Abstract
We hear more and more about autonomous vehicles, however, where are we heading with automation? Recently, we have heard about some crashes of autonomous vehicles, which have occurred during trials. Therefore, can this technology be trusted? Equally important, what kind of moral and ethical questions are behind the decisions that a vehicle like this must make? Who should be saved if someone must die, in a car accident? The elderly, or perhaps younger people? Executives or the homeless? People who follow traffic rules, or is that not relevant? This article will start by reviewing some of the ethical questions that have already been raised and results found within the public domain. Then the article proceeds by discussing our survey, administered in Portugal, with 111 answers. For situations which would seemingly lead to an obvious consensus and to a 100% agreement on the matter, we found this to not be the case, as our survey never achieved such polarized results. For example, even when confronted with saving pet animals or children, there was still a split in the choice made (albeit pending heavily towards saving the children). This goes to show how people are different and make different choices in life. Are we superior to animals and do we have a greater right to life than pets? Of course, this is debatable, according to differing values and cultures. Therefore, it follows that much debate should ensue as to how to program autonomous vehicles to behave - in case the loss of life (in whatever form) is at stake. Automation, as such, is thus leading us down ambiguous avenues where grey areas abound and we may simply not know what is best all of the time. Automation is thus making us work harder at being human beings and is bringing us to new levels of rationality - where emotions should also play a big part. The spontaneity and intuitiveness of human decision-making being taken out of the equation (when an accident occurs without automated cars that is how we think) makes many pre-meditated decisions uncertain and unpopular. Profoundly humane issues, which are often culture specific, are thus yet to be discussed.

Abstract
The interest in modeling the operation of large-scale battery energy storage systems (BESS) for analyzing power grid applications is rising. This is due to the increasing storage capacity installed in power systems for providing ancillary services and supporting nonprogrammable renewable energy sources (RES). BESS numerical models suitable for grid-connected applications must offer a trade-off, keeping a high accuracy even with limited computational effort. Moreover, they are asked to be viable in modeling for real-life equipment, and not just accurate in the simulation of the electrochemical section. The aim of this study is to develop a numerical model for the analysis of the grid-connected BESS operation; the main goal of the proposal is to have a test protocol based on standard equipment and just based on charge/discharge tests, i.e., a procedure viable for a BESS owner without theoretical skills in electrochemistry or lab procedures, and not requiring the ability to disassemble the BESS in order to test each individual component. The BESS model developed is characterized by an experimental campaign. The test procedure itself is framed in the context of this study and adopted for the experimental campaign on a commercial large-scale BESS. Once the model is characterized by the experimental parameters, it undergoes the verification and validation process by testing its accuracy in simulating the provision of frequency regulation. A case study is presented for the sake of presenting a potential application of the model. The procedure developed and validated is replicable in any other facility, due to the low complexity of the proposed experimental set. This could help stakeholders to accurately simulate several layouts of network services.

Abstract
In this paper, demand-side management (DSM) is performed through demand response aggregators (DRAs) in an uncertain environment within zonal price market framework. The proposed scheme aims to allow cross-border electricity trading and optimize interconnections usage as well as to obtain optimum DR volume from the perspective of the Market Coupling Operator (MCO). The market consists of several zonal price markets as Nominated Electricity Market Operators (NEMO) who run their day-ahead and balancing market internally and communicate the information to the MCO to provide the cooperation with other NEMOs. To this end, a stochastic two-stage model is formulated in which the total operation cost from MCO's viewpoint is minimized. Accordingly, the model aims to consider day-ahead decisions in the first stage and balancing decisions in the second stage. Furthermore, the intermittent nature of renewable sources generation is handled by scenario generation with Monte-Carlo Simulation (MCS) method. NEMOs are physically connected as radial network. Therefore, all relative network constraints are taken into account as a linear power flow for radial networks. The results of the implementation of the proposed model demonstrate the effectiveness of various DR biddings on hourly DR volume, hourly DR cost and power exchange between different NEMOS. © 2019 IEEE.

Abstract
This study proposes a novel multi-agent method for electric vehicle (EV) owners who will take part in the electricity market. Each EV is considered as an agent, and all the EVs have vehicle-to-grid capability. These agents aim to minimise the charging cost and to increase the privacy of EV owners due to omitting the aggregator role in the system. Each agent has two independent decision cores for buying and selling energy. These cores are developed based on a reinforcement learning (RL) algorithm, i.e. Q-learning algorithm, due to its high efficiency and appropriate performance in multi-agent methods. Based on the proposed method, agents can buy and sell energy with the cost minimisation goal, while they should always have enough energy for the trip, considering the uncertain behaviours of EV owners. Numeric simulations on an illustrative example with one agent and a testing system with 500 agents demonstrate the effectiveness of the proposed method.

Abstract
We consider the problem of determining the optimal aggregate power consumption of a population of thermostatically controlled loads such as air conditioners. This is motivated by the need to synthesize the demand response for a load serving entity (LSE) catering a population of such customers. We show how the LSE can opportunistically design the aggregate reference consumption to minimize its energy procurement cost, given day-ahead price, load forecast, and ambient temperature forecast, while respecting each individual load's comfort range constraints. The resulting synthesis problem is intractable when posed as a direct optimization problem after Euler discretization of the dynamics, since it results in a mixed-integer linear programming problem with number of variables typically of the order of millions. In contrast, in this paper, we show that the problem is amenable to continuous-time optimal control techniques. Numerical simulations elucidate how the LSE can use the optimal aggregate power consumption trajectory thus computed, for the purpose of demand response.

Abstract
This study examined three-dimensional, volumetric mean velocity fields and corresponding performance measurements for an isolated vertical-axis wind turbine (VAWT) and for co- and counter-rotating pairs of VAWTs with varying incident wind direction and turbine spacings. The purpose was to identify turbine configurations and flow mechanisms that can improve the power densities of VAWT arrays in wind farms. All experiments were conducted at a Reynolds number of ReD = 7.3 × 10⁴. In the paired arrays, performance enhancement was observed for both the upstream and downstream turbines. Increases in downstream turbine performance correlate with bluff–body accelerations around the upstream turbine, which increase the incident freestream velocity on the downstream turbine in certain positions. Decreases in downstream turbine performance are determined by its position in the upstream turbine’s wake. Changes in upstream turbine performance are related to variations in the surrounding flow field due to the presence of the downstream rotor. For the most robust array configuration studied, an average 14% increase in array performance over approximately a 50^? range of wind direction was observed. Additionally, three-dimensional vortex interactions behind pairs of VAWT were observed that can replenish momentum in the wake by advection rather than turbulent diffusion. These effects and their implications for wind-farm design are discussed.