Sara Lumbreras

Stochastic Programming Applied to EV Charging Points for Energy and Reserve Service Markets

A more realistic management of electric vehicle (EV) charging points requires to cope with stochastic behavior on vehicle staying patterns. This paper presents a stochastic programming model to achieve optimal management taking into account price variation in day-ahead and intraday electricity markets, together with regulating reserve margins. In this model, first-stage decisions determine day-ahead energy purchases and sales and the upward and downward reserve margins committed. Second-stage decisions correspond to intraday markets and deal with reserve requirements and several possible scenarios for vehicle staying pattern. The design of the objective function prioritizes supplying energy to EV batteries while minimizing the net expected energy cost at the EV charging point. A case study describing a parking for 50 EVs is analyzed. The case includes household, commercial and mixed EV staying patterns with several intraday arrival and departure scenarios. Pure and hybrid EVs are included, taking into account their respective energy characteristics. Sensitivity analysis is used to show the potential energy cost savings and the impact of different non-supply penalizations. The case study considers several vehicle staying patterns, energy price profiles and discharge allowances. The model achieves energy cost reductions between 1% and 15% depending on the specific case. A model validation by simulation has been done.

A Progressive Contingency Incorporation Approach for Stochastic Optimization Problems

Reliability is a key objective in many optimal design problems. Very often this criterion is incorporated into stochastic optimization problems by introducing contingency evaluation scenarios. This results in a special problem structure where the stochastic scenarios used to describe reliability are linked to the failure of specific individual elements. This paper presents a progressive contingency incorporation (PCI) approach that takes advantage of this structure to increase efficiency. The algorithm is applied to the design of an offshore wind farm, where the electrical layout is decided, as representative of the possible advantages of PCI within power systems. The problem must determine the placement and type of cables in a collector system. Results show that time savings achieved by the PCI approach can be remarkable, reaching two orders of magnitude for the case study.

Optimal Regime Switching under Risk Aversion and Uncertainty

Technology adoption is key for corporate strategy, often determining the success or failure of a company as a whole. However, risk aversion often raises the reluctance to make a timely technology switch, particularly when this entails the abandonment of an existing market regime and entry in a new one. Consequently, which strategy is most suitable and the optimal timing of regime switch depends not only on market factors, such as the definition of the market regimes, as well as economic and technological uncertainty, but also on attitudes towards risk. Therefore, we develop a utility-based, regime-switching framework for evaluating different technology-adoption strategies under price and technological uncertainty. We assume that a decisionmaker may invest in each technology that becomes available (compulsive) or delay investment until a new technology arrives and then invest in either the older (laggard) or the newer technology (leapfrog). Our results indicate that, if market regimes are asymmetric, then greater risk aversion and price uncertainty in a new regime may accelerate regime switching. In addition, the feasibility of a laggard strategy decreases (increases) as price uncertainty in an existing (new) regime increases. Finally, although risk aversion typically favours a compulsive and a laggard strategy, a leapfrog strategy may be feasible under risk aversion provided that the output price and the rate of innovation are sufficiently high.

How relevant is the lack of reciprocity in pairwise comparisons? An experiment with AHP

Most pairwise comparison (PC) methods typically require the explicit elicitation of only half of the comparisons, and infer the rest by assuming reciprocity in the decision maker’s comparisons. However, this may imply losing useful information contained in the additional comparisons that could be made, and which might be different from the first ones. This study assesses how relevant the lack of reciprocity may be in an experimental setting, and to what extent the information included in the additional comparisons may influence results. Our experiment shows that decision makers display substantial levels of irreciprocity and inconsistency, and that they generally prefer preference vectors calculated without assuming reciprocity in their comparisons. According to our results, our main conclusion is that, in general, decision makers should be requested all the comparisons in a PC matrix.

Network partition based on critical branches for large-scale transmission expansion planning

The size and complexity of large power systems, such as the European one, often make them unmanageable for the purposes of Transmission Expansion Planning (TEP). Therefore, network reduction methods are necessary to condense their key features into a workable model, which should approximate the behavior of the nodal system as accurately as possible. We define critical branches as the transmission lines that are particularly relevant for TEP purposes because of, for instance, frequent congestions or a special nature such as the cases of HVDC (High-Voltage Direct Current) transmission lines or PSTs (Phase-Shifting Transformers). It would be desirable to preserve these critical branches while simplifying the remaining grid. We propose a heuristic algorithm that creates an initial partition that is later refined by clustering nodes based on a composite distance measure. The proposed technique has been applied to a real case study based on the French and Spanish systems.

