Bulk energy storage systems (ESSs) are essential for integrating renewable energy sources into future power networks, yet their widespread adoption is hindered by high investment costs and uncertain revenue streams in deregulated markets. The economic viability of bulk ESSs is enhanced in this thesis by developing advanced operational strategies that address key operational challenges and maximize revenue potential within energy markets. First, risk management approaches for the dispatch of ESSs under volatile electricity prices are developed, integrating risk-averse optimization techniques to balance potential profits with market uncertainties. By improving decision-making processes, financial risks associated with price fluctuations are mitigated by these strategies. Next, methods are proposed to increase the efficiency and profitability of ESSs by coupling them with other energy processes. By formulating novel dispatch models that optimize interactions across multiple energy carriers and markets, synergies are leveraged to enhance energy utilization and revenue streams. Furthermore, the interaction between ESSs and the electrical grid is explored, modeling it as an optimization problem that accounts for grid constraints such as transmission limitations. To address the computational challenges inherent in these complex models, a machine learning–based framework is introduced that efficiently solves the optimization problems, enabling real-time application. Overall, comprehensive solutions are provided by this research to improve the economic viability of bulk ESSs, facilitating their integration into modern energy systems and supporting the transition toward sustainable and resilient energy networks.