• Energy Research

A directional overcurrent relay is commonly used to protect the power distribution networks of a distributed system. The selection of the appropriate settings for the relays is an important component of the protection strategies used to isolate the faulty parts of the system. The rapid growth of distributed generation (DG) systems present new challenges to these protection schemes. The effect of solar photovoltaic power plants on relay coordination is studied initially in this research work. A protection strategy was formulated to guarantee that the increased penetration of solar photovoltaic (PV) plants does not affect the relay coordination time. This paper addresses these issues associated with a high penetration of DG through the use of a hybrid protection scheme. The protection strategy is divided into two parts. The first part is based on an optimal fault current limiter value estimated with respect to constraints and the optimal time multiplier setting, and then the coordination time interval is estimated with respect to constraint in Part II. The results of these analyses show that a hybrid protection scheme can effectively handle the complexity of distributed generation (DG) and dynamic relay coordination problems. In this research, three optimization algorithms have been used for calculating the estimated value of impedance fault current limiter (Zfcl) and time multiplier setting (TMS). The response time of hybrid protection schemes is very important. If the computational time of their proposed algorithms is less than their actual computational time, then their response time to address the issue is also less. The performance in all algorithms was identified to arrive at a conclusion that the grey wolf optimized algorithm (GWO) algorithm can substantially reduce the computational time needed to implement hybrid protection algorithms. The GWO algorithm takes a computational time of 0.946 s, achieving its feasible solution in less than 1 s.

The rapid increase in the penetration of photovoltaic (PV) power plants results in an increased risk of grid failure, primarily due to the intermittent nature of the plant. To overcome this problem, the flexible power point tracking (FPPT) algorithm has been proposed in the literature over the maximum power point tracking (MPPT) algorithm. These algorithms regulate the PV power to a certain value instead of continuously monitoring the maximum power point (MPP). The proposed work carries out a detailed comparative study of various constant power generation (CPG) control strategies. The control strategies are categorized in terms of current-, voltage-, and power-based tracking capabilities. The comparative analysis of various reported CPG/FPPT techniques was carried out. This analysis was based on some key performance indices, such as the type of control strategy, irradiance pattern, variation in G, region of operation, speed of tracking, steady-state power oscillations, drift severity scenario, partial shading scenario, implementation complexity, stability, fast dynamic response, robustness, reactive power, cost, and tracking efficiency. Among existing FPPT algorithms, model-based control has a superior performance in terms of tracking speed and low steady-state power oscillations, with a maximum tracking efficiency of 98.57%.

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum ( $6.83\times 10 ^{-4}$ , $2.06\times 10 ^{-4}$ , and $4.48\times 10 ^{-6}$ ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

A load shifting strategy for the heat pump load (HPL), powered by both utility and the PV, is proposed in this paper. The heat pump is an energy intensive device, and the cost of electricity from the grid can be minimized with the proposed load shifting approach by strategically shifting both the base load and the HPL towards the PV peak, so that the cost during the daytime is reduced. To achieve this purpose in the Maltese context, the heat pump was modeled in the heating mode based on the ambient temperature and the thermal energy delivered. It was observed that with the actual data of the PVs and the existing load, the thermal energy storage can be achieved with self-consumption from PVs.

AbstractOne of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell’s precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E−03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E−03 and 4.7571E−05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.