Mining operations are located in increasingly remote areas in order to search for relatively high-grade mineral deposits, despite the challenges that arise. These challenges are fundamentally logistic and directly impact the profitability of the remote operation. One of the main challenges is energy supply, since locations that lack a power grid, fuel pipelines, or adequate—if existing—road access have substantially increased energy-related operating costs. Today, a remote mine's energy costs add up to 40% of total operating expenses; this is in contrast with grid-connected, accessible mines, where the energy costs seldom reach 20% of the total. In searching for more cost-effective energy supply options, the present work uses the optimal mine site energy supply (OMSES) concept to optimize the design and operation schedule of a remote underground mine's energy supply system (ESS). Energy demand, weather, and economic data were collected and processed, emulating a remote mine in the Northwest Territories, Canada. The optimal energy system minimized the total cost of the energy supply, which included not only the operation cost but also the annuitized capital investment in equipment. Subsequently, the optimal system's design for the considered demands and environmental factors was subject to simulation and control optimization. Wind power was included in the formulation. Issues such as the necessary spinning reserve and the penetration curtailment, among others, were analyzed, both in the design and the control problems. The present work identified potential improvements for the integrated design (ID) and control of a remote mine's energy system, in particular when including a renewable energy resource with a considerable level of variability, i.e., wind. The optimal solution included the installation of two wind turbines (WTs), achieving 3% diesel savings with a 20% increase of investment compared with the conventional design. The model was validated with a real project—the Diavik Diamond Mine ESS, which included a wind farm with four turbines. A model predictive control (MPC) approach was chosen to optimize scheduling in a simulation with variable conditions of wind speed and ambient temperature; this proved to be a convenient method to assess the robustness of optimal designs. Results also confirmed the limitations of design optimization when uncertainties related to wind energy were ignored.