Abstract

Abstract
The proliferation of demand response programs in the smart grid provides the system operator unique opportunities to reduce the load peak and alleviate network congestions. This paper considers the economic dispatch problem with elastic demands which flexibly respond to the locational marginal prices (LMPs). However, LMP is the dual variable of optimal power flow (OPF) problem and thus is unknown before the OPF problem is solved. Without LMP, the elastic demand is unclear, and the OPF problem cannot be set up. Given this interactive nature, it is difficult to acquire the dispatch strategy as well as the LMP according to the traditional OPF method. This paper thoroughly addresses this problem. Specifically, the limitation of the traditional LMP scheme in the described situation is analyzed. An equilibrium solution may not exist because the demand function and the discontinuous LMP may not have an intersection. To overcome this difficulty, LMP at the discontinuity point is redefined, so that the dispatch problem always has an equilibrium solution. A mixed-integer linear programming model for the economic dispatch problem with LMP-dependent load is proposed, and the equilibrium solution simultaneously offers the dispatch strategy and LMPs. Case studies demonstrate the difficulties of traditional approaches and the effectiveness of the proposed method.

Abstract
Despite significant advances in web archive infrastructures, the problem of exploring the historical heritage preserved by web archives is yet to be solved. Timeline generation emerges in this context as one possible solution for automatically producing summaries of news over time. Thanks to this, users can gain a better sense of reported news events, entities, stories or topics over time, such as getting a summary of the most important news about a politician, an organisation or a locality. Web archives play an important role here by providing access to a historical set of preserved information. This particular characteristic of web archives makes them an irreplaceable infrastructure and a valuable source of knowledge that contributes to the process of timeline generation. Accordingly, the authors of this chapter developed "Tell me Stories" (), a news summarisation system, built on top of the infrastructure of Arquivo.pt-the Portuguese web-archive-to automatically generate a timeline summary of a given topic. In this chapter, we begin by providing a brief overview of the most relevant research conducted on the automatic generation of timelines for past-web events. Next, we describe the architecture and some use cases for "Tell me Stories". Our system demonstrates how web archives can be used as infrastructures to develop innovative services. We conclude this chapter by enumerating open challenges in this field and possible future directions in the general area of temporal summarisation in web archives. © Springer Nature Switzerland AG 2021. All rights reserved.

Abstract
Accurate monthly electricity consumption forecasting is indispensable for electricity retailers to mitigate trading risks in the electricity market. Clustering-based forecasting method are commonly used to generate accurate monthly electricity consumption forecasting results. This paper focuses on the problem that the existing clustering-based monthly electricity consumption forecasting methods perform clustering and forecasting independently, causing that the joint optimization of two steps cannot be achieved. The reason for this situation is that the target of current clustering algorithms, maximizing individual similarity in a group, is not consistent with the final target of improving the forecasting accuracy. To solve the above problem, the greedy clustering-based monthly electricity consumption forecasting model (GCMECF) is proposed in this paper. Its clustering step takes improving the overall predictability as the optimization target, which is closely related to the forecasting target. In this way, with matching targets, the joint optimization of clustering and forecasting can be achieved. Meanwhile, the selection of the optimal number of clusters is decided based on the forecasting performance under multiple clustering scenarios. The case study verifies the effectiveness and superiority of the proposed method via a realworld dataset.

Abstract
Geophysical techniques can be successfully applied towards the detection of buried explosive devices, the ground-penetrating radar (GPR) being an example of one such method. This technology works through emission and reception of electromagnetic radio waves being thus able to detect the presence of a subsurface object fundamentally due to reflections from contrasting electromagnetic properties between the object and the surrounding medium (e.g., soil). Many factors can influence the success of a GPR survey (e.g., target type, soil type, environmental conditions, GPR antenna frequency, data processing techniques), being essential to know and understand their likely effects before performing GPR studies, mainly in real cases. In this paper, through the analysis of case studies related to the use of GPR technology towards the detection of buried explosive devices, we intend to arrange and layout the main prior knowledge that a forensic geophysical expert must have when dealing with this type of fieldwork.

Abstract
Collaboration between companies in transportation problems seeks to reduce empty running of vehicles and to increase the use of vehicles' capacity. Motivated by a case study in the food supply chain, this paper examines a lateral collaboration between a leading retailer (LR), a third party logistics provider (3 PL) and different producers. Three collaborative strategies may be implemented simultaneously, namely pickup-delivery, collection and cross-docking. The collaborative pickup-delivery allows an entity to serve customers of another in the backhaul trips of the vehicles. The collaborative collection allows loads to be picked up at the producers in the backhauling routes of the LR and the 3 PL, instead of the traditional outsourcing. The collaborative cross-docking allows the producers to cross-dock their cargo at the depot of another entity, which is then consolidated and shipped with other loads, either in linehaul or backhaul routes. The collaborative problem is formulated with three different objective functions: minimizing total operational costs, minimizing total fuel consumption and minimizing operational and CO2 emissions costs. The synergy value of collaborative solutions is assessed in terms of costs and environmental impact. Three proportional allocation methods from the literature are used to distribute the collaborative gains among the entities, and their limitations and capabilities to attend fairness criteria are analyzed. Collaboration is able to reduce the global fuel consumption in 26% and the global operational costs in 28%, independently of the objective function used to model the problem. The collaborative pickup-delivery strategy outperforms the other two in the majority of instances under different objectives and parameter settings. The collaborative collection is favoured when the ordering loads from producers increase. The collaborative cross-docking tends to be implemented when the producers are located close to the depot of the 3 PL.