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The transition from internal combustion engines to electric vehicles (EVs) has received significant attention and investment due to its potential in reducing greenhouse gas emissions. The integration of EVs into electric and transport systems presents both benefits and challenges in energy management. The scheduling of EV charging can alleviate congestion in the electric system and reduce waiting times for EV owners. The use of renewable energy sources (RESs) for EV charging and supporting the grid can help mitigate the uncertainty of these energy resources. Vehicle-to-grid (V2G) technology can be used as an alternative approach in the event of sudden high consumption of the grid. Additionally, cost minimization through large-scale coordinated planning is crucial for the future of e-mobility systems. This review paper focuses on the latest trends considering the various approaches and features in coordinated EV scheduling, as well as the influence of different stakeholders, categorized as single- and multiple-charging stations (CS) and aggregator levels. By implementing coordinated EV scheduling, various methods are presented to better manage the needs and satisfaction of EV owners as well as the profit of CS and the market trends of e-mobility systems. In this regard, EV charging strategies considering V2G, uncertainty evaluation of parameters, coordinated charging management, congestion of CSs and electrical lines, route mapping, and technical and economic aspects of the system hierarchy, including consumers, CSs and aggregators, are reviewed and discussed. [ABSTRACT FROM AUTHOR]

Intelligent regional aggregator (IRA) integrates regional electric vehicles (EVs) to participate in electric power market transaction. However, in the pool market (include the day-ahead and real-time markets), the transaction risk of IRA will be increased by the randomness of pool market price. To this end, the bidding strategy model of IRA aims to minimize the conditional expectation of electricity purchase cost in pool market with considering the pool market price uncertainty. And the model is reformulated to be a stochastic optimization problem with the conditional value-at-risk (CVaR) constraints. The integration function with probability density of random variable is difficult to solve, so the Monte-Carlo simulation method is adopted for prediction of the distribution characteristics of the pool market price. And the model is transformed and discretized by sample average approximation as a solvable convex programming problem. Numerical experiments show that the IRA controls EVs bidding in the pool electricity market, and manages EVs charging profile to achieve the purposes of low peak valley difference and enhance stability of power grid. [ABSTRACT FROM AUTHOR]