Araz Ashouri

Comparing control strategies for EV and PHEV fleets providing regulation ancillary services

Plug-in hybrid and fully electric vehicle fleets can be utilized to provide regulation services for the electric grid through a bi-directional Vehicle-to-Grid cooperation. This paper presents several control approaches for the charging or discharging of vehicles batteries as performed by a distribution network operator, which in return benefits from the reduction of power imbalances. A comparison of results reveals the advantage of deploying real-time regulation prices as well as the superior functionality of plug-in hybrid vehicles. The study is based on the German electricity market data of 2010.

Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment

Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads towards production periods. Therefore, this paper introduces a systematic approach to provide a preliminary evaluation of the thermo-economic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an e-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed integer linear programming technique. In order to illustrate the contribution of MPC, different applications on the single and multi-dwelling level are presented and analysed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock.

Building Smart Grid: Optimal Coordination of Consumption with Decentralized Energy Generation and Storage

This work describes an implementation of an office and/or personal smart grid for environmentally friendly buildings. These can be equipped with a local energy source (e.g., photovoltaic panels or combined heat-power units), energy storage devices (batteries, electric hot water boilers, heating, and ventilation systems including air conditioning), a building energy management system with sensors (e.g., providing the room temperatures), and household appliances acting as actuators (in general, split into groups of schedulable and non-schedulable ones). The idea behind this work has been to develop an automatic control system which would optimally decide for the end customer when to buy, sell, or store electric energy with the objective to minimize his total costs. At the same time, it fulfills all constraints in terms of the limits on power allowed to be taken from the grid. For the user, besides the optimal scheduling of household appliances and selling energy to the grid whenever it becomes profitable, the developed prototype gives also the opportunity to monitor the local generation, storage, and consumption in real time. On the other side, the electric utility can produce a real-time pricing signal which reflects daily peaks in consumption. Making such a price signal available for this building energy management system will lead to a decrease of power consumption from the grid. In this project, a model predictive control approach to the energy optimization problem in a building has been proposed. Different scenarios have been run and the results are discussed here.