Abstract
The building sector is responsible for a large share of Europe's energy consumption. Modelling buildings thermal behavior is a key factor for achieving the EU energy efficiency goals. Moreover, it can be used in load forecasting applications, for the prediction of buildings total energy consumption. The first phase of this work is the application of Artificial Immune Systems (AIS) for clustering buildings with similar physical characteristics and similar thermal efficiency. In the second phase, Artificial Neural Networks (ANN) are used to estimate the buildings heating and cooling loads. A final sensitivity test is performed to identify which building features have the most impact on the heating and cooling loads. The results obtained in the first phase revealed very distinct cluster prototypes, which demonstrates the AIS discriminating ability. The good estimation performance obtained in the second phase showed that this approach can be integrated in energy efficiency audits. Finally, the sensitivity analysis provided indications for actions (or legislation directives) in order to promote the design of more efficient buildings. © 2019 IEEE.

Abstract
Transmission Expansion Planning (TEP) problem aims at identifying when and where new equipment as transmission lines, cables and transformers should be inserted on the grid. The transmission upgrade capacity is motivated by several factors as meeting the increasing electricity demand, increasing the reliability of the system and providing non-discriminatory access to cheap generation for consumers. However, TEP problems have been changing over the years as the electrical system evolves. In this way, this paper provides a detailed historical analysis of the evolution of the TEP over the years and the prospects for this challenging task. Furthermore, this study presents an outline review of more than 140 recent articles about TEP problems, literature insights and identified gaps as a critical thinking in how new tools and approaches on TEP can contribute for the new era of renewable and distributed electricity markets.

Abstract
Transmission Expansion Planning (TEP) is traditionally carried out based on long-term forecasts for the peak load, which is viewed as the worst-case scenario. However, with the increasing renewable penetration, the peak load may not be longer the only worst-case to quantify new investment requirements. In fact, high off-peak load scenarios combined with low renewable generation can originate unforeseen bottlenecks. Besides, as TEP is a time-consuming problem, relaxed decision-making processes are often proposed in the literature to address the problem, however there is no guarantee that optimal planning has been achieved when some costs in the decision-making process are neglected. In this sense, this paper proposes a novel methodological framework to ensure that the system is sufficiently robust to overcome conditions with high electricity demand and low renewable energy, furthermore, this paper also presents a broad comparison between the common decision making processes adopted in the TEP literature aiming at providing a more insightful understanding of its impact on the total system cost. The optimization model, which is based on a multi-stage planning strategy, considers an AC-OPF model to enforce operational constrains, including the N-1 contingency criterion. The proposed model is tested through an evolutionary algorithm on a large test system with 118 bus. The uncertainties inherent to wind-solar-hydrothermal systems, demand and the life cycle of generation and transmission equipment are duly considered in the simulations. The results demonstrate the effectiveness of the proposed methodology in providing solution plans able to meet the demand even in scenarios with high off-peak load and low renewable generation, unlike the planning carried out considering only the peak load. Besides, the results also demonstrate that relaxed decision-making models may generate insufficient expansion plans.

Abstract
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers' consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers' usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers' elasticity is effectively utilized.

Abstract
Engaging the residential consumers and providing the best tariffs for their randomized behavior is one of the major barriers to demand response (DR) implementation. Additionally, DR offers submitted by aggregators or retailers are not consumer-specific, which turns it even more difficult for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causal inference between dynamic DR tariffs and observed residential electricity consumption (resolution of 30 minutes) to estimate consumers' consumption elasticity. Ultimately, the aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs.

Abstract
Security and quality of supply continue to be major concern of power system operators. Thus, the expansion of transmission grids is certainly one of the major drivers to achieve this goal. In this scope, this paper presents a three-stage approach to solve the multi-year Transmission Expansion Planning (TEP) problem. This approach uses heuristic algorithms coupled with the Harmony Search (HS) metaheuristic and the Branch & Bound (B&B) algorithm. This hybrid method (HS-B&B) aims at finding the optimal multi-stage investment plan avoiding load shedding over the planning horizon. In this work, the AC-Optimal Power Flow (AC-OPF) is used to model the network as a way to consider the real operation conditions of the system. The method was validated using the Garver and the IEEE RTS 24 bus systems. Results demonstrate the reduction of computational effort without compromising the quality of the TEP.