IEEE Systems, Man and Cybernetics Magazine - April 2022 - 24

DR can be categorized into
two types: incentive- and pricebased
programs [5]. In an incentive-based
program, customers
participate by reallocating their
energy consumption in off-peak
hours, in response to which a
reward (a bill credit or payment)
is given to them. Incentive-based
programs involve direct load control,
load curtailing, emergency
DRs, and so on.
On the other hand, a price-based
program is a more indirect means
of achieving DR. In this approach,
different pricing signals are sent
at varying times to customers. As
a result, customers are induced
to reduce their energy consumption
at certain times to take
advantage of possible monetary benefits. Price-based
programs include time-of-use (TOU) tariffs, real-time
pricing, inclined block rate, critical peak pricing, and
day-ahead pricing [2], [6], [7]. In recent research, pricebased
DR has been widely studied in the residential sector,
particularly in HEMSs.
For price-based DR, the price tariff scheme, i.e., the
DR methods, which
offer financial
incentives to
customers, are
popular and
highly researched
techniques to
achieve DSM since
they incentivize RES
integration along
with DSM.
the peak time interval (which
yields a higher cost) or off peak
(at a lower cost as a result of less
stress on the grid).
In this case, DR algorithms
depend on the flexibility offered
by home appliances. An appliance
is flexible if its energy consumption
can be shifted in time
within the boundaries of end-user
comfort requi rements whi le
maintaining the total consumption
[8]. Home appliances can be
divided into two types based on
their characteristics and priorities
[9], [10]:
◆ Fixed-power appliances have a
fixed power consumption profile
and operating time, e.g.,
ceiling fans, lamps, and TVs.
price bands for different designated time intervals,
including off-peak, midpeak, and peak hours, is important.
The TOU tariff scheme is widely used in many
countries for customers in the residential sector. It
provides the average electricity cost of power generation
during different time periods, thereby enabling
customers to manage their energy usage voluntarily.
Customers have flexibility to use electricity either in
◆ Flexible-power appliances can be controlled, and
their energy consumption profiles can be scheduled
by the HEMS. Their operation can be controlled by
incentive- or price-based programs. These loads can
be further categorized into two types-uninterruptible
and interruptible-depending on whether their
operations can be interrupted or not. Table 1 lists
the appliance classes of fixed and flexible home
appliances with their power ratings and operating
times [9]-[11].
Table 1. Home appliance characteristics:
The type, power rating (PR), and
operating time (OT).
Appliance
Ceiling fan
Lamp
TV
Oven
Washing
machine
Iron
Air
conditioner
Water heater
Type
Fixed
Fixed
Fixed
Fixed
Flexible (uninterruptible)
Flexible (uninterruptible)
Flexible (interruptible)
Flexible (interruptible)
PR (kWh) OT (h)
0.075
0.1
0.48
2.3
0.7
1.8
1.44
4.45
24 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE April 2022
14
13
7
6
8
7
10
8
Heuristic Scheduling Algorithms
Many techniques have been explored to exploit the
flexibility in home appliances and perform DR-based
optimization. A typical approach is to cleverly adapt
optimization techniques to solve linear and nonlinear
objective functions. Recently, artificial intelligence
(AI)-based methods have also become popular. Heuristic
scheduling (HS) algorithms comprise an important
group of techniques to realize energy optimization and
load-shifting operations in HEMSs. Many HAs have
been explored previously, depending on the problem
setup and conditions [2], [7], [11]-[19]. Among the various
optimization techniques, the genetic algorithm (GA)
and harmony search algorithm (HSA) are two important
ones that are particularly suitable for solving constraint-optimization-based
scheduling problems and
the flexible selection criteria of achieving an optimal
(balanced) combination of exploration and exploitation
[11], [20], [21].
Genetic Algorithm
GA is a widely applied algorithm due to its fast computational
time and easy implementation of many complex
problems [22]. It is a metaheuristic algorithm
inspi red by the theory of natural evolut ion and

IEEE Systems, Man and Cybernetics Magazine - April 2022

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