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Power versus voltage curves of partial shading photovoltaic (PV) systems contain several local peaks (LPs) and one global peak (GP). Most conventional maximum power point tracker (MPPT) techniques may not follow the GP under partial shading conditions (PSC). The use of metaheuristic techniques such as the bat algorithm (BA) and particle swarm optimization (PSO) can overcome these obstacles. All problems inherent in the using of BA as MPPT of PV systems has been discussed and solved in this paper. The first problem is the random initial values of bats that may cause premature convergence. Therefore, the initial values of bats were modified to be close to the anticipated positions of peaks to reduce the convergence time and improve the chance of capturing the GP. The second problem occurs when shading pattern changes the value and position of the GP which is not configurable because all bats are concentrated at the previous GP; this can be resolved by BA re-initialization. The the third problem is the GP memorized in the execution of the BA code forces the PV system to work at the duty ratio of the highest GP ever seen, which may not be the real GP. This problem is solved by updating the memorized GP. This paper also proposes a new criterion for selecting the optimal swarm size against number of peaks to reduce the convergence time and improve the chance of capturing the GP. To the authors' knowledge, most of these problems inherent in the BA have hitherto not been addressed in the literature. The simulation and experimental results obtained from the proposed modified BA (MBA) with re-initialization have been compared to the PSO and grey wolf optimization (GWO) techniques which show the superiority of using MBA strategy in the MPPT of partial shading PV systems. INDEX TERMS Bat algorithm, boost converter, dynamic global peak, maximum power point tracker, partial shading conditions, PV system.

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The sizing problem of the hybrid energy system (HES) is a crucial issue especially in rural communities because any wrong results can mislead the decision makers for building the new HES. Due to the intermittent nature of the renewable energy sources (RES) such as wind and PV, there will be a need for a high storage system and/or a standby diesel engine, which increase the investment, required, and increases the cost of energy (CoE). The use of smart grid concepts like the demand response (DR) using dynamic tariff can improve the system performance, enhance the stability, reduces the size and investments of HES components, reduces the customers' bills, and increases the energy providers' profits. The DR strategy will allow the customers to share the responsibility of the HES stability with the energy providers to maintain the stability of the HES. The DR strategies should be selected to ensure the balance between the available RES and the load requirements. In this article, a novel DR strategy is introduced to model the required change in the tariff with the battery state of charge and its charging/discharging power. The novel DR strategy is used in the sizing of the HES based on techno-economic objectives using three different soft computing optimization techniques. This article introduces modeling and simulation of the smart grid integrated with hybrid energy systems to supply a standalone load for a rural site in the north of Saudi Arabia. The sizing of the HES is built based on minimizing the CoE and the loss of load probability. The novel DR strategy introduced in this article reduced the size of the HES compared to the fixed load technique by 20.66%. The results obtained from this novel strategy proved its superiority in the sizing and operation stage of the HES.

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers.

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