DUBAI, UNITED ARAB EMIRATES - A Yemen rebel drone strike this week on a critical Saudi oil pipeline shows that the otherwise-peaceful sandy reaches of the Arabian Peninsula now are at risk of similar assault, including an under-construction nuclear power plant and Dubai International Airport, among the world's busiest. U.N. investigators said the Houthis' new UAV-X drone, found in recent months during the Saudi-led coalition's war in Yemen, likely has a range of up to 1,500 km (930 miles). That puts the far reaches of both Saudi Arabia and the United Arab Emirates, the two main opponents of the Iranian-allied Houthi rebels in Yemen, within reach of drones difficult to detect and track. Their relatively simple design, coupled with readily available information online, makes targeting even easier, analysts say. "These installations are easily findable, like on Google Earth," said Tim Michetti, an expert on illicit weapons technology with experience in Yemen.
DNV GL operates globally across a range of industries to provide trust and help stakeholders understand and manage risks. The company has over 2,200 experts providing advice for renewable generation, transmission, distribution and energy management and efficiency. The work they do ranges from testing high voltage grid components to bankability assessments to allow solar farms to be financed. Their latest research showcases the insight related to how drone technology and computer vision can help with inspections while also providing a global outlook to 2050. We wanted to learn more about the energy innovations DNV GL is exploring and utilizing, so we talked with Elizabeth Traiger, Senior Researcher, Power & Renewables DK & GB at DNV GL – Energy.
Distributed electricity producers, such as small wind farms and solar installations, pose several technical and economic challenges in Smart Grid design. One approach to addressing these challenges is through Broker Agents who buy electricity from distributed producers, and also sell electricity to consumers, via a Tariff Market--a new market mechanism where Broker Agents publish concurrent bid and ask prices. We investigate the learning of pricing strategies for an autonomous Broker Agent to profitably participate in a Tariff Market. We employ Markov Decision Processes (MDPs) and reinforcement learning. An important concern with this method is that even simple representations of the problem domain result in very large numbers of states in the MDP formulation because market prices can take nearly arbitrary real values. In this paper, we present the use of derived state space features, computed using statistics on Tariff Market prices and Broker Agent customer portfolios, to obtain a scalable state representation. We also contribute a set of pricing tactics that form building blocks in the learned Broker Agent strategy. We further present a Tariff Market simulation model based on real-world data and anticipated market dynamics. We use this model to obtain experimental results that show the learned strategy performing vastly better than a random strategy and significantly better than two other non-learning strategies.
Sustainable resource management in many domains presents large continuous stochastic optimization problems, which can often be modeled as Markov decision processes (MDPs). To solve such large MDPs, we identify and leverage linearity in state and action sets that is common in resource management. In particular, we introduce linear dynamic programs (LDPs) that generalize resource management problems and partially observable MDPs (POMDPs). We show that the LDP framework makes it possible to adapt point-based methods--the state of the art in solving POMDPs--to solving LDPs. The experimental results demonstrate the efficiency of this approach in managing the water level of a river reservoir. Finally, we discuss the relationship with dual dynamic programming, a method used to optimize hydroelectric systems.