This work presents a model to co-optimize the energy and reserves markets, taking into account the penetration and participation of various flexibility providers in both markets. In particular, a detailed unit commitment model has been developed based on mixed-integer programming techniques incorporating energy storage systems with both charging and discharging options, electric vehicles with both grid-to-vehicle and vehicle-to-grid modes, and demand response programs for cost-optimal energy and ancillary services scheduling. The balancing services considered include Frequency Containment Reserves (FCR), automatic Frequency Restoration Reserves (aFRR), and manual Frequency Restoration Reserves (mFRR), in both upward and downward directions. The impact of all these flexibility providers on operational and economic aspects has been assessed through an illustrative case study of a power system with high penetration of renewable energy sources, including thermal and hydroelectric power units. The results highlight the superiority of results when considering the participation of all flexibility providers, especially in the ancillary services market, in terms of economic competitiveness, renewable energy curtailment, associated CO2 emissions, and utilization of costly energy resources. The growing share of flexibility providers in both energy management and reserve provision mix highlights the importance of these sources for power mixes with low carbon content. The methodological framework developed can be employed by system operators, market participants, and policymakers to provide price signals and optimize their resources and portfolios.