Distribution networks are facing new challenges with the emergence of smart grids, such as capacity limitations, voltage instability, and many others. These challenges can potentially lead to brownouts and blackouts. This paper presents an innovative technique for optimal siting and sizing of distributed generators (DGs) in radial distribution networks (RDNs). The proposed technique uses a novel algorithm that combines improved grey wolf optimization with particle swarm optimization (I-GWOPSO) by incorporating dimension learning-based hunting (DLH). The proposed I-GWOPSO employs a novel aspect of DLH to reduce the gap between local and global searches to maintain a balance. The main optimization objectives aim to optimally site and size the DG with minimization of active power loss, voltage deviation, and improvement of voltage stability in RDNs. Case studies are simulated with IEEE 33-bus and IEEE 69-bus test systems, for the optimal allocation of DG units by considering various power factors. The results validate the efficacy of the proposed algorithm with a significant reduction in real power loss (up to 98.1%), improvement in voltage profile, and optimal reduced cost of DG operation with optimal sizing across all considered cases. A comparative analysis of the proposed approach with existing literature validates the improved performance of the proposed algorithm.

In addition to zero-carbon generation, the plummeting cost of renewable energy sources (RES) is enabling the increased use of distributed-generation sources. Although the RES appear to be a cheaper source of energy, without the appropriate design of the RES with a true understanding of the nature of the load, they can be an unreliable and expensive source of energy. Limited research has been aimed at designing small-scale hybrid energy systems for irrigation pumping systems, and these studies did not quantify the water requirement, or in turn the energy required to supply the irrigation water. This paper provides a comprehensive feasibility analysis of an off-grid hybrid renewable energy system for the design of a water-pumping system for irrigation applications in Sudan. A systematic and holistic framework combined with a techno-economic optimization analysis for the planning and design of hybrid renewable energy systems for small-scale irrigation water-pumping systems is presented. Different hybridization cases of solar photovoltaic, wind turbine and battery storage at 12 different sites in Sudan are simulated, evaluated, and compared, considering the crop water requirement for different crops, the borehole depth, and the stochasticity of renewable energy resources. Soil, weather, and climatic data from 12 different sites in Sudan were used for the case studies, with the key aim to find the most robust and reliable solution with the lowest system cost. The results of the case studies suggest that the selection of the system is highly dependent on the cost, the volatility of the wind speed, solar radiation, and the size of the system; at present, hybridization is not the primary option at most of sites, with the exception of two. However, with the reduction in price of wind technology, the possibility of hybrid generation will rise.

Multi-agent based small scaled smart grid reinforcement scheme is proposed to manage energy resources by enhancing resilience to supply power to critical loads in peak demand by leveraging demand side management (DSM) for smoothing load profile and optimal energy storage system (ESS) scheduling in response to grid cost. Interconnected microgrids comprise diverse energy resources that allow sharing energy to balance fluctuation by deploying a market that rewards cheap energy. Three stages are defined in this proposed scheme. In the first stage, without DER, consumption charges are reduced to 7.1% through DSM, but customer comfort is compromised. In the second stage, microgrids comprise RER; energy consumption-related charges are reduced to 92.2% by the feed-in tariff (FIT) Program. In the final stage, the inclusion of energy market for trading among microgrids (ESS, diesel and RER) resulting in reduced charges greater than charges incurred from the grid is defined by 122.1%. The findings show that the average power drawn from utility grid increased from 134.7kW to 205.3kW, and peak demand decreased from 591kW to 495kW. Integer linear programming optimization technique is applied to allow the optimal dispatch of microgrids generation to reduce the operation cost to 42.64%. Less reliance on the grid, flexible energy use, and avoiding costly electricity purchases are achieved through DSM, effective resources scheduling, and energy trading.

This paper deals with an energy management problem to ensure the best performance of the recharging tools used in electric vehicles. The main objective of this work is to find the optimal condition for controlling a hybrid recharging system by regrouping the photovoltaic cells and fuel cells. The photovoltaic and fuel cell systems were connected in parallel via two converters to feed either a lithium battery bank or the main traction motor. This combination of energy sources resulted in a hybrid recharging system. The mathematical model of the overall recharging system and the designed power management loop was developed, taking into account multiple aspects, including vehicle loading, the stepwise mathematical modelling of each component, and a detailed discussion of the required electronic equipment. Finally, a simplistic management loop was designed and implemented. Multiple case studies were simulated, statistical approaches were used to quantify the contribution of each recharging method, and the benefits of the combination of the two sources were evaluated. The energetic performance of an electric vehicle with the proposed hybrid recharging tool under various conditions, including static and dynamic modes, was simulated using the MATLAB/Simulink tool. The results suggest that despite the additional weight of PV panels, the combination of the PV and FC systems improves the vehicle’s energetic performance and provides a higher charging capacity instead of using an FC alone. A comparison with similar studies revealed that the proposed model has a higher efficiency. Finally, the benefits and drawbacks of each solution are discussed to emphasise the significance of the hybrid recharging system.

A virtual power plant (VPP) is a cloud based distributed power plant that aggregates the capacities of diverse distributed energy resources (DERs) for the purpose of enhancing power generation as well as trading or selling power on the electricity market. The main issue faced while working on VPPs is energy management. Smart energy management of a VPP is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This research paper proposes a real-time (RT) smart energy management model for a VPP using a multi-objective, multi-level optimization-based approach. The VPP consists of a solar, wind and thermal power unit, along with an energy storage unit and some flexible demands. The term multi-level refers to three different energy levels depicted as three homes comprising of different amounts of loads. RT operation of a VPP is enabled by exploiting the bidirectional communication infrastructure. Multi-objective RT smart energy management is implemented on a community-based dwelling system using three alternative algorithms i.e., hybrid optimal stopping rule (H-OSR), hybrid particle swarm optimization (H-PSO) and advanced multi-objective grey wolf optimization (AMO-GWO). The proposed technique focuses on achieving the objectives of optimal load scheduling, real-time pricing, efficient energy consumption, emission reduction, cost minimization and maximization of customer comfort altogether. A comparative analysis is performed among the three algorithms in which the calculated real-time prices are compared with each other. It is observed that on average H-PSO performs 7.86 % better than H-OSR whereas AMO-GWO performs 10.49% better than H-OSR and 5.7% better than H-P-SO. This paper concludes that AMO-GWO is the briskest, most economical, and efficient optimization algorithm for RT smart energy management of a VPP.

Distribution networks have entered a new era with the broad adoption of the distributed generation (DG) allocation as a practical solution for addressing power losses, voltage variation, and voltage stability. The primary goal is to enhance techno-economic and environmental characteristics while meeting the limitations of the system. In order to allocate DGs in active distribution networks (ADNs) efficiently, this study demonstrates two optimization methods inspired by nature: ant lion optimization (ALO) and multiverse optimization (MVO). Various multi-criteria decision-making (MCDM) methods are used to find the best possible solution among the different alternatives. On the IEEE 33- and 69-bus active distribution networks, the proposed ALO was shown to be effective and produces the highest loss reduction in the IEEE 33- and 69-bus systems at 94.43% and 97.16%, respectively, and the maximum voltage stability index (VSI) was 0.9805 p.u and 0.9937 p.u, respectively; moreover, the minimum voltage deviation (V_D) and annual energy loss cost for the given test systems was 0.00019 p.u and 3353.3 PKR, which shows that the suggested method can produce higher quality results as compared to other methods presented in the literature. Therefore, the proposed ALO is a very efficient, effective, and appealing solution to the optimal allocation of the distributed generation (OADG) problem.