rational way between preventive and corrective control, while taking into account i) the prior probabilities of contingencies, ii) the possible failure modes of corrective control actions, iii) the socio-economic consequences of service interruptions. This work is a first step towards the construction of a globally coherent decision making framework for security management from long-term system expansion, via mid-term asset management, towards short-term operation planning and real-time operation.

Probabilistic Reliability Management Approach and Criteria for power system real-time operation

This paper develops a probabilistic approach for power system reliability management in real-time operation where risk is a product of i) the potential occurrence of contingencies, ii) the possible failure of corrective (i.e., post-contingency) control and, iii) the socio-economic impact of service interruptions to end-users. Stressing the spatiotemporal variability of these factors, we argue for reliability criteria assuring a high enough probability of avoiding service interruptions of severe socio-economic impact by dynamically identifying events of non-negligible implied risk. We formalise the corresponding decision making problem as a chance-constrained two-stage stochastic programming problem, and study its main features on the single area IEEE RTS-96 system. We also discuss how to leverage this proposal for the construction of a globally coherent reliability management framework for long-term system development, midterm asset management, and short-term operation planning.

Participation of electric heat pump resources in electricity markets under uncertainty

This paper presents a model for calculating the optimal purchasing strategy in a day ahead market for an aggregation of domestic buildings utilising electric heat pumps (EHP) to supply low grade thermal energy (for space heating and domestic hot water). The model includes physical models of buildings and of thermal energy stores (TES). Uncertainty in outdoor temperature (hence space heating demand and imbalance volume) and imbalance prices is modelled using a stochastic programming approach, whilst uncertainty in non-heating electricity and domestic hot water demand, and building occupancy (which is a determinant of space heating demand) is accounted for through random assignation of synthetic profiles to buildings/scenarios. The effect on the purchasing costs of the presence and size of a TES and the effect of the size of the EHP are tested.

Machine learning of real-time power systems reliability management response

In this paper we study how supervised machine learning could be applied to build simplified models of realtime (RT) reliability management response to the realization of uncertainties. The final objective is to import these models into look-ahead operation planning under uncertainties. Our response models predict in particular the real-time reliability management costs and the resulting reliability level of the system. We tested our methodology on the IEEE-RTS96 benchmark. Among the supervised learning algorithms tested, extremely randomized trees, kernel ridge regression and neural networks appear to be the best methods for this application. Furthermore, by using feature “importances” computed by tree-based ensemble methods, we were able to extract the most relevant variables to predict the response of real-time reliability management, and thus obtain a better understanding of the system properties.

Framework for Threat Based Failure Rates in Transmission System Operation

Reliability of electrical transmission systems is presently managed by applying the deterministic N-1 criterion, or some variant thereof. This means that transmission systems are designed with at least one level of redundancy, regardless of the cost of doing so, or the severity of the risks they mitigate. In an operational context, the N-1 criterion provides a reliability target but it fails to accurately capture the dynamic nature of shortterm threats to transmission systems. Ongoing research aims to overcome this shortcoming by proposing new probabilistic reliability criteria. Such new criteria are anticipated to rely heavily on component failure rate calculations. This paper provides a threat modelling framework, using the Icelandic transmission system as an example, highlighting the need for improved data collection and failure rate modelling. The feasibility of using threat credibility indicators to achieve spatio-temporal failure rates, given minimal data, is explored in a case study of the Icelandic transmission system. The paper closes with a discussion on the assumptions and simplifications that are implicitly made in the formulation, and the additional work required for such an approach to be included in existing practices. Specifically, this paper is concerned only with short term and real-time management of electrical transmission systems.

A computational model of mid-term outage scheduling for long-term system studies

This paper presents a computational model of the mid-term outage scheduling process of electric power transmission assets, to be used in long-term studies such as maintenance policy assessments and system development studies, while accounting for the impact of outage schedules on short-term system operation. We propose a greedy algorithm that schedules the outages one by one according to their impact on system operation estimated via Monte-Carlo simulations. The model is implemented in JULIA and applied to the IEEE RTS-96.

Integrating Demand Response into agent-based models of electricity markets

This paper introduces a model for the decision-making of a Demand Response (DR) program operator as an adaptive agent participating in a competitive electricity market. The model focuses on an electricity retailer stimulating DR as a means of avoiding high balancing market prices. Nevertheless, we demonstrate the ability to extend the model to other actors who could capitalize on the value of demand flexibility - e.g. wind power producers looking to offset output variability. The model considers voluntary demand modifications whose materialization, subject to the uncertainty of consumer behavior, results in redistribution of consumption over a short time frame. As the retailer is modeled via an adaptive agent, it has the potential to learn from both consumer behavior and market outcomes. Here, we implement a reinforcement learning approach with the objective of allowing the agent to increase its profit by identifying the conditions under which DR should be stimulated. We validate the proposed agent-based model as a tool to quantify DR potential in a market setting.

Near real-life pilot testing of real-time probabilistic reliability assessments

This paper introduces a probabilistic reliability management approach and describes a pilot test planned by the Icelandic transmission system operator, Landsnet, in early 2017, as part of the EU GARPUR project. The pilot test will assess the viability of the approach and criteria proposed by GARPUR, in the context of real-time system operation. The mathematical formulation of reliability assessment being studied in the pilot test and the required algorithms are outlined. Data and tools needed for the pilot test are detailed, identifying the required assumptions and simplifications where existing data and tools are lacking. Finally, the preliminary methodology of the pilot test is described, followed by a discussion of the expected value of the pilot test. It is anticipated that the pilot test will provide insights into data and modelling needs for the implementation of probabilistic risk management approaches and criteria in transmission system operation.