MATCH for the Prosumer Smart Grid The Algorithmics of Real-Time Power Balance

Prosumers or proactive consumers are steadily on the rise in emerging Smart Grid systems. These consumers, apart from their traditonal role of using energy from the grid, also are actively involved in individually transferring stored energy from renewable sources such as wind and solar, to the grid. The large-scale integration of renewable generation in the emerging grid will re-define ways of meeting consumer energy demands, and more importantly drive greener and cost-effective utility operations. In this paper, we investigate the problem of matching consumer demand with the grid supply in real-time, and in the presence of renewables. We formulate this problem as a stochastic optimization problem and propose MATCH, a fast distributed real-time algorithm that accounts for the uncertainties in (i) renewable generation, (ii) the latter's transmission through the grid network, (iii) loads, and (iv) energy prices, and balances power in the Smart Grid at all times. MATCH is based on the Lyapunov stochastic optimization framework and scales to localities with a large number of networked renewable generation sources. We validate the efficacy of MATCH through experiments conducted using data modelled on proprietary data obtained from two public utilities. As part of the main results of this work, we show that (a) MATCH outputs unique approximate-optimal grid parameter configuration vectors in real-time that ensure perennial supplydemand balance in the grid at a minimum cost, and (b) mesh transmission network topologies lead to better MATCH outputs when compared to other existing transmission network topologies.