The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Consequently, a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration is proposed. In the first layer, DSM is employed to minimize the electricity demand (e.g. heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Rutgers University, Laboratory for Energy Smart Systems) team. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in total annual energy consumption and annual cost of microgrid.

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received the Ph.D. degree in reliability engineering and asset management from the University of Cambridge, U.K., in 2016. Currently, he is a research associate at the Distributed Information and Automation Laboratory at the University of Cambridge. His research interests include stochastic process, predictive maintenance, maintenance optimization and reliability of power equipment. Ajith Kumar Parlikad received the Ph.D. degree in manufacturing

The traditional net present value approach to investment in microgrid assets does not take into account the inherent uncertainties in fuel prices, cost of technology, and microgrid load profile. We propose a real option approach to microgrid investment, which includes solar photovoltaic (PV) and gas-fired generation assets. Likewise the (n, m) exchange literature in real option analysis, we examine cases with interdependency and independency of fuel price and the cost of PV technology. This work, however, makes a major contribution by the way of introducing a new parameter, which is defined as the elasticity of the option value to prices and is used in the formulation of closed form solutions. We further extend the (1, 1) exchange problem here to include operational flexibility of microgrid, such that optimal switching between investment, suspension and re-activation can be examined.