The ultimate goal of the project is to help the electric grid become more reliable even when a large amount of electricity is generated from green, but intermittent - sources such as solar and wind. To deal with this intermittency, inexpensive source of energy storage are required. Instead of investing in batteries, this project seeks to obtain cheap storage by manipulating power demand in consumer loads through intelligent decision-making algorithms. By varying power demand up and down from what a load would nominally consume, the load can be made to behave like a battery, effectively creating a source of Virtual Energy Storage (VES). This kind of virtual storage is cheaper than batteries since it is a software-based solution; little additional hardware is needed. Another aspect of the project is to develop decision-making algorithms to cope with operational issues faced by the power distribution networks (that deliver electricity to neighborhoods) due to increasing use of intermittent solar power. There are several technical challenges in achieving this vision: of intelligently manipulating consumer loads and other devices to help the power grid operate reliably. This project aims to address two of these key challenges. One is the design of algorithms to coordinate the actions of a large number of loads without a central decision-maker. Distributed (as opposed to centralized) control will be necessary since the communication and computation burden of a centralized scheme is overwhelming. At the same time, distributed control algorithms need to deliver reliable performance with low communication bandwidth, and be cognizant of the privacy and security concerns inherent in such Cyber Physical Systems (CPS) with humans in the loop. The second challenge is that the demand manipulation at the loads has to be done in such a manner that consumers. Quality of Service (QoS) is maintained in tight, pre-negotiated bounds. A number of technical innovations undergird the proposed work. These innovations include (1) frequency decomposition to map consumer resources to grid?s needs, (2) randomized control algorithms to convert intractable combinatorial optimization problems to tractable convex problems, and (3) physical signaling through grid-frequency and voltage measurements to enable multi-agent coordination without explicit communication.