• Energy Research

Solar energy is one of the most abundant and widely available renewable energy sources. It can be harnessed using photovoltaic panels on top of buildings to reduce dependence on the electrical grid and to achieve the status of net-zero energy building. However, the rooftop coverage by solar panels can modify the heat interface between the roof surface and its surrounding environment. This can alter a building's energy demand for heating, ventilation, and air conditioning. Such an impact on a building's energy demand is highly correlated with its roof structure and climate. In this work, three-dimensional distributed thermal models of the bare and photovoltaic added rooftop ensembles are developed to simulate the heat gain/loss associated with the roof structure for monthly mean diurnal cycles. This work considers the low-rise, high-density building style and hot semi-arid climate of Faisalabad, Pakistan to quantify the impact of a rooftop photovoltaic on the roof-related thermal load of a building. Results depict a 42.58% reduced heat loss from the photovoltaic added roof structure during winter and a 1.98% increase in heat gain during summer. This reduces the electricity demand for indoor heating during winter and slightly increases it for indoor cooling during summer. The obtained results prove the significance of this work and provide guidelines to energy policymakers, the construction industry, and energy consumers. Moreover, this work provides a better understanding of the building's energy demand for heating, ventilation, and air conditioning with a rooftop photovoltaic system and its net-zero energy requirements, which are pivotal for modern construction.

A b s t r a c t Power system operation with proper planning is an utmost task for enhancing the economy of the country. The Optimal Reactive Power Dispatch (ORPD) plays an important task for secure, reliable and optimal operations of power system. ORPD is a complex, non-linear, non-convex, non-continuous and multi-model problem which involves discrete as well as continuous variables. Thus, its solution comprises of different objective functions like improving voltage profile, reducing power losses, voltage stability enhancement and transmission cost minimization. Due to the non-linear nature of problem, most of the techniques applied to ORPD are meta-heuristic. Despite the numerous articles published on these techniques, none has given sufficient emphasis on the comprehensively summarizing the existing meta-heuristic methods for solution of ORPD problem. This work presents a survey of different meta-heuristic techniques applied for solution of ORPD problems in power transmission system. Moreover, a new meta-heuristic Sine-Cosine algorithm is also proposed to solve the problem. A case study is performed where different meta-heuristic techniques are implemented to solve the same problem. Later, statistical analysis is performed to rank all implemented techniques. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for researchers working on this topic.

The fast charger for electric vehicle (EV) is a complex system that incorporates numerous interconnected subsystems. The interactions among these subsystems require a holistic understanding of the system architecture, control, power electronics, and their overall interaction with the electrical grid system. This review paper presents important aspects of a PV-grid integrated dc fast charger—with a special focus on the charging system components, architecture, operational modes, and control. These include the interaction between the PV power source, grid electricity, energy storage unit (ESU) and power electronics for the chargers. A considerable amount of discussion is also dedicated to battery management systems (BMS) and their mutual interactions in the control processes. For the power electronics, the paper evaluates soft switching non-isolated dc-to-dc power converter topologies that can be possibly used as future EV chargers. In addition to these, a brief discussion on the impact of the PV-grid charging on the ac grid and distribution system and their remedial measures are presented. Furthermore, the challenges in regard to the vehicle to grid (V2G) concept are also described. It is envisaged that the information provided in this paper would be useful as a one-stop document for engineers, researchers and others who require information related to the dc fast charging of EV that incorporates a renewable energy source.

AbstractThis work presents an energy management scheme (EMS) based on a rule-based grasshopper optimization algorithm (RB-GOA) for a solar-powered battery-ultracapacitor hybrid system. The main objective is to efficiently meet pulsed load (PL) demands and extract maximum energy from the photovoltaic (PV) array. The proposed approach establishes a simple IF-THEN set of rules to define the search space, including PV, battery bank (BB), and ultracapacitor (UC) constraints. GOA then dynamically allocates power shares among PV, BB, and UC to meet PL demand based on these rules and search space. A comprehensive study is conducted to evaluate and compare the performance of the proposed technique with other well-known swarm intelligence techniques (SITs) such as the cuckoo search algorithm (CSA), gray wolf optimization (GWO), and salp swarm algorithm (SSA). Evaluation is carried out for various cases, including PV alone without any energy storage device, variable PV with a constant load, variable PV with PL cases, and PV with maximum power point tracking (MPPT). Comparative analysis shows that the proposed technique outperforms the other SITs in terms of reducing power surges caused by PV power or load transition, oscillation mitigation, and MPP tracking. Specifically, for the variable PV with constant load case, it reduces the power surge by 26%, 22%, and 8% compared to CSA, GWO, and SSA, respectively. It also mitigates oscillations twice as fast as CSA and GWO and more than three times as fast as SSA. Moreover, it reduces the power surge by 9 times compared to CSA and GWO and by 6 times compared to SSA in variable PV with the PL case. Furthermore, its MPP tracking speed is approximately 29% to 61% faster than its counterparts, regardless of weather conditions. The results demonstrate that the proposed EMS is superior to other SITs in keeping a stable output across PL demand, reducing power surges, and minimizing oscillations while maximizing the usage of PV energy.

