• Energy Research

Hydro-thermal economic dispatch is a widely analyzed energy optimization problem, which seeks to make the best use of available energy resources to meet demand at minimum cost. This problem has great complexity in its solution due to the uncertainty of multiple parameters. In this paper, we view hydro-thermal economic dispatch as a multistage decision-making problem, and propose several Deep Reinforcement Learning approaches to solve it due to their abilities to handle uncertainty and sequential decisions. We test our approaches considering several hydrological scenarios, especially the cases of hydrological uncertainty due to the high dependence on hydroelectric plants, and the unpredictability of energy demand. The policy performance of our algorithms is compared with a classic deterministic method. The main advantage is that our methods can learn a robust policy to deal with different inflow and load demand scenarios, and particularly, the uncertainties of the environment such as hydrological and energy demand, something that the deterministic approach cannot do.

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings.

Analyzing energy consumption is currently of great interest to define efficient energy management strategies. In particular, studying the evolution of the behavior of the consumption pattern can allow energy policies to be defined according to the time of the year. In this sense, this work proposes to study the evolution of energy behavior patterns using online clustering techniques. In particular, the centroids of the groups constructed by the techniques will represent their consumption patterns. Specifically, two unsupervised online machine learning techniques ideal for the stated objective will be analyzed, X-Means and LAMDA, since they are capable of varying and adapting the number of clusters at runtime. These techniques are applied to energy consumption data in commercial buildings, making groupings on previous groups, in our case, monthly and quarterly. We compared their performance by analyzing the evolution of the patterns over time. The results are very promising since the quality of the consumption patterns obtained is very good according to the performance metrics. Thus, the three main contributions of this article are to propose an approach to determine energy consumption patterns using online non-supervised learning approaches, a methodology to analyze and explain the evolution of energy consumption using centroids of clusters, and a comparison strategy of online learning techniques. The online clustering techniques have qualities of the order of 0.59 and 0.41 for Silhouette and Davies-Boulding, respectively, for X-Means and of the order of 0.71 and 0.24 for Silhouette and Davies-Boulding, respectively, for LAMDA in different datasets of energy. The results are motivating since very good results are obtained in terms of the quality of the clusters, particularly with LAMDA; therefore, analyzing its centroids as the patterns of user behaviors makes a lot of sense.

