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This paper presents a decentralized demand response (DR) framework that, incorporating optimal multi-objective energy management strategies, addresses uncertainties in power networks. The power industry faces challenges in operational optimization due to uncertainties in generation and consumption while evaluating environmental impacts and long-term economic implications. This research introduces an innovative approach by combining advanced DR techniques with a robust energy management strategy designed for uncertain conditions, enhanced by sensitivity analysis to key system parameters. The article considers a network with distributed generating resources, including wind turbines, microturbines, photovoltaics, energy storage systems (ESS), and diesel generators, where generation is controlled hourly based on load fluctuations. Energy consumption optimization requires not only distributed energy generation but also DR to variations in demand, ensuring system reliability under diverse scenarios. Consumers play a crucial role in optimizing energy usage through incentive-based participation. To achieve the research goal of reducing generation and purchasing costs in power grids through optimal energy management and DR to fluctuations, a stochastic approach is employed to obtain the best outcomes. This paper proposes a novel method for optimizing energy consumption in power networks by integrating stochastic techniques to manage uncertainties and variable conditions. The findings show improved network efficiency and cost reduction, achieving a 15.62% decrease in voltage deviation, 37.08% reduction in load demand, 62.05% decrease in active losses, 81.25% reduction in reactive losses, and 33–45% reduction in Expected Energy Not Supplied (EENS).