• Energy Research

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.

With the increasing deployment of the Internet of Things (IoT) applications, the demand for energy efficiency (EE) and quality-of-service (QoS) guarantee is on the rise. This paper considers the typically contradicting requirements for EE and QoS provisioning in an integrated manner for an IEEE 802.15.4-based wireless sensor network (WSN). Specifically, we consider variable traffic conditions of IoT applications in the presence of an energy harvesting technique based on the movement of vehicles. To this end, we introduce an adaptive energy-efficient algorithm, referred to as ABSD, that adapts the medium access control (MAC) parameters of IEEE 802.15.4 sensor nodes in response to the queue occupancy level of sensor nodes and the offered traffic load levels. By adapting the transmission parameters, the ABSD algorithm minimizes the network contention level which could in turn improve the EE as well as the network throughput. The ABSD algorithm is further enhanced by integrating with an energy harvesting technique and a new MAC parameter known as the ‘energy backoff’. The enhanced algorithm referred to as EH-ABSD offers priority to different classes of traffic by improving the battery lifetime and QoS values under variable traffic load conditions. Numerical results confirm that our proposed algorithms achieve high EE and QoS values while extending the lifetime of sensor nodes for outdoor applications.

LTE has been proposed as a possible wide area communications network for the Smart Grid. We analyze the performance of the LTE TDD mode when the uplink traffic (such as AMI meter readings and sensor data) is significantly higher than downlink traffic. We derive theoretical best case mean uplink latency figures for the relevant subset of LTE TDD uplink/downlink configurations (0, 1 and 6) and validate the findings using an OPNET simulation model. This leads to the conclusion that configuration 1 provides optimum uplink latency performance in general, but at high uplink traffic levels, only configuration 0 can reliably be used.

A robust communication infrastructure is the touchstone of a smart grid that differentiates it from the conventional electrical grid by transforming it into an intelligent and adaptive energy delivery network. To cope with the rising penetration of renewable energy sources and expected widespread adoption of electric vehicles, the future smart grid needs to implement efficient monitoring and control technologies to improve its operational efficiency. However, the legacy communication infrastructures in the existing grid are quite insufficient, if not incapable of meeting the diverse communication requirements of the smart grid. Therefore, utilities from all over the world are now facing the key challenge of finding the most appropriate technology that can satisfy their future communication needs. In order to properly assess the vast landscape of available communication technologies, architectures and protocols, it is very important to acquire detailed knowledge about the current and prospective applications of the smart grid. With a view to addressing this critical issue, this paper offers an in depth review on the application characteristics and traffic requirements of several emerging smart grid applications and highlights some of the key research challenges present in this arena.

This article investigates optimal sizing and realtime control of electrical and thermal distributed energy resources (DERs) in smart buildings. Initially, a comprehensive system architecture is presented considering both electrical and thermal DERs. Then, the DERs are optimally sized through a planning optimization problem with respect to minimization of the total initial investment cost, replacement cost, operations-and-management cost and environmental cost normalized by energy delivered from renewables and controllable non-renewables. Finally, the DERs are optimally controlled in a realtime through a one-slot-look-ahead (OSLA) optimization technique with respect to minimization of load scheduling delay cost, energy procurement cost, energy storage degradation cost, DERs operations-and-management cost, and environmental deterioration cost. The proposed OSLA based technique has the following distinct features and benefits: (i) it introduces a customer-oriented specific duration based modelling that makes it useful for practical customer energy needs and available budget limitation; (ii) it relies on the current states of the system inputs only making it applicable in general scenarios; (iii) it employs special techniques of problem modification, transformation, approximation and separation with a significantly lower computational complexity making it useful for practical implementation. Performance of the proposed approach is validated through simulations. Results show that the proposed algorithm can reduce the monthly energy consumption bill of BEMS customers by 20.53%. Also, it can execute 88.15% of customer load tasks accurately accounting for realtime changes in system inputs.

With the increasing demand of wireless body area network (WBAN) for patient monitoring systems, it is necessary to develop efficient energy management systems to either prolong node battery replacement time or to develop batteryless WBAN nodes. In this paper, we introduce an energy harvesting WBAN architecture that combines the energy harvesting system and an adaptable duty cycle adjustment algorithm ABSD (Adaptive Beacon Order, Superframe Order and Duty cycle) to prolong a WBAN node battery lifetime. The paper presents an energy harvesting model to show the effectiveness of human body motion based energy harvester. The paper further analyses the performance of a multi-patient and energy harvested WBAN using our proposed adjustable duty algorithm known as ABSD. The paper also presents several initial simulation results.

Electric vehicles (EVs) are an integral part of the future transportation systems due to enhanced fuel and energy conversion efficiency. The success of electric vehicle technology requires an efficient charging management system that ensures their timely fueling. To support such a service, vehicular ad hoc networks can be used to implement an information system for EV energy management. For this purpose, it is essential for the electric vehicles to reliably and timely exchange information with the information/control servers using infrastructure nodes (INs) deployed at different geographical locations. As the INs may be located farther away from the vehicles, robust multi-hop packet transmissions are required. In this chapter, we present the design considerations for an information system for EV energy management. We propose a vehicle-to-infrastructure and infrastructure-to-vehicle (V2I-I2V) information transmission system that efficiently delivers packets for EV energy management services by mitigating the broadcast storm and hidden node problems. Moreover, the proposed system provides a signaling mechanism to select the shortest path for the downlink I2V transmissions, a challenging task due to the mobile nature of vehicles. Simulation results show that the developed system offers a low delay and reduced number of packet transmissions for different vehicle densities and mobility conditions.

Energy harvesting (EH) is considered the key enabling technology for mass deployment of Internet-of-Things (IoT) devices. Efficient EH techniques eliminate the needs for frequent energy source replacement, thus offering a near perpetual network operating environment. Advances in the EH techniques have shifted the design paradigm of routing protocols for energy-harvesting wireless sensor network based IoT applications from “energy-aware” to “energy-harvesting-aware” . This paper aims to design an energy-harvesting-aware routing protocol for heterogeneous IoT networks in the presence of ambient energy sources. We propose a new routing algorithm energy-harvesting-aware routing algorithm (EHARA), which is further enhanced by integrating a new parameter called “energy back-off” . Combining with different energy harvesting techniques, the proposed algorithm improves the lifetime of the nodes and the network’s quality-of-service (QoS) under variable traffic load and energy availability conditions. This work also investigates the system performance metrics for different energy harvesting conditions. Performance results demonstrate that the proposed EHARA significantly improves energy efficiency while satisfying the QoS requirements of distributed IoT networks in comparison with existing routing protocols.

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.