1. Introduction
Demand-side management (DSM) is a very promising approach in a smart grid environment to minimize the energy consumption for the consumer and minimize the energy generation for the utility. The DSM algorithms and schemes include energy conservation programs, energy efficiency programs, and demand response (DR) programs. In the literature, there are different methods in which DSM has been implemented. Different types of the load management system have been implemented, such as a scheduling mechanism for interruptible loads over 16 h [
1], a load-shifting based DSM controller for different categories of load in a residential area, commercial area, and industrial area [
2], a day-ahead load scheduler with user preference data has been developed for different classes of loads [
3], a load-shifting based scheduler for the privacy protection of user energy consumption behavior [
4], and a scheduler for controllable residential loads [
5]. Different DSM schemes have been proposed for residential consumers, such as an intelligent residential energy management system (IREMS) [
6], an optimization model for the operation of a residential microgrid in the presence of various distributed energy sources [
7], and a real-time pricing scheme and progressive pricing scheme for smart grid system involving renewable energy resources [
8].
The major objectives of DSM include minimization of the cost of energy consumption [
1,
3,
4,
5,
6,
8], reduction of peak demand [
3,
8], minimization of user dissatisfaction [
1,
4,
5,
8], and reduction of peak-to-average ratio PAR [
3]. Minimizing the total annual cost of microgrids and total annual emission was acheived in [
7]. In addition, the reduction in the distance between the actual load consumption curve and the optimized load consumption curve has been described in [
2]. The major constraints handled are the maximum power demand limit [
6], operational constraints of the loads, dynamics of renewable energy resources [
6,
8], battery constraints, and economic constraints.
A robust nonlinear controller used for a stand-alone DC microgrid with a battery, a photovoltaic panel (PV), and a supercapacitor [
9] and the power flow through the tie-line is typically regulated at a prescheduled value, thereby enforcing the individual microgrid to manage their respective load at steady-state [
10]. Theoretical graph analysis is presented to analyze per unit load sharing among all the nodes. The stability of the proposed controller is analyzed considering multiple source nodes using Lyapunov’s approach [
11]. Some of the load scheduling mechanisms in residential buildings have been reviewed. An intelligent universal load management scheduler (IULMS) can handle different power loads for a real-time residential apartment in India [
12]. Exploring the possible demand response and load reduction opportunities under the smart grid for residential electricity load profile [
13]. In [
14], an intelligent residential load management system (IRLMS) can handle the dynamics of different types of residential loads.
Some of the research work has been focused on the scheduling of specific appliances. A load manager for scheduling the charging duration for electric vehicles (EV) and air-conditioners (AC) has been proposed in [
15]. A stochastically formulated methodology used for modeling and analyzing the load demand in a domestic distribution system [
16]. Distributed DSM systems were implemented based on the artificial immune network algorithm for air conditioning devices to meet the desired demand to tackle the peak load problem [
17]. The effect of installing renewable energy sources also has been studied. The impact of wind energy penetration in the scheduling of reserves and energy in a smart distribution system was studied in [
18]. A two-stage optimal scheduler was formulated for distributed generation sources in [
19]. Demand response potential and characteristics of smart buildings load play a pivotal role in DR programs [
20].
