The Integration of renewable energy resources suffers from two fundamental issues: variability, and uncertainty of power output. These issues hinder the integration of renewable resources with the existing grid. This paper addresses these issues and proposes a new methodology to minimize the impact of intermittency by offering an alternative approach for energy storage. The concept of value storage is introduced as an alternative to energy storage to replace the typical large-scale battery energy storage system. The concept refers to the storage of excess renewable energy as products from industrial loads instead of the energy itself which enables a demand side management technique to be applied, namely, load shifting for some industrial plants. A hybrid Photovoltaic-wind turbine generator PV-WTG and storage system is proposed to penetrate the existing electric grid, with significant cost savings by displacing the conventional energy generation in a fossil fuel-rich location. A size optimization based on differential system cost is formulated and solved by an enhanced genetic algorithm technique. Uncertainty impact studies were done by incorporating multiple scenarios and comprehensive sensitivity analysis.

An autonomous microgrid is often formed by incorporating distributed generators into the distribution system. However, distributed generators have less inertia compared to traditional synchronous generators, and can cause the system frequency to become unstable. Additionally, as more clusters are integrated into the distribution microgrid, frequency instability increases. To resolve frequency instability in the microgrid cluster, this study proposes a supercapacitor control approach. The microgrid consists of several clusters which integrate wind power generators, solar PV, STP, fuel cells, aqua electrolyzers, and diesel generators. Initially, a small signal model is developed to facilitate the control design. A fractional-order supercapacitor controller is augmented with the developed small-signal model to stabilize the frequency of the microgrid. Furthermore, the controller parameters are optimized to guarantee robust controller performance. The proposed fractional-order supercapacitor controller provides more degrees of freedom compared to the conventional controller. Time-domain simulations were carried out considering several real-time scenarios to test the performance of the proposed controller. We observed that the presented approach is capable of stabilizing the system frequency in all cases. Furthermore, the proposed approach outperforms existing approaches in stabilizing the frequency of the microgrid cluster.

In this study, a new switched-capacitor-based seven-level inverter topology with a photovoltaic system is presented. The proposed topology requires a smaller number of devices and has the ability to self-balance the voltage across the capacitor. The proposed topology configuration is simple and has the ability to extend to higher levels of voltage. This multilevel inverter topology is suitable for low- and medium-voltage applications with photovoltaic (PV) system integration. To improve the PV system efficiency as an input of a DC–DC boost converter, a Fuzzy logic-based maximum power point controller technique is used. A PV system with a DC–DC boost converter integrates with the proposed seven-level inverter topology. The anti-predatory particle swarm optimization (APSO) technique is used to solve the non-linear transduction equations of the seven-level PV switched-capacitor-based multilevel inverter (7L−PV−SCMLI) topology. The proposed APSO is described to minimize the harmonics in the multilevel inverter (MLIs), which is a complex optimization problem involving a non-linear transcendental equation. Furthermore, APSO can be applied in order to solve non-linear transcendental equations for all symmetrical and asymmetrical MLIs that have equal and non-equal DC sources. The APSO-based selected harmonic elimination (SHE) technique obtained the best switching angle value, and the optimized obtained switching angles reduced the total harmonic distortion (THD) of 7L−PV−SCMLI.

This paper represents an approach for renewable energy sources integration with the existing grid. Unique types of loads can be exploited to allow for a sustainable solution to energy integration. A case study of Saudi Arabia is investigated where desalination plants coupled with a water storage tank is utilized to mitigate the variability of renewable sources. A hybrid photovoltaic-wind turbine generator system (PV-WTG) is proposed. The results indicate two critical points. First, even in a fossil fuel-rich country, sustainable renewable sources are economically feasible. The second point is that the typical storage element (i.e., batteries), is not always the best candidate for energy storage as in the case of desalination plants. Instead, storing the excess energy as water in a storage tank is economical and less prone to failures as compared to large-scale batteries. The problem is formulated as an optimization problem and solved using heuristic techniques.

The integration of various computational paradigms, including the Internet of Things (IoT) and edge-cloud platforms, have the potential to enhance the efficiency of evacuation during emergencies. Conspicuously, this study presents an intelligent evacuation framework by integrating the IoT-edge-cloud (IEC) computing paradigm. The proposed framework utilizes IoT technology to collect ambient data and track occupant movement based on the location. Edge computing involves the incorporation of a support vector machine (SVM) to identify emergency events. Additionally, it facilitates real-time spatio-temporal data processing. Furthermore, cloud computing enables the implementation of an evacuation algorithm for efficiently computing a secure and expeditious path based on environmental and occupant data. The presented algorithm generates a comprehensive evacuation map, which serves as a guidance tool to direct individuals toward the designated exit point. Based on experimental simulations, enhanced results were obtained for performance enhancement in terms of temporal delay (5.23 s), decision-making efficiency [precision (95.56%), sensitivity (96.44%), specificity (96.97%), F-measure (96.69%)], energy efficiency (4.56 mJ), reliability (92.69%), and stability (73%).

The paper is concerned with the assessment of energy storage systems at the distribution level. Several projects related to energy storage are reviewed and analyzed for a better understanding of the motivation and benefits gained from such technology. Different applications and technologies of energy storage (ES) are identified, as well as the distinguishing characteristics of these ES technologies. The role of ES in the transition to a sustainable energy system is also identified and discussed. A sizing and location optimization study is conducted to realize the operational impact of installing battery energy storage systems (BESSs) in an existed distribution network in Riyadh, Saudi Arabia. The results will quantify the economic return that BESSs can offer for the electrical network, which would improve the decision making of the electric utility towards handling increasing load demand and power quality issues.