Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Scaling energy management in buildings with artificial intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Distributed Model Predictive Control Approach for Optimal Coordination of Multiple Thermal Zones in a Large Open Space

Model Predictive Control (MPC) based approaches have recently seen a significant increase in applications to the supervisory control of building heating, ventilation and air-conditioning (HVAC) systems, thanks to their ability to incorporate weather, occupancy, and utility price information in the optimization of heating/cooling strategy while satisfying the physical constraints of HVAC equipment. Many of the proposed MPC solution approaches are centralized ones that often have difficulty in dealing with large-scale building clusters or even a single building consisting of multiple thermal zones due to the high computational cost caused by the large number of decision variables and the overhead in information gathering and distribution. This paper investigates a decomposition technique based on a variant of the Alternating Direction Method of Multipliers (ADMM) that can significantly reduce the computational and communication costs of centralized solutions, and more importantly, facilitates a plug-and-play implementation. The proposed Distributed Model Predictive Control (DMPC) framework takes advantage of parallel computation and collaborations among multiple agents, each of which is assigned to address a smaller dimensional optimization problem. The proposed method is general in that it can accommodate some fairly general types of couplings in agent dynamics as well as in the cost function; therefore it can be used for a broad class of HVAC optimal operation problems. A case study on one of the Purdue Living Labs is carried out to demonstrate the effectiveness of the proposed method. The Living Lab considered is an open office space served by one central air handling unit (AHU) with multiple diffusers whose openings can be controlled individually. The office space is partitioned into multiple thermal zones with individual thermostat controls. In view of significant thermal couplings due to direct air exchange and noticeable load gradient between zones, a multiple thermal zone coordination problem is formulated with the objective of optimally scheduling the different thermostat setpoints for energy minimization and comfort delivery while satisfying actuation constraints. Preliminary results show that the proposed method successfully converges to the optimal solution for the problem concerned in this case study. The optimal solutions demonstrate significant opportunities of inter-zonal coordination and energy savings potentials

A Distributed Coordination Strategy for Heterogeneous Building Flexible Thermal Loads in Responding to Smart Grids

Air conditioning systems are promising energy flexibility resources for smart grids. However, buildings with various thermodynamics must be coordinated to utilize limited energy flexibility effectively. This study proposes a distributed coordination strategy to coordinate building flexible thermal loads of different characteristics for optimized utilization of energy flexibility in a scalable and distributed manner. It consists of two components:1) an average consensus-based distributed sensing scheme to estimate the average thermal state of charge (SoC) of multiple zones, and 2) a weighted consensus-based distributed allocation module to allocate the demand response (DR) tasks or limited energy resources to multiple zones, proportional to their thermal storage capacities and deviations to the average thermal SoCs. Both algorithms achieve their goals respectively by fully distributed means through a sparse network with neighbor-to-neighbor communication. The sufficient condition for converging the weighted consensus algorithm is also derived for the first time. The proposed strategy is adopted for 1) weighted DR participation of residential inverter air conditioners and 2) weighted water flow redistribution of the commercial building water heating systems under urgent DR events. Simulation results show that adopting the distributed coordination strategy avoids the early depletion of demand flexibility resources and nonuniform thermal comfort sacrifices under uncoordinated control

A VOLTTRON based implementation of Supervisory Control using Generalized Gossip for Building Energy Systems

 Building energy systems comprising of many subsystems with local information and heterogenous preferences demand the need for coordination in order to perform optimally. The performance required by a typical airside HVAC system involving a large number of zones are multifaceted, involves attainment of various objectives (such as optimal supply air temperature) which requires coordination among zones. The use of traditional centralized optimization involving a large number of variables is very difficult to solve in near real time. This paper presents a novel distributed optimization framework to achieve energy efficiency in large-scale buildings. The primary goals are to achieve scalability, robustness, flexibility and low-cost commissioning. The results are presented using the proposed distributed optimization framework based on a physical testbed in the Iowa Energy Center and demonstrate the advantages of the proposed methodology compared to a typical baseline strategy. The paper outlines a real-life implementation of the proposed framework based on the VOLTTRONTM platform, recently developed by the Pacific Northwest National Laboratory (PNNL)

μGIM - Microgrid intelligent management system based on a multi-agent approach and the active participation of end-users

