Page 453 - Decision Making Applications in Modern Power Systems

412 Decision Making Applications in Modern Power Systems

Regarding EV load control, a number of recent studies aim to understand
the adaptation needs of the existing operational control mechanisms to real-
ize smart charging and often propose novel planning and control approaches.
These approaches can be grouped into direct and indirect control approaches
[11]. In direct control approaches the control actions are realized without the
vehicle owner in the control loop. Often, load aggregations are created to
increase the size of the resource, so it can offer economic benefits to the
aggregator [12,13]. In Ref. [14], for example, the authors propose a direct
load control strategy to provide vehicle-to-grid services for three different
predefined mobility patterns. In Ref. [15] the study proposes a smart charg-
ing framework to identify the benefits of nonresidential EV charging to the
load aggregators and the distribution grid. With the assumption of knowing
charging behaviors and direct load controlling of EVs, a case study of over
2000 nonresidential EV supply equipments (EVSEs) shows a reduction of up
to 24.8% in the monthly bill where the demand charge accounts for half of
the utility bill. In Ref. [16] the authors conduct a simulation study for 3000
EVs parked at a municipal parking lot and evaluate the real-time perfor-
mance of a direct control approach, which maximizes the expected state of
charge (SOC) of the EV aggregation in the next time step subject to mobility
constraints. In Ref. [17] the authors develop an optimal direct control scheme
based on global charging costs. The authors compare the proposed direct
control scheme to the local scheduler in a simulation environment, including
100 400 EVs. The authors in Ref. [18] propose a DR strategy by directly
controlling EVs charging start time and develop a severity index defined as
the longest EV charging time delay in percentage. In Refs. [9,19 22] the
authors propose coordinated charging strategies which are applicable for
real-time coordinated charging control of charging stations. The results indi-
cate that the charging load of EVs is flattened without sacrificing the charg-
ing station profits and customers’ service quality.
In indirect control approaches the EV owner manages the control authority
through a decentralized strategy. These strategies often make use of a broad-
casted exogenous price signal. The cost of energy is minimized at each EV
charging station considering the local mobility and charging constraints. An
iterative cost minimal charging framework based on game theory is presented
in and a similar strategy is given in Refs. [23,24]. In addition, EV charging
problem is modeled as a convex optimization problem, with proof of the exis-
tence of optimal solution. However, these approaches do not include the
impacts or additional costs that can be induced on the distribution network due
to increased demand during low-cost periods and often assume that the supply
and non-EV demand is known. Given a supply curve for EV charging, price-
based control strategies may lead to significant oscillations driven by interac-
tions between energy price and demand for a large population size of EVs [2].
Many researchers have investigated the benefits of EV charging and dif-
ferent grid-level services that can be provided by an aggregation of EV