Abstract
Nowadays, human operators at grid control centers analyze a large volume of alarm information during outage’s events, and must act fast to restore the service. Currently, after the occurrence of short-circuit faults and its isolation via feeder protection, fault location and isolation is achieved via remotely controlled switching actions defined by operator’s experience. Despite operator’s experience and knowledge, this makes the process sub-optimal and slower. This paper proposes two novel machine learning-based algorithms to assist human operator decisions, aiming to: i) classify the complexity of a fault occurrence (Occurrences Classifier) based on its alarm events; ii) provide fast insights to the operator on how to solve it (Data2Actions). The Occurrences Classifier takes the alarm information of an occurrence and classifies it as a “simple” or “complex” occurrence. The Data2Actions takes a sequence of alarm information from the occurrence and suggests to the operator the more adequate sequence of switching actions to isolate the fault section on the overhead medium voltage line. Both algorithms were tested in real data from a Distribution System Operator between 2017 and 2020, and showed i) an accuracy of 86% for the Data2Actions, and ii) the Occurrences Classifier reached 74% accuracy for “simple” occurrences and 58% for “complex” ones, leading to an overall 65% accuracy. © 2022 IET Conference Proceedings. All rights reserved.

Abstract
The widespread adoption of distributed energy resources (DER) is creating an opportunity for aggregators to transform DER flexibility into electricity market services. In a scenario of high DER integration, aggregators will need to coordinate the optimisation of DER with the distribution system operator (DSO) in order to avoid congestion and voltage incursions in the distribution networks. This coordination task is notably complex since both network and DER operation are impacted by multiple sources of uncertainty. To address these challenges, this paper proposes a new bidding strategy for aggregators of prosumers to make robust network-secure bidding decisions in day-ahead energy and reserve markets. The bidding strategy computes robust network-secure bids without jeopardising the data privacy of aggregators and the DSO. The data privacy is preserved by using the alternating direction method of multipliers (ADMM) to decompose a stochastic network-secure bidding problem into bidding and network subproblems and solve them separately and in parallel. The uncertainty of the prosumers is incorporated in the bidding problem through scenarios of load, renewable generation, and DER preferences. Our experiments show that the proposed bidding strategy computes robust bids against distribution network problems, outperforming deterministic and stochastic state-of-the-art bidding strategies in terms of cost and network observability.

Abstract
Reducing office buildings' energy consumption can contribute significantly towards carbon reduction commitments since it represents similar to 40% of total energy consumption. Major components of this are lighting, electrical equipment, heating, and central cooling systems. Solid evidence demonstrates that individual occupants' behaviors impact these energy consumption components. In this work, we propose the methodology of using virtual choreographies to identify and prioritize behavior-change interventions for office users based on the potential impact of specific behaviors on energy consumption. We studied the energy-related office behaviors of individuals by combining three sources of data: direct observations, electricity meters, and computer logs. Data show that there are behaviors with significant consumption impact but with little potential for behavioral change, while other behaviors have substantial potential for lowering energy consumption via behavioral change.

Abstract
Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents 10% of total energy consumption. Major components are lighting (40% of consumption), electrical equipment (35%), and heating and central cooling systems (25\%). Occupants’ behaviours impact these energy consumption components, with solid evidence on the role of individual behaviours. In this work, we propose a methodology that uses virtual choreographies to identify and prioritize behaviour-change interventions towards office users based on the potential impact on energy consumption. The data shows that some behaviours with significant consumption have little potential for behavioural change impact, while other behaviours hold substantial potential for lowering energy consumption via behavioural change.

Abstract
To continue to make successful progress towards the achievement of net zero emissions by 2050, a significant number of new facilities based on renewable technologies must continue to be deployed at large scale. However, the integration of large capacities of renewable generation sources into power systems leads to a series of challenges that must be urgently addressed. On the one hand, the intermittent character of renewable resources may lead to imbalances between generation and demand curves, and on the other hand, transmission and distribution system operators will have to carefully consider the impact of reduced power system inertia due to the increase in the number of renewable power plants. Under this framework, stricter technical requirements will be demanded to new power plants that will be integrated into the grid to guarantee quality of electricity supply. These requirements are included within increasingly modern and up-to-date network connection-or grid-codes. Thus, grid codes have a significant role to play in the years to come towards the transition of a more sustainable future, and therefore this paper presents an overview of two grid codes for connecting new generation units across Europe, focusing on the current situation of Iberia. A special emphasis is given on the detailing of certain grid code requirements based on a comparison between the Portuguese and the Spanish grid codes, together with few highlights on the operational procedures for connecting new generation units on both regions. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

Abstract
To modernize distribution networks and enable the energy transition, we need to understand the most appro-priate regulatory approach. A set of new technologies with positive externalities challenge the traditional reg-ulatory models. We develop a decision model to assess firms' incentives to invest in new technologies under different regulatory schemes that consider externality effects. Results show that regulatory schemes under which companies retain the gains (or losses) of achieving (or not) efficiency targets more effectively promote inno-vation investments that reduce network costs. However, a case-by-case approach should be preferred for tech-nologies whose benefits go mostly beyond the network activities.