Energy storage systems (ESSs) and demand-side management (DSM) strategies have significant potential in providing flexibility for renewable-based distribution networks. Therefore, combining ESSs and DSM strategies with renewable energy sources (RESs) to solve economic, operational, environmental, and power-related political issues has received special attention from power system planners around the world. In this regard, developed countries, which are pioneers in renewable technologies, have proposed various supportive policies and practices for the widespread use of ESSs and DSM strategies in the context of distribution networks. Hence, this paper performs a comprehensive review of the most recent actions taken to build the infrastructure necessary to achieve 100% renewable energy. On this basis, this paper firstly surveys the necessity of using ESSs and DSM strategies in renewable-based distribution networks. Then, the existing policies and incentive programs implemented in different countries for the development of RESs in optimal coordination with ESSs and DSM strategies are presented. The impacts of utilizing ESSs and DSM strategies in improving the economic performance of the renewable-based distribution networks are also investigated. Finally, prevalent energy management strategies, which are proposed to optimize utilization of ESSs and DSM strategies in renewable-based distribution networks, are investigated from the perspective of optimization approaches.

In this paper, a new conceptual design methodology for a hybrid propulsion system consisting of the battery and hydrogen fuel cells is presented based on the minimum energy consumption of the UAV. In this context, an optimization problem with non-linear constraints is constituted to determine the wing loading, aspect ratio, stall velocity, endurance velocity, battery power, fuel cell power, maximum lift coefficient, and zero-lift drag coefficient of the UAV. The solution to this optimization problem, based on the UAV’s minimum energy consumption for the endurance and cruising flight modes, is implemented by meta-heuristic algorithms: particle swarm optimization, cuckoo search algorithm, artificial bee colony, and genetic algorithm. The results of these algorithms are compared in terms of energy consumption. This article contributes to the literature on sizing fuel cell-powered small UAVs based on minimum energy consumption and comparing different metaheuristics. According to the results, the lowest power consumption of the UAV is obtained by the particle swarm optimization algorithm as 2018.63 W. Additionally, the weight ratios calculated with the parameters obtained from the sizing optimization for the UAV propulsion system components and flight stages are compared with a benchmark study in the literature. Finally, the payload weight ratio is also increased by 28.5% by sizing optimization.

Increasing tendency to highly penetrate distribution networks with renewables due to environmental concerns, has lead distribution network operators to establish energy storage systems (ESS) for obtaining required flexibility. In this paper, the role of a widely used ESS, namely, compressed-air energy storage (CAES) is evaluated considering hosting distributed generation capacity of network. A deterministic scheduling strategy is proposed to minimize cost of operation and maintenance of network in presence of PV and wind generation as renewable sources. By utilization of CAES, renewable energy curtailment has been reduced while total electricity cost injected from the grid is minimized. Optimal charge and discharge scheduling of CAES has allowed excess renewable energy to be stored and then injected to the network on peak hours in order to reduce cost by decreasing power grid dependency on peak hours.

The high penetration of renewable energy sources in distribution networks may cause operational risks. Electrical energy storage systems are used in coordination with renewables to overcome bus voltage violations and branch power flow limits. In this study, with a probabilistic approach, a scheduling optimization problem is solved to minimize the total operational cost of a distribution network. In the presence of ESS and RESs, the risk indicators are investigated. Effects of CAES (Compressed Air Energy Storage) and increasing penetration level of PV systems and WTs are estimated. Results may provide a perspective for network operators to increase penetration of RESs in distribution networks.