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.

Abstract
One of the greatest challenges of power system planners is to incorporate the expected growth of Distributed Generation (DG) in operation and expansion studies, while modeling and understanding its impacts. This paper describes and compares the outcomes of two different methodologies to model the impact of the growth of DG using concepts of the Bass theory of diffusion. We are applying both Fixed Adoption and System Dynamics models using public data and the tariff scenarios proposed in a public consultation developed by the Brazilian Electricity Regulation Agency. The results indicate that the System Dynamics approach can be more suitable to conduct this analysis, since it does not have discontinuities. Additionally, the model and numerical results that are obtained can be used to help the regulatory agency to better design tariff schemes in the public consultation process.

Abstract
The Transmission Expansion Planning (TEP) problem aims at identifying a list of equipment, among transmission lines, cables and transformers, that will be installed on the grid over a predefined planning horizon. TEP must also identify the respective periods of installations of the selected pieces-of-equipment to expand the grid. TEP is a mixed-integer non-linear and non-convex problem that requires a huge computational effort to be solved and, for this reason, many authors have been proposing relaxed TEP versions to reduce the mentioned computational burden. In this direction, this paper presents a broad comparison between the relaxed static TEP approach and the complete dynamic TEP. Numerical simulations are conducted using the Garver-6-bus and the RTS-24-bus test systems. The problem is handled by a hybrid tool composed by the Evolutionary Particle Swarm Optimization (EPSO) algorithm and a version of Hill-Climbing (HC), besides an AC-optimal power flow model is used to get more realistic operation conditions of the network. Even though dynamic TEP approaches present a higher computational effort, the results show that with this solution approach it is possible to obtain relevant investment savings when compared with the static TEP approach.

Abstract
The Portuguese generation system includes a large amount of wind parks plus a rapidly increasing capacity in PV units. These units as well as some other technologies are classified as Special Regime Generation, SRG, and are paid feed in tariffs. The operation planning for the next day is based on estimates of these injections made in the morning of the day before operation. Then on the operation day, the wind and PV generations will likely be different originating the activation of reserves, namely automatic Frequency Restoration Reserve, aFRR, and Replacement Reserve, RR. Therefore, the main purpose of this paper is to report the research that was conducted to check whether the large amount of wind and PV capacities explain the amounts of contracted and mobilized aFRR and RR reserves. To do this we used data for 2016 publicly available on the web page of the Portuguese TSO. The clarification of this issue is relevant because the amount of money required to pay reserves has been increasing in recent years and this amount is internalized in the Access Tariffs paid by all Portuguese consumers.