As concerns for the environment and energy independence leads a transition towards a power grid that depends increasingly on energy from renewable resources like solar and wind, the integration and intelligent control of distributed energy resources (DER) including photovoltaic (PV) arrays, controllable loads, energy storage (ES) and the batteries in plug-in electric vehicles (EVs) will be critical to realizing a power grid that can handle both the variability and unpredictability of renewable energy sources as well as increasing system complexity. In addition to providing added system reliability, DERs acting in coordination can be leveraged to address supply-demand imbalances through Demand Response (DR) and/or price signals on the electric power grid by enabling continuous bidirectional load balancing. Intelligent control and integration has the capability to reduce or shift demand peaks and improve grid efficiency by offsetting the need for spinning reserves and peaking power plants. In this dissertation, the use of dynamic and distributed algorithms that can handle the higher penetration of renewable resources in a more open and transparent energy market is explored. Specifically, we look at solving the power scheduling problem using Model Predictive Control (MPC) to ensure a dynamic response and the Alternating Direction Method of Multipliers (ADMM) to distribute the optimization problem when apropos. MPC allows the DERs to be adaptive and robust while ADMM encourages each DER to cooperate to achieve system-level goals while still operating and functioning independently. This enables policies and incentives to co-develop and work with optimization and control technologies to ensure a smooth transition to an infrastructure that can run on renewable energy resources and a more distributed grid. Climate change and ecological concerns coupled with economic concerns over fossil fuel prices are addressed not only through the integration of cleaner energy sources, but also by providing a way to encourage participation throughout the grid. Since the forthcoming grid will require integrating renewable energy at all different levels, we look specifically at an example of generation in a grid-connected PV system with storage as well as a distributed microgrid with onsite PV generation and demand response capabilities. We present simulation results that demonstrate the ability of the algorithms to respond dynamically to external price signals and provide benefits to the grid while respecting and maintaining the functional requirements of the local resources. Each example uses real data taken from measurements at generation and demand sites. While we focus on two of the more popular solutions currently being explored, the platforms using the algorithms are application agnostic in the sense that they can include a range of DERs with varying objectives. Since the algorithms used are both flexible and scalable, devices can be easily integrated as more come online. The platforms can also be implemented in remote areas and developing countries. In addition to demonstrating that such platforms can be implemented dynamically in real-time, the algorithms can also be used in models and simulations as a design tool to inexpensively develop future systems with more generation from renewable resources which can still operate efficiently and reliably.