Real options valuation applied to transmission expansion planning

Transmission expansion planning (TEP) is a complex problem where building a new line involves a long permitting process of around 10 years. Therefore, transmission expansion must anticipate the evolution of uncertainties, particularly those derived from changes in the capacity and location of new generating facilities. As it is not possible to request permits for all possible lines, priorities must be established. We develop a formulation to use real options valuation to evaluate the potential benefit of candidate lines and thereby identify priority projects. We present a feasible representation of optionality in TEP projects and propose a tractable evaluation of option value. The proposed technique identifies the candidate transmission lines with the highest potential, as well as their main value drivers. This is implemented in a realistic large-scale case study based on the Spanish system.

Snapshot selection based on statistical clustering for Transmission Expansion Planning

Transmission Expansion Planning (TEP) is usually performed on a few operating situations or snapshots. In order to get a representative set of snapshots, it is necessary to select them carefully. We propose a clustering method based on the K-means algorithm that uses features drawn from information about system operation. Features based on price differences and non-controllable injections are considered and a small test case is proposed. We suggest replacing local features by statistical indicators over the system to reduce the clustering complexity. The obtained results show that statistical price differences can be used as a good clustering feature for snapshot selection and the error introduced in the investment solution compared to the solution without clustering is very small.

Getting Ready for the Next Step: Merging Information Ethics and Roboethics—A Project in the Context of Marketing Ethics

This article presents some pressing issues on roboethics, which lie at the frontier between roboethics and information ethics. It relates them to the well-established field of marketing ethics, stressing two main points. First, that human attention and willpower is limited and susceptible to be exploited. Second, that the possibility of using consumer profiles considerably increases the possibility of manipulation. It presents the interactions with robots as a particularly intense setting, in which the humanlike presence and the possibility of tailoring communications to the profile of the human target can be especially problematic. The paper concludes with some guidelines that could be useful in limiting the potentially harmful effects of human–robot interactions in the context of information ethics. These guidelines focus on the need for transparency and the establishment of limits, especially for products and services and vulnerable collectives, as well as supporting a healthy attention and willpower.

Strong Artificial Intelligence and imago hominis: The Risks of a Reductionist Definition of Human Nature

The last decades have witnessed an unprecedented development in Information and Communication Technologies (ICTs) and Artificial Intelligence (AI), which have modified the way that interpersonal relationships are established and could also modify the way that human specificity is understood, even challenging the concept of human specialness. These technological advances have led some authors to anticipate that soon there will be machines built in our own image and to which we will relate as equals. However, we understand that subjectivity is a key element of all expressions of intelligence and emotion and is pivotal to consciousness itself, despite being overlooked in many reductionist arguments. We propose to bring the concept of authenticity to the discussion to distinguish different values in the possible expressions of intelligence, consciousness or emotion. Authenticity in this context would be deeply linked to subjective experience: for instance, an emotion is only authentic if it is felt in addition to being expressed. These subjective experiences, by definition, cannot be objectively measured. In order to overcome this limitation, we propose to use emergence as an objective surrogate for this authenticity: when behaviour is authentic, it emerges from lower-level domains rather than being imposed by programming or conditioning. In this paper we provide a framework for these concepts, with a double aim: stressing the importance of subjectivity and the difference between simulated processes and real ones, and proposing a filter based on emergence as a first guide in assessing the authenticity of these processes in AI.

Enhanced pan-European transmission planning methodology

This paper presents a new methodology for long-term transmission planning over large systems. The developed approach aims at finding the optimal design of a large grid including its modular development plan over a long time horizon. Advanced optimization and simulation methods have been investigated to tackle this very large and complex problem, which includes highly combinatorial aspects and stochastic behaviours of system components, while ensuring some control over the system. Some tools have been implemented and tested on a case study based on the French and Spanish systems. The preliminary results of this study give an estimation of the resources that would be needed for a real study over a system of the size of the whole pan-European network for the period 2020 to 2050.