La plupart des études sur le contrôle, l'automatisation, l'optimisation et la supervision des systèmes CVC du bâtiment se concentrent sur le régime permanent, c'est-à-dire lorsque l'équipement fonctionne déjà à ses points de consigne. L'originalité du travail actuel consiste à proposer l'optimisation de la construction de systèmes multi-HVAC depuis le démarrage jusqu'à ce qu'ils atteignent le point de consigne, rendant la transition vers des stratégies basées sur l'état stationnaire fluide. L'approche proposée fonctionne sur le régime transitoire des systèmes multi-HVAC en optimisant des objectifs contradictoires, tels que le confort souhaité et les coûts énergétiques, sur la base du concept de « cycle autonome des tâches d'analyse de données ». Dans ce cas, le cycle autonome est composé de deux tâches d'analyse de données : une pour déterminer si le système va vers la consigne opérationnelle définie, et si ce n'est pas le cas, une autre tâche pour reconfigurer le mode opérationnel du système multi-HVAC pour le rediriger. La première tâche utilise des techniques d'apprentissage automatique pour construire des modèles de détection et de prédiction, et la seconde tâche définit un modèle de reconfiguration à l'aide d'algorithmes évolutifs multi-objectifs. Cette proposition est prouvée dans une étude de cas réelle qui caractérise un système multi-HVAC particulier et ses points de consigne opérationnels. Les performances obtenues à partir des expériences dans diverses situations sont impressionnantes car il existe un niveau élevé de conformité pour que le système multi-HVAC atteigne le point de consigne et délivre le fonctionnement à l'état d'équilibre en douceur, en évitant le dépassement et d'autres effets transitoires indésirables. La mayoría de los estudios sobre el control, la automatización, la optimización y la supervisión de los sistemas de climatización de edificios se concentran en el régimen de estado estacionario, es decir, cuando el equipo ya está trabajando en sus puntos de ajuste. La originalidad del trabajo actual consiste en proponer la optimización de la construcción de sistemas multi-HVAC desde la puesta en marcha hasta que alcanzan el punto de ajuste, haciendo que la transición a estrategias basadas en el estado estacionario sea fluida. El enfoque propuesto trabaja sobre el régimen transitorio de los sistemas multi-HVAC optimizando objetivos contradictorios, como el confort deseado y los costes energéticos, basándose en el concepto de "Ciclo Autónomo de Tareas de Análisis de Datos". En este caso, el ciclo autónomo se compone de dos tareas de análisis de datos: una para determinar si el sistema va hacia el punto de ajuste operativo definido, y si no es así, otra tarea para reconfigurar el modo operativo del sistema multi-HVAC para redirigirlo. La primera tarea utiliza técnicas de aprendizaje automático para construir modelos de detección y predicción, y la segunda tarea define un modelo de reconfiguración utilizando algoritmos evolutivos multiobjetivo. Esta propuesta está probada en un estudio de caso real que caracteriza un sistema multi-HVAC en particular y sus puntos de ajuste operativos. El rendimiento obtenido de los experimentos en diversas situaciones es impresionante, ya que existe un alto nivel de conformidad para que el sistema multi-HVAC alcance el punto de ajuste y entregue la operación al estado estable sin problemas, evitando el exceso y otros efectos de transición no deseables. Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects. تركز معظم الدراسات حول التحكم في أنظمة التدفئة والتهوية وتكييف الهواء في المبنى وأتمتتها وتحسينها والإشراف عليها على نظام الحالة المستقرة، أي عندما تعمل المعدات بالفعل عند نقاط الضبط الخاصة بها. تتكون أصالة العمل الحالي من اقتراح تحسين بناء أنظمة التدفئة والتهوية وتكييف الهواء المتعددة من بدء التشغيل حتى وصولها إلى نقطة الضبط، مما يجعل الانتقال إلى استراتيجيات ثابتة قائمة على الحالة سلسًا. يعمل النهج المقترح على النظام العابر لأنظمة التدفئة والتهوية وتكييف الهواء المتعددة لتحسين الأهداف المتناقضة، مثل تكاليف الراحة والطاقة المطلوبة، بناءً على مفهوم "الدورة المستقلة لمهام تحليل البيانات". في هذه الحالة، تتكون الدورة اللاإرادية من مهمتين لتحليل البيانات: واحدة لتحديد ما إذا كان النظام يتجه نحو نقطة الضبط التشغيلية المحددة، وإذا لم يكن الأمر كذلك، فهناك مهمة أخرى لإعادة تكوين الوضع التشغيلي لنظام التدفئة والتهوية وتكييف الهواء المتعدد لإعادة توجيهه. تستخدم المهمة الأولى تقنيات التعلم الآلي لبناء نماذج الكشف والتنبؤ، وتحدد المهمة الثانية نموذج إعادة التشكيل باستخدام خوارزميات تطورية متعددة الأهداف. تم إثبات هذا الاقتراح في دراسة حالة حقيقية تميز نظامًا معينًا متعدد التدفئة والتهوية وتكييف الهواء ونقاط ضبطه التشغيلية. الأداء الذي تم الحصول عليه من التجارب في مواقف متنوعة مثير للإعجاب نظرًا لوجود مستوى عالٍ من المطابقة لنظام التدفئة والتهوية وتكييف الهواء المتعدد للوصول إلى نقطة الضبط وتسليم العملية إلى الحالة الثابتة بسلاسة، وتجنب التجاوز والآثار الانتقالية الأخرى غير المرغوب فيها.

Situational awareness of armed forces acting in an urban environment is a key factor determining the success of military operations. In 2021 the European Defence Agency established the project on Wireless Sensor Networks for Urban Local Areas Surveillance. The main goal of the project is to assess how the situational awareness in an urban environment can be enhanced with the application of heterogeneous, autonomous and reconfigurable sensors. The paper presents a novel comprehensive approach that takes into account modelling and management of heterogeneous sensors, energy harvesting techniques, planning and management of the communication backbone, network security for data transfer and authorization for secure information exchange. The architecture of the system and the information flow are presented. The topology aspects are discussed and the sensing part is described. The paper finally highlights new essential enhancements of C2 with particular emphasis on mission planning, data fusion and threat prediction.

This paper considers the time of use (TOU) pricing scheme to propose a consumer aware pricing policy (CAPP), where each customer receives a separate electricity pricing signal. These pricing signals are obtained based on individualized load demand patterns to optimally manage the flexible load demand. The main objective of CAPP is to reduce the peaks in overall system demand in such a way that the pricing signals remain non-discriminatory. To achieve this goal, firstly the mathematical model of CAPP comprising TOU electricity price, and its variation based on consumption patterns is formulated. Secondly, the proposed CAPP model is further extended by integrating renewable energy and storage sources to overcome the possible creation of rebound peaks due to scheduling. This objective is achieved by implementing a control variable modeling the upper bound of the low tariff area. Thirdly, the cost minimization optimization problem is solved by using a Genetic Algorithm (GA) with the objective of the fair price distribution. Numerical and simulation results are obtained to validate the proposed model in terms of convergence, optimality, and cost reduction objective function. Results reveal that each customer receives a separate electricity price signal based on his demand pattern without affecting the utility/retailer revenue. Furthermore, the total cost results are also compared with and without TOU & CAPP schemes to further validate the nondiscrimination in electricity price