[ES] Los sistemas de potencia y energía están cambiando su paradigma tradicional, de sistemas centralizados a sistemas descentralizados. La aparición de redes inteligentes permite la integración de recursos energéticos descentralizados y promueve la gestión inclusiva que involucra a los usuarios finales, impulsada por la gestión del lado de la demanda, la energía transactiva y la respuesta a la demanda. Garantizar la escalabilidad y la estabilidad del servicio proporcionado por la red, en este nuevo paradigma de redes inteligentes, es más difícil porque no hay una única sala de operaciones centralizada donde se tomen todas las decisiones. Para implementar con éxito redes inteligentes, es necesario combinar esfuerzos entre la ingeniería eléctrica y la ingeniería informática. La ingeniería eléctrica debe garantizar el correcto funcionamiento físico de las redes inteligentes y de sus componentes, estableciendo las bases para un adecuado monitoreo, control, gestión, y métodos de operación. La ingeniería informática desempeña un papel importante al proporcionar los modelos y herramientas computacionales adecuados para administrar y operar la red inteligente y sus partes constituyentes, representando adecuadamente a todos los diferentes actores involucrados. Estos modelos deben considerar los objetivos individuales y comunes de los actores que proporcionan las bases para garantizar interacciones competitivas y cooperativas capaces de satisfacer a los actores individuales, así como cumplir con los requisitos comunes con respecto a la sostenibilidad técnica, ambiental y económica del Sistema. La naturaleza distribuida de las redes inteligentes permite, incentiva y beneficia enormemente la participación activa de los usuarios finales, desde actores grandes hasta actores más pequeños, como los consumidores residenciales. Uno de los principales problemas en la planificación y operación de redes eléctricas es la variación de la demanda de energía, que a menudo se duplica más que durante las horas pico en comparación con la demanda fuera de pico. Tradicionalmente, esta variación dio como resultado la construcción de plantas de generación de energía y grandes inversiones en líneas de red y subestaciones. El uso masivo de fuentes de energía renovables implica mayor volatilidad en lo relativo a la generación, lo que hace que sea más difícil equilibrar el consumo y la generación. La participación de los actores de la red inteligente, habilitada por la energía transactiva y la respuesta a la demanda, puede proporcionar flexibilidad en desde el punto de vista de la demanda, facilitando la operación del sistema y haciendo frente a la creciente participación de las energías renovables. En el ámbito de las redes inteligentes, es posible construir y operar redes más pequeñas, llamadas microrredes. Esas son redes geográficamente limitadas con gestión y operación local. Pueden verse como áreas geográficas restringidas para las cuales la red eléctrica generalmente opera físicamente conectada a la red principal, pero también puede operar en modo isla, lo que proporciona independencia de la red principal. Esta investigación de doctorado, realizada bajo el Programa de Doctorado en Ingeniería Informática de la Universidad de Salamanca, aborda el estudio y el análisis de la gestión de microrredes, considerando la participación activa de los usuarios finales y la gestión energética de lascarga eléctrica y los recursos energéticos de los usuarios finales. En este trabajo de investigación se ha analizado el uso de conceptos de ingeniería informática, particularmente del campo de la inteligencia artificial, para apoyar la gestión de las microrredes, proponiendo un sistema de gestión inteligente de microrredes (μGIM) basado en un enfoque de múltiples agentes y en la participación activa de usuarios. Esta solución se compone de tres sistemas que combinan hardware y software: el emulador de virtual a realidad (V2R), el enchufe inteligente de conciencia ambiental de Internet de las cosas (EnAPlug), y la computadora de placa única para energía basada en el agente (S4E) para permitir la gestión del lado de la demanda y la energía transactiva. Estos sistemas fueron concebidos, desarrollados y probados para permitir la validación de metodologías de gestión de microrredes, es decir, para la participación de los usuarios finales y para la optimización inteligente de los recursos. Este documento presenta todos los principales modelos y resultados obtenidos durante esta investigación de doctorado, con respecto a análisis de vanguardia, concepción de sistemas, desarrollo de sistemas, resultados de experimentación y descubrimientos principales. Los sistemas se han evaluado en escenarios reales, desde laboratorios hasta sitios piloto. En total, se han publicado veinte artículos científicos, de los cuales nueve se han hecho en revistas especializadas. Esta investigación de doctorado realizó contribuciones a dos proyectos H2020 (DOMINOES y DREAM-GO), dos proyectos ITEA (M2MGrids y SPEAR), tres proyectos portugueses (SIMOCE, NetEffiCity y AVIGAE) y un proyecto con financiación en cascada H2020 (Eco-Rural -IoT)