Environmental concerns and the Paris agreements have prompted intensive efforts towards greener and more sustainable transportation. Persistent expansion of electric vehicles (EV) in the transportation sector requires electric vehicle charging stations (EVCSs) to accommodate the increased demand. Offsetting demand and alleviating the resultant electrical grid stress necessitates establishing grid-integrated renewable energy sources (RESs) where these sustainable strategies are accompanied by variable-weather-related obstacles, such as voltage fluctuations, grid instability, and increased energy losses. Strategic positioning of EVCSs and RES as distributed generation (DG) units is crucial for addressing technical issues. While technical constraints have received considerable attention, there is still a gap in the literature with respect to incorporating the additional complex optimization problems and decision-making processes associated with economic viability, social acceptance, and environmental impact. A possible solution is the incorporation of an appropriate multi-criteria decision analysis (MCDA) approach for feasible trade-off solutions. Such methods offer promising possibilities that can ease decision-making and facilitate sustainable solutions. In this context, this paper presents a review of published approaches for optimizing the allocation of renewable energy DG units and EVCSs in active distribution networks (ADNs). Promising published optimization approaches for the strategic allocation of multiple DG units and EVCSs in ADNs have been analyzed and compared.

Optimally positioning renewable-based distributed generation (DG) units is vital for mitigating technical challenges in active distribution networks (ADNs). With the goal of achieving technical goals such as reduced losses and mitigated unstable voltage, two available optimization methods have been combined for positioning wind-energy DGs: grey wolf optimization (GWO) and opposition-based learning (OBL), which tries out opposite possibilities for each assessed population, thus addressing GWO’s susceptibility to becoming stuck in local optima. This new fusion technique enhances the algorithm’s scrutiny of each area under consideration and reduces the likelihood of premature convergence. Results show that, compared with standard GWO, the proposed OBL-GWO reduced active power losses by up to 95.16%, improved total voltage deviation (TVD) by 99.7%, and increased the minimum bus voltage from 0.907 p.u. to 0.994 p.u. In addition, the voltage stability index (VSI) was also enhanced by nearly 30%. The proposed methodology outperformed both standard GWO on the IEEE 33-bus test system and comparable techniques reported in the literature consistently. By accounting for the uncertainty in wind generation, load demand, and future growth, this framework offers a more reliable and practical planning approach that better reflects real operating conditions.

The global shift towards cleaner energy and sustainable mobility, motivated by international climate commitments such as the Paris Agreement, has accelerated the adoption of electric vehicles (EVs) and renewable energy sources (RESs). In this chapter, a comprehensive review of the key challenges is presented, including enabling technologies and optimisation approaches involved in jointly deploying electric vehicle charging stations (EVCSs) and renewable generation within active distribution networks. Its novelty lies in presenting a framework that integrates technical, economic, environmental, and regulatory perspectives, while formulating a taxonomy that links energy management, optimisation, and policy aspects into a unified planning framework. The review identifies four main categories of barriers: technical, economic and infrastructural, environmental, and planning or regulatory. It also explores a variety of EV charging strategies, such as vehicle-to-grid, grid-to-vehicle (G2V), inductive wireless charging, battery swapping, and smart charging, to assess their influence on load profiles and grid stability. To enable reliable and flexible integration of EVCSs and RESs, advanced energy management frameworks are examined, including demand response strategies, aggregator-based distributed control, and hierarchical control architectures. Furthermore, the chapter highlights the importance of multi-objective optimisation in managing trade-offs among competing goals, such as cost efficiency, carbon reduction, voltage stability, and social acceptance. It reviews a range of decision-support tools for optimal siting, sizing, and operation of EVCS and RES infrastructure. Overall, this work provides a well-structured synthesis of current research and practical developments, offering valuable insights for policymakers, utilities, and researchers to inform future energy planning, investment decisions, and policy formulation.

Improving energy efficiency and renewable-energy hosting capacity in distribution networks is essential for sustainable power-system development. Conventional networks face power losses, voltage deviations, thermal stress, and limited flexibility to accommodate increasing renewable penetration. Accordingly, optimal placement and sizing of photovoltaic-based distributed generation (PV-DG) has become an effective approach for enhancing network performance. This study applies the Grey Wolf Optimiser (GWO) to PV-DG allocation considering three objectives: minimisation of active power loss and total voltage deviation, and maximisation of the voltage stability index. To mitigate the premature convergence of standard GWO, Opposition-Based Learning (OBL) is integrated to enhance exploration through opposite candidate solutions. The proposed OBL-GWO framework is evaluated on the IEEE 33-bus system under three PV-DG penetration scenarios using five-year irradiance and load data from Riyadh, including seasonal uncertainty and long-term load growth. Compared with standard GWO, it achieves average additional gains of 11.24, 10.0, 6.79, and 2.74 percentage points in power-loss reduction, TVD reduction, VSI improvement, and minimum-bus-voltage improvement, respectively. Sensitivity analysis confirms limited dependence on moderate changes in objective weights. A persistent cloudy-week case demonstrates robust performance against GWO, PSO, and GA under adverse irradiance. Scalability tests on IEEE 69- and 118-bus systems confirm that OBL-GWO retains its solution-quality and convergence advantages on larger networks.