• Energy Research

This article surveys emerging technologies related to pervasive edge computing (PEC) for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks. PEC encompasses all devices that are capable of performing computational tasks locally, including those at the edge of the core network (edge servers co-located with 5G base stations) and in the radio access network (sensors, actuators, etc.). The main advantages of this paradigm are core network offloading (and benefits therefrom) and low latency for delay-sensitive applications (e.g., automatic control). We have reviewed the state-of-the-art in the PEC paradigm and its applications to the IIoT domain, which have been enabled by the recent developments in 5G technology. We have classified and described three important research areas related to PEC-distributed artificial intelligence methods, energy efficiency, and cyber security. We have also identified the main open challenges that must be solved to have a scalable PEC-based IIoT network that operates efficiently under different conditions. By explaining the applications, challenges, and opportunities, our paper reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks, considering the modern trend toward private industrial 5G networks with local operations and flexible management.

Este artículo presenta una estrategia PEM basada en ER para hogares inteligentes integrados fotovoltaicos para optimizar conjuntamente sus retrasos en la programación de la carga, el coste de las transacciones de energía y el coste de degradación de la batería. El enfoque propuesto incorpora un caso de MA, donde, la ER actúa como un agente de selección principal realizado por todos los demás elementos del sistema. Esto conduce a un problema de optimización combinatoria, que puede resolverse de manera efectiva mediante métodos de optimización heurística (HOM), a saber, algoritmo genético (GA), optimización de enjambre de partículas binarias (BPSO), algoritmo de evolución diferencial (DE) y algoritmo de búsqueda de armonía (HSA). Específicamente, investigamos el impacto de los hiperparámetros de los HOM en el sistema PEM basado en ER diseñado. Se realizan simulaciones para múltiples hogares inteligentes en diferentes condiciones climáticas para evaluar la efectividad de los HOM en términos de métricas de rendimiento seleccionadas. Los resultados muestran que el PEM basado en ER reduce el costo promedio agregado del sistema, garantiza beneficios económicos al vender el excedente de energía, al tiempo que satisface la demanda de paquetes de energía de los clientes, satisface su calidad de servicio y sus limitaciones operativas. Cet article présente une stratégie PEM basée sur les ER pour les maisons intelligentes intégrées PV afin d'optimiser conjointement leurs retards de planification de charge, leur coût de transaction énergétique et leur coût de dégradation de la batterie. L'approche proposée intègre un cas d'AMM, où le RE agit comme un agent de sélection principal réalisé par tous les autres éléments du système. Cela conduit à un problème d'optimisation combinatoire, qui peut être efficacement résolu par des méthodes d'optimisation heuristique (HOM), à savoir, l'algorithme génétique (GA), l'optimisation d'essaim de particules binaires (BPSO), l'algorithme d'évolution différentielle (DE) et l'algorithme de recherche d'harmonie (HSA). Plus précisément, nous étudions l'impact des hyperparamètres des CDM sur le système PEM conçu à base de RE. Des simulations sont effectuées pour plusieurs maisons intelligentes dans des conditions météorologiques variables afin d'évaluer l'efficacité des CDM en termes de paramètres de performance sélectionnés. Les résultats montrent que le PEM basé sur ER réduit le coût moyen agrégé du système, garantit des avantages économiques en vendant de l'énergie excédentaire, tout en répondant à la demande de paquets d'énergie des clients, en satisfaisant leur qualité de service et leurs contraintes opérationnelles. This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates a MA case, where, the ER acts as a main selecting agent realized by all other system elements. This leads to a combinatorial optimization problem, which can be effectively solved by heuristic optimization methods (HOMs), namely, genetic algorithm (GA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and harmony search algorithm (HSA). Specifically, we investigate the impact of the hyperparameters of the HOMs on the designed ER-based PEM system. Simulations are carried out for multiple smart homes under varying weather conditions to evaluate the effectiveness of HOMs in terms of selected performance metrics. Results show that the ER-based PEM reduces the average aggregated system cost, ensures economic benefits by selling surplus energy, while meeting customers energy packet demand, satisfying their quality-of-service, and operational constraints. تقدم هذه المقالة استراتيجية PEM القائمة على ER للمنازل الذكية المتكاملة الكهروضوئية لتحسين تأخيرات جدولة الأحمال وتكلفة معاملات الطاقة وتكلفة تدهور البطارية بشكل مشترك. يتضمن النهج المقترح حالة تقييم الألفية، حيث تعمل غرفة الطوارئ كعامل اختيار رئيسي تحققه جميع عناصر النظام الأخرى. وهذا يؤدي إلى مشكلة التحسين التوافقي، والتي يمكن حلها بفعالية من خلال طرق التحسين الاستكشافي (HOMs)، وهي الخوارزمية الجينية (GA)، وتحسين سرب الجسيمات الثنائي (BPSO)، وخوارزمية التطور التفاضلي (DE)، وخوارزمية البحث التوافقي (HSA). على وجه التحديد، نقوم بالتحقيق في تأثير المعلمات الفائقة لـ HOMs على نظام PEM المصمم القائم على ER. يتم إجراء عمليات المحاكاة للعديد من المنازل الذكية في ظل ظروف جوية مختلفة لتقييم فعالية المنازل من حيث مقاييس الأداء المختارة. تظهر النتائج أن PEM القائم على ER يقلل من متوسط تكلفة النظام الإجمالية، ويضمن الفوائد الاقتصادية من خلال بيع فائض الطاقة، مع تلبية طلب العملاء على حزم الطاقة، وتلبية جودة الخدمة، والقيود التشغيلية.