Over the past few years, the use of DC-DC buck-boost converters for Photovoltaic (PV) in renewable energy applications has increased for better results. One of the main issues with this type of converter is that output voltage is achieved with the undesired ripples. Many models are available in the literature to address this issue, but very limited work is available that achieves the desired goal using deep learning-based models. Whenever it comes to the PV, then it is further limited. Here, a deep learning-based model is proposed to reduce the steady-state time and achieve the desired buck- or boost mode for PV modules. The deep learning-based model is trained using data collected from the conventional PID controller. The output voltage of the experimental setup is 12V while the input voltage from the PV modules is 10V (when the sunlight decreases) to 24V (for 3.6 kVA) to 48V (for more than 5 kVA). It is among the few models using a single big battery (12V) for off-grid and on-grid for a single building. Experimental results are validated using objective measures. The proposed model outperforms the conventional PID controller-based buck-boost converters. The results clearly show improved performance in terms of steady-state and less overshoot.

Reduction in power loss while maintaining the acceptable voltage profile has become a challenge for distribution system operators due to expanded living standards. Properly sized shunt capacitors (SCs) allocated at suitable locations of the distribution system can enhance its performance by tackling the power quality issues and foster multiple technical and economic benefits. Most of the existing research work in this domain is accomplished at fixed loading conditions without incorporating purchasing, installation, operation and degradation costs of the SCs. In this work, an intelligent metaheuristic polar bear optimization algorithm (PBOA) is applied to solve the optimal capacitor placement and sizing problem in the radial distribution system (RDS) at various loading conditions. By formulating multiple objective functions for the annual operating cost (AOC), this work incorporates the cost of real power loss ( $$ P_{\text{loss}} $$ ) and various costs associated with SCs. The proposed technique is tested on various IEEE standard bus systems under different loading conditions. The proposed PBOA reduces AOC and $$ P_{\text{loss}} $$ of RDS while maintaining tolerable voltages at systems’ buses. The reductions of 51%, 35%, 26% and 53% in $$ P_{\text{loss}} $$ are observed for IEEE standard 15-, 33-, 34- and 85-bus systems, respectively, at 100% loading from their uncompensated cases. Similarly, reductions in AOCs for the same systems at 100% loading are 32%, 25%, 16% and 45%, respectively. These results prove superiority of proposed method over recent state-of-the-art algorithmic approaches. The results of this work can be useful for distribution system operators in achieving reliable and efficient operation of the power grid.

This work proposes a rule-based energy management scheme (REMS) for electric vehicle (EV) charging from photovoltaic-grid (PV-grid) system. The main feature of this scheme is that it provides uninterrupted daytime charging at a constant price. In order to simulate the system, the models of PV output power, EV power demand, state of charge (SOC) estimation of energy storage unit (ESU) and grid electricity prices are developed. The uninterrupted and constant price charging is achieved by managing the energy flow between PV, ESU and grid according to the rules defined by REMS. Furthermore, the valley-filling operation is implemented during the grid off-peak hours. The resiliency of REMS is validated under various weather conditions, different ESU prices and at grid parities. For comparison, its performance is benchmarked against the standard grid-based EV charging. The results demonstrate a decline in charging price by 16.1% besides reducing the burden on the grid by 93.7% with the implementation of REMS. In addition, the vehicle-to-grid (V2G) technology is incorporated in the charging system to improve the payback schedule of the existing PV-grid system.

Abstract The integration of solar photovoltaic (PV) into the electric vehicle (EV) charging system has been on the rise due to several factors, namely continuous reduction in the price of PV modules, rapid growth in EV and concerns over the effects of greenhouse gases. Despite the numerous review articles published on EV charging using the utility (grid) electrical supply, so far, none has given sufficient emphasis on the PV charger. With the growing interest in this subject, this review paper summarizes and update all the related aspects on PV–EV charging, which include the power converter topologies, charging mechanisms and control for both PV–grid and PV-standalone/hybrid systems. In addition, the future outlook and the challenges that face this technology are highlighted. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for researchers working in this topic.