Efficient Control Mechanism Using Data Centers and Electric Vehicles for Grid Frequency Support

Abstract

Frequency regulation is one of the most crucial ancillary services that strives to maintain the demand and supply balance in Smart Grid (SG) setup. The deviations in grid’s frequency can be managed efficiently by adjusting the generation of the supply side against the en- ergy consumption of the demand side. Traditionally, frequency support is provided using conventional generators but their usage leads to the emission of harmful gases, degraded heat rate, and associated wear and tear. Nevertheless, several efforts have been made to manage frequency deviations using flywheels, battery energy storage systems, commercial buildings, and renewable energy resources. However, these agents too have their associated shortcomings such as-heavy installation and maintenance cost; and intermittency issues. Hence, modern frequency regulation agents are required which can strive the delicate balance between demand and supply. Towards this end, the modern Data centers (DCs) and Electric Vehicles (EVs) have emerged as promising solutions for instantaneous frequency support. This can be attributed to their rapid proliferation in the global market accompanied with the large charging and discharging capacities of EVs’ batteries. According to a recent survey, the energy consumption of future transportation sector is expected to increase manifold with the penetration of 400 billion EVs by 2020. On the similar lines, the overall energy consumption of cloud DCs is expected to increase up to 1963.74 TWh by 2020. Hence, it is essential to manage their energy interactions with the grid in accordance with the grid’s frequency; else risk a blackout. Thus, in this thesis, innovative power management schemes have been proposed to leverage the distributed participation of EVs and DCs for grid frequency sup- port. Consequently, four different schemes have been designed based on hierarchical control structure in lieu of grid frequency stabilization. The first scheme regulates the charging and discharging rates of fleet of EVs for managing grid frequency fluctuations. It achieves the same using a Colored Petri Net-based controller; wherein EVs work in close coordination with aggregators and charging stations. The second scheme presents an “Aggregator-based Hierarchical Control Mechanism” for frequency regulation using fleet of EVs. In the proposed solution, EVs’ scheduling problem has been formulated to provide optimal frequency support, while satisfying EVs’ energy demands under battery degradation constraints. This multi-objective primal problem under multiple constraints is solved using an approximation approach. The third scheme also leverages the joint participation of EVs for grid frequency regulation. Additionally, it generates an optimal schedule for EV’s charging and discharging needs with reduced battery degradation and maximal revenue generating opportunities. The overall problem has been formulated as a “Mixed Integer Linear Programming (MILP)” problem and is solved using Mosek solver. Widely accepted Pennsylvania-New Jersey-Maryland (PJM) and ERCOT regulation datasets are used to perform extensive simulations. The results obtained demonstrate that the proposed schemes achieve better performance in comparison with the other competing existing schemes. Last but not the least, the fourth scheme presents an “Energy-aware and SLA-driven (En-SLA) job scheduling framework” for cloud DC equipped with MapReduce. The primary aim of the proposed framework is to explore task-to-slot mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. The designed problem is a complex multi-objective problem comprising of different constraints and is solved using heuristic approaches. The efficacy of the proposed scheme has been validated using real-time data traces acquired from OpenCloud. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.