Smart meters with automatic meter reading functionalities are becoming popular across the world. As a result, load measurements at various sampling frequencies are now available. Several methods have been proposed to infer device usage characteristics from household load measurements. However, many techniques are based on highly intensive computations that incur heavy computational costs; moreover, they often rely on private household information. In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households. Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms. Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearance of utilization patterns. Self organizing maps are used to create a system status classifier. We applied our techniques to two public datasets from two different countries, the United Kingdom (UK-DALE) and the US (REDD), with different usage patterns. The proposed clustering techniques enable effective demand-side management, while the system status classifier can detect appliance malfunctions only through system status analyses.

This paper aims to introduce a predictive weather-based control policy for the microgrid energy management to improve the resilience of the microgrid. This policy relies on the application of machine learning models for the prediction of microgrid load demand and solar production and supply interruption in the upstream distribution network. The predictions serve as an input to multiobjective chance constraint optimization that balances the microgrid resilience and economic objectives based on the probability of a supply interruption. The interruption predictions are made with a decision-tree-based model that can predict an upcoming interruption in the distribution network with 78% of the maximum accuracy. The case study microgrid consisting of several customers, solar photovoltaic generation, and battery storage is applied to cluster areas located in Finland. Overall, the developed control policy shows an improvement in the daily resilience of the microgrid in regard to an interruption in the main grid when compared with economic dispatch only.

The climate change crisis, exacerbated by the global dependency of fossil fuels, has brought significant challenges. In the medium to long term, extensive renewable-energy-based electrification is considered to be one of the most promising development paths to address these challenges. However, this is tangible only if the energy infrastructure can accommodate renewable energy sources and distributed energy resources, such as batteries and heat pumps, without adversely affecting power grid operations. To realize renewable-energy-based electrification goals, a new concept—the Energy Internet (EI)—has been proposed, inspired by the most recent advances in information and telecommunication network technologies. Recently, many measures have also been taken to practically implement the EI. Although these EI models share many ideas, a definitive universal definition of the EI is yet to be agreed. Additionally, some studies have proposed protocols and architectures, but a generalized technological overview is still missing. An understanding of the technologies that underpin and encompass the current and future EI is very important to push toward a standardized version of the EI that will eventually make it easier to implement it across the world. In this paper, we first examine and analyze the typical popular definitions of the EI in scientific literature. Based on definitions, assumptions, scope, and application areas, the scientific literature is then classified into four different groups representing the way in which the papers have approached the EI. Then, we synthesize these definitions and concepts, and keeping in mind the future smart grid, we propose a new universal definition of the EI. We also identify the underlying key technologies for managing, coordinating, and controlling the multiple (distributed or not) subsystems with their own particular challenges. The survey concludes by highlighting the main challenges facing a future EI-based energy system and indicating core requirements in terms of system complexity, security, standardization, energy trading and business models and social acceptance.

Peer-to-peer (P2P) electricity sharing or trading can empower consumers and prosumers, incentivize the balancing of generation and demand locally, increase system resilience and reliability, and help in achieving societal goals, such as increasing renewable energy penetration. Nevertheless, the development of P2P trading in actual environments has been slow due to the unclear position of P2P markets in the power system. Recent developments in the European legislation are promising for the establishment of P2P markets and energy communities. Hence, the interplay between local trading and existing market structures needs to be addressed carefully. Furthermore, P2P trading with distributed resources presumes that electricity end users will become active players in the power system. This paper proposes a bidding and pricing mechanism for local markets, considering the external markets; a new approach to balance settlement and balance responsibility when local trading occurs; and an interface to promote end-user interest in, and interactions with, local energy trading. The proposed local market concept and interface solution promote the coupling between local and existing retail, wholesale and ancillary service markets, and can be seen as a step towards the establishment of local energy markets in real-life settings.