• Energy Research

In future distribution grids, prosumers (i.e., energy consumers with storage and/or production capabilities) will trade energy with each other and with the main grid. To ensure an efficient and safe operation of energy trading, in this paper, we formulate a peer-to-peer energy market of prosumers as a generalized aggregative game, in which a network operator is only responsible for the operational constraints of the system. We design a distributed market-clearing mechanism with convergence guarantee to an economically-efficient and operationally-safe configuration (i.e., a variational generalized Nash equilibrium). Numerical studies on the IEEE 37-bus testcase show the scalability of the proposed approach and suggest that active participation in the market is beneficial for both prosumers and the network operator. 11 pages, 8 figures. Published in IEEE Transactions on Smart Grid, 2022

In this paper, the user–base station (BS) association problem is addressed to reduce grid consumption in heterogeneous cellular networks (HetNets) powered by hybrid energy sources (grid and renewable energy). The paper proposes a novel distributed control scheme inspired by population games and designed considering both atomicity and non-anonymity – i.e., describing the individual decisions of each agent. The controller performance is considered from an energy–efficiency perspective, which requires the guarantee of appropriate qualityof-service (QoS) levels according to renewable energy availability. The efficiency of the proposed scheme is compared with other heuristic and optimal alternatives in two simulation scenarios. Simulation results show that the proposed approach inspired by population games reduces grid consumption by 12% when compared to the traditional best-signal-level association policy.

In this paper, a distributed control scheme based on population games is proposed. The controller is in charge of dealing with the energy consumption problem in a Heterogeneous Cellular Network (HetNet) powered by hybrid energy sources (grid and renewable energy) while guaranteeing appropriate quality of service (QoS) level at the same time. Unlike the conventional approach in population games, it considers both atomicity and non-anonymity. Simulation results show that the proposed population-games approach reduces grid consumption by up to about 12% compared to the traditional best-signal level association policy. U.S. Air Force Office of Scientific Research FA9550-17-1-0259 Ministerio de Cultura y Deporte DPI2016-76493-C3-3-R Ministerio de Economía y Empresa DPI2017-86918-R

Nowadays, wind power plants (WPPs) should be able to dynamically change their power output to meet the power demanded by the transmission system operators. When the wind power generation exceeds the power demand, the WPP works in de-loading operation keeping some power reserve to be delivered into the grid to balance the frequency drop. This paper proposes to cast a model predictive control strategy as a multi-objective optimization problem which regulates the power set-points among the turbines in order to track the power demand profile, to maximize the power reserve, as well as to minimize the power losses in the inter-arrays connecting the wind turbines within the wind farm collection grid. The performance of the proposed control approach was evaluated for a wind farm of 12 turbines using a wind farm simulator to model the dynamic behavior of the wake propagation through the wind farm.

Trabajo presentado en el 4th Colombian Conference on Automatic Control (CCAC), celebrado en Medellín (Colombia), del 15 al 18 de octubre de 2019 Given the need of implementing methodologies in industry for the reduction of the energy consumption costs, it is required to create modelling methodologies that, together with the use of new technologies, will allow identifying energy consumption models based on input-output data. These models will later be used to design a suitable model-based control strategy. In this paper, a subspace identification algorithm based on the RQ decomposition approach has been reported, which is both implemented and validated on a test-bench that emulates the energy consumption of an industrial machine within a manufacturing process. Subsequently, the resultant model fitting when using the proposed modelling methodology has been compared with different identification routines included into the MATLAB System Identification Toolbox¿, showing, in general, better results for the proposed methodology in this paper, with up to almost 80% of fitting in some cases. This work has been funded by the project IKERCON (ref. C10683).

The importance of implementing energy efficiency methodologies in industrial environments has increased considerably in the last decade given the high energy costs and environmental impact (e.g., greenhouse gas emissions). This paper proposes a methodology to improve the energy efficiency of an industrial machine, without sacrificing either production or quality, using an adaptive predictive controller based on dynamic energy models that manages peripheral devices to activate/deactivate them at the proper times. The proposed adaptive mechanism aggregates robustness to the control system in industrial environments, which experiment constantly changes related to equipment degradation and that affect their energy consumption profile over time. Thus, this novel adaptive mechanism automatically updates the energy model to minimize the error between prediction and real energy consumption, including new energy behavior resulting from machine degradation. This methodology has been validated via a testbed and its performance was compared with rule-based control, which is the most widely used control strategy in industry. The energy efficiency of both approaches was evaluated using performance indicators, which show the effectiveness of the proposed control approach, highlighting remarkable improvements in reducing both energy consumption (about 2%) and sudden power peaks (more than 11%). This work has been supported by the Doctorats Industrials program from the Catalan Gobernment (2019 DI 4). Authors would like to thank the companies of the Inzu Group, Etxe-tar, and Ikergune, for the support related to high-productivity systems.