Optimal Management of Mobile Energy Generation and Storage Systems

Abstract

As the global demand for energy increases, new technologies are needed to satisfy the necessity for the electrical network growth. As part of a Smart Grid (SG), Distributed Energy Resources (DERs) are adopted to enhance the efficiency, stability, reliability, and the power quality of the electric grid, in addition to, deferring the need for network upgrades. However, in many cases, there is a temporary need for a DER supply such as during peak grid prices, planned outages, and forced outages. Thus, a mobile energy resource can be utilized in these cases to serve several customers. The research presented in this thesis proposes a new approach to optimally dispatch and schedule a Mobile Energy Generation and Storage System (MEGSS) fleet of electric trucks that encompass three types of DER, namely photo-voltaic (PV) panels, dispatchable generator, and battery energy storage system (BESS). The aim of the proposed approach is to maximize the profit of the MEGSS while meeting customers' requirements. The outcomes of the proposed approach are the day-ahead optimal decisions regarding the customers to be served, the route to be followed by each MEGSS in the fleet, and the onboard resources scheduling. To develop these optimal decisions, the proposed approach utilizes traffic information, customers’ requests, PV generation forecast, and offered energy and demand charges. The MEGSS dispatch problem is formulated as a mixed-integer non-linear programming (MINLP) problem, which is decomposed into two sub-problems: an outer problem and an inner problem. The outer problem decides on the customers to be served and the route to be followed, while the inner problem decides on the onboard resources scheduling. The resulted optimal decisions will be used by the dispatch center to mobilize and schedule the fleet of MEGSS units.The proposed approach has been tested on a typical set of 19 industrial customers to optimally dispatch a sample fleet of two trucks. Results show a maximum daily profit of ＄945 by using two trucks. The suggested method successfully achieved the anticipated goal of the system of attaining maximum profits by reducing daily operation costs.