Farshad Rassaei

Distributed Scalable Autonomous Market-Based Demand Response via Residential Plug-In Electric Vehicles in Smart Grids

Flexibility in power demand, diverse usage patterns and storage capability of plug-in electric vehicles (PEVs) grow the elasticity of residential electricity demand remarkably. This elasticity can be used to form the daily aggregated demand profile and/or alter instantaneous demand of a system wherein a large number of residential PEVs share one electricity retailer. In this paper, we propose a demand response technique to manage vehicle-to-grid enabled PEVs’ electricity assignments (charging and discharging) in order to reduce the overall electricity procurement costs for a retailer bidding to a two-settlement electricity market, i.e., a day-ahead and a spot or real-time (RT) market. We show that our approach is decentralized, scalable, fast converging and does not violate users’ privacy. Extensive simulations show significant overall cost savings can be achieved for a retailer bidding to an operational electricity market by using the proposed algorithm. This technique becomes more needful when the power grid accommodates a large number of intermittent energy resources. There, RT demand altering is crucial due to more likely contingencies and hence more RT price fluctuations and even occurring the so-called black swan events. Finally, such retailer could offer better deals to customers as well to stay competitive.

Helical antenna to measure radiated power density around a BTS: Design and implementation

A helical antenna is a helical shaped conductor winded around a cylinder. This antenna can radiate in different modes but the axial mode is one of the most commonly used ones since in this mode, it gives the maximum radiation power. This paper presents a novel, simple and inexpensive method to measure the radiated power density around base transceiver stations (BTS). We designed an optimized helical antenna for this application. Our simulation results show that it is possible to measure the power density with about 10% error, which is acceptable for this application. Furthermore, we implemented the designed antenna based on our analytical results and calibrate it. Experimental tests have been carried out to examine the performance of the designed antenna.

Environmentally-friendly demand response for residential plug-in electric vehicles

In December 2015, the world has reached an agreement in Paris by which many countries commit to bolster their efforts about reducing adverse climate changes. Hence, we can expect that decarbonization will even attract more attention in different energy sectors in near future. In particular, both generation side and consumption side are required to be run more congruently and environmentally friendly. Thus, employing the renewables at the generation side along with our proposed decarbonized demand response (DDR) at the consumption side could significantly reduce deleterious impacts on the climate. In this paper, we present such matching demand response (DR) algorithm for residential users owning vehicle-to-grid (V2G) enabled plug-in electric vehicles (PEVs) who obtain electricity from a common retailer. The retailer itself is connected to the wholesale electricity market to purchase and sell electricity. Our simulation results illustrate that substantial cost savings can be achieved along with pollution reduction by our proposed technique.

Modeling daily electrical demand in presence of PHEVs in smart grids with supervised learning

Replacing a portion of current light-duty vehicles (LDVs) with plug-in hybrid electric vehicles (PHEVs) offers the possibility to reduce the dependence on fossil fuels together with environmental and economic benefits. However, charging a myriad of PHEVs will certainly introduce a huge new load to the power grid. In the framework of the development of a smarter grid, the primary focus of the present study is to propose a model for the daily electrical demand from the residential sector in presence of PHEVs. The expected demand from a PHEV is modeled by assigning certain probability distributions to the PHEVs required charging time and the starting time of charge. We assign a normal distribution for the starting time of charge which follows the real world practice. Furthermore, several distributions for the required charging time are considered: uniform distribution, Gaussian with positive support, Rician distribution and a non-uniform distribution coming from driving patterns in real-world data. We generate daily demand profiles by using real-world residential profiles throughout 2014 in the presence of different expected PHEV demand scenarios. Support vector machines (SVMs), a set of supervised machine learning models, are employed in order to find the best model to fit the data. SVMs with radial basis function (RBF) and polynomial kernels were tested. Model performances are evaluated by means of mean squared error (MSE) and mean absolute percentage error (MAPE). We show that the best results are obtained with RBF kernel: maximum (worst) values for MSE and MAPE are about 2.89 10-8 and 0.023, respectively.