This study presents a novel energy optimization framework for local energy networks, addressing the stochastic nature of renewable energy generation, demand fluctuations, and the integration of electric vehicles (EVs) and battery storage systems. The proposed methodology supports fair power allocation by considering operational constraints, dynamic pricing schemes, and demand response (DR) programs. A key contribution of this study is defining an EV's charging and discharging probabilistic model, aiming to enhance interactions with the grid while reducing operational cost and increasing economic returns. In addition, the challenge of optimization is augmented by including market-oriented constraints like real-time pricing and uncertain loading patterns, both of which are dynamically embedded into the decision-making process using the Markov Decision Process (MDP). Moreover, a modified symbiotic organism search (SOS) algorithm has been proposed to deal with the limitations entailed by multi-objective optimization.