• Energy Research

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

Due to limited energy resources, energy balancing becomes an appealing requirement/ challenge in Underwater Wireless Sensor Networks (UWSNs). In this paper, we present a Balanced Load Distribution (BLOAD) scheme to avoid energy holes created due to unbalanced energy consumption in UWSNs. Our proposed scheme prolongs the stability period and lifetime of the UWSNs. In BLOAD scheme, data (generated plus received) of underwater sensor nodes is divided into fractions. The transmission range of each sensor node is logically adjusted for evenly distributing the data fractions among the next hop neighbor nodes. Another distinct feature of BLOAD scheme is that each sensor node in the network sends a fraction of data directly to the sink by adjusting its transmission range and continuously reports data to the sink till its death even if an energy hole is created in its next hop region. We implement the BLOAD scheme, by varying the fractions of data using adjustable transmission ranges in homogeneous and heterogeneous simulation environments. Simulation results show that the BLOAD scheme outperforms the selected existing schemes in terms of stability period and network lifetime.

In this paper, we investigate a joint real-time load scheduling and energy storage management at a grid-connected solar powered electric vehicle. Without any a priori knowledge, we consider a finite time approach with arbitrary dynamics of system inputs. Our aim is to minimize an average aggregated system cost through joint optimization of electric vehicle's energy procurement price, load scheduling delays, photovoltaic sufficiency in terms of locally generated renewable energy mix, and battery degradation. Through subsequent modification and reformulation of the joint optimization problem, we utilize the concept of one-slot look-ahead queue stability to solve the problem by employing the Lyapunov optimization technique. We show that the joint optimization problem is separable into sub-problems, which are sequentially solved with asymptotic optimality and a bounded performance guarantee. Simulations are carried in different scenarios and under varying weather conditions. Results show that our proposed algorithm can achieve a daily electric vehicle's photovoltaic sufficiency up to 50.50%, a monthly bill reduction up to 72.61%, and a yearly reduced CO $_2$ emission level up to 6.06 kg, while meeting electric vehicle user's energy and delay requirements.

This paper presents two new energy balanced routing protocols for Underwater Acoustic Sensor Networks (UASNs); Efficient and Balanced Energy consumption Technique (EBET) and Enhanced EBET (EEBET). The first proposed protocol avoids direct transmission over long distance to save sufficient amount of energy consumed in the routing process. The second protocol overcomes the deficiencies in both Balanced Transmission Mechanism (BTM) and EBET techniques. EBET selects relay node on the basis of optimal distance threshold which leads to network lifetime prolongation. The initial energy of each sensor node is divided into energy levels for balanced energy consumption. Selection of high energy level node within transmission range avoids long distance direct data transmission. The EEBET incorporates depth threshold to minimize the number of hops between source node and sink while eradicating backward data transmissions. The EBET technique balances energy consumption within successive ring sectors, while, EEBET balances energy consumption of the entire network. In EEBET, optimum number of energy levels are also calculated to further enhance the network lifetime. Effectiveness of the proposed schemes is validated through simulations where these are compared with two existing routing protocols in terms of network lifetime, transmission loss, and throughput. The simulations are conducted under different network radii and varied number of nodes.

In this paper, we propose a user centric real-time (RT) technique for an optimal control of a distributed photovoltaic stand-alone micro-grid. Our objective is to minimize an average aggregated system cost over a finite time period considering a joint optimization problem of battery energy storage (BES) management, load scheduling, and energy procurement process from a peaker generator (PG). Due to online decision making, the dynamic control actions of the state of energy of the storage battery are correlated over time. Thus, we impose a constraint on the BES state of energy over the considered time period and eliminate the finite BES capacity constraint. In the load scheduling process, we account for the temporal variability and spatial uncertainty of each household load explicitly. We introduce the concept of block duration to optimize the energy procurement cost from a PG. We modify and then transform the problem to utilize the Lyapunov optimization technique. We show that the proposed solution to the joint optimization problem is asymptotically optimal with a bounded performance guarantee and is easy to implement. Simulations in different weather conditions show the effectiveness of our proposed user centric RT solution in terms of the selected performance metrics.

Abstract Energy storage control, load scheduling, and indoor user comfort management are perceived as key management solutions for electric industry in the building sector. Nevertheless, requirement of a-priori knowledge on system inputs (i.e., renewable energy generation process, load arrival process, and dynamic price signals) raises concerns about the ability of existing building energy management solutions to accurately adapt to real-time needs in energy generation, demand, storage, and indoor comfort feel. Conversely, with the consideration of unknown dynamics of system inputs, a one-slot-look-ahead virtual queue stability based Lyapunov optimization technique is employed in this article for a real-time energy and comfort optimization in grid-connected solar integrated smart buildings. The goal is to minimize an average aggregated system cost through a real-time joint optimization of electrical and thermal load scheduling delays, energy procurement cost from controllable generators and external grid, electrical and thermal energy storage degradation, and indoor user comfort feel. It is also shown that the joint optimization problem is separable into subproblems which are sequentially solved to obtain all solutions in closed-forms. The solutions are proved as asymptotically optimal, and can be easily implemented in real-time building energy and comfort management scenarios especially when the statistics of system inputs are unknown and arbitrary. The proposed algorithm is validated through simulations where it is tested in different weather conditions. Results show that the proposed algorithm can achieve an average monthly energy procurement-and-operations cost reduction up to 16.37%, while meeting building’s energy and comfort requirements.

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.

In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.