The role of educational buildings towards energy consumption across the world also has attracted researchers, and it is being explored. Long short-term memory (LSTM) based energy consumption forecasting model has been formulated in an academic building at IIT Bombay, India [
21]. The institutional lab instruments are effectively managed to save energy and estimating the energy efficiency of educational loads [
22]. The relation between using energy and space has been investigated for an educational building in Australia using multiple linear regression models in [
23]. The optimal design and economical aspects of grid-interactive PV system configuration for an educational campus have been carried out using hybrid optimization of multiple electric renewable (HOMER) software [
24]. The load pattern of energy consumption has been studied for Motilal Nehru national instituteof technology (MNNIT), Allahabad, India, using a bottom-up load model [
25]. Artificial intelligence techniques are also proposed to solve the energy demand planning in smart homes [
26]. The dynamic performance of the energy management schemes is compared based on demand response programs [
27]. Demand-side management is used with the PV and the thermal energy storage for peak electric load- shifting [
28]. In general, the reach of meta-heuristics algorithms is wide in computer scientists and researchers of other fields. The simplicity, flexibility, derivation-free mechanism, and local optimum avoidance made meta-heuristic algorithms more popular. The proposed RDSM technique deploys a GWO, enhancing its transfer function [
29]. A game theory-based decentralized control strategy and a game theory-based fuzzy are developed to address the demand-side management problems [
30]. Binary Gray Wolf Optimization [
31] and Improved Binary Grey Wolf Optimization [
32] approaches are used for solving non-linear problems. A demand-side management approach with user satisfaction is implemented for an institutional building [
33]. An approach for cost optimization and is capable of giving maximum satisfaction to the user based on the predetermined user budget for an institutional building [
34]. A coordinated load scheduling and controlling algorithm have been used to schedule the controllable appliances to minimize the peak load consumption [
35]. A virtual queue stability-based Lyapunov optimization technique is employed for real-time energy and comfort optimization in grid-connected solar integrated smart buildings [
36]. An innovative home appliance scheduling (IHAS) framework is proposed based on the fusion of the gray wolf and crow search optimization (GWCSO) algorithm to the cost of electricity reduction and user-comfort maximization [
37]. A combinatorial heuristic-based profound-search algorithm (CHPSA) has been proposed for solving transmission expansion planning (TEP) problems in electric power networks considering wind power penetration [
38]. Optimal power flow (OPF) is an important tool in the planning and operation of the power systems and aims to optimize the operational costs. The proposed fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution (FAHSPSO-DE) algorithms is very effective and robust for solving the OPF problem [
39].
To date, from the literature review conducted, there are different methods of implementing DSM in DC microgrids, residential buildings, educational buildings, specific loads, and scheduling the various generation sources. Most of the previous DSM approaches were carried on residential, commercial and industrial loads, whereas DSM approaches for educational loads are less.
The RDSM technique is proposed to achieve the following objectives for the institution:
Minimize the electricity consumption cost;
Modeling a separate class of loads for air conditioners (AC);
Significant reduction in PAR and peak demand;
Shifts and schedules the institutional loads optimally by considering the constraints;
User comfort index (UCI) is introduced that helps in increasing the user’s satisfaction level for certain non-critical loads by incorporating the SPV.
The paper is organized as follows:
Section 2 provides the proposed architecture of the RDSM–EBGWO algorithm.
Section 3 presents the mathematical representation of the different classifications of load.
Section 4 provides the problem formulation, objective function and constraints.
Section 5 provides the details of the optimization algorithm.
Section 6 provides implementing the proposed methodology with institutional user input data.
Section 7 provides the results and discussion of the different cases.
Section 8 presents the conclusion of the paper and enlightens on possible directions for future research.
Novelty
The DSM techniques for scheduling the loads are more common and popular in residential, industrial and commercial sectors. Educational institutions need DSM strategies to curtail their energy consumption significantly so that annual electricity costs are minimized. The RDSM is proposed for the educational institution Kamaraj College of Engineering and Technology (KCET), Tamil Nadu, India.
The proposed reinforced demand-side management–enhanced binary gray wolf optimization (RDSM–EBGWO) approach shifts and schedules KCET institutional load optimally, accomplishing the main objective of minimizing the electricity consumption cost.
Proposed a dedicated class for temperature-controlled loads, i.e., the air conditioner is modeled using the proposed RDSM–EBGWO, as this type of load contributes 80% of overall peak demand.
Formulation of an index termed as user comfort index (UCI) to measure the degree of user’s comfort before and after deployment of the proposed RDSM–EBGWO control.
A 30 kW solar PV at the institutional premises is integrated into the UCI to increase the percentage of UCI. This lowers the energy consumption from the utility minimizing the electricity consumption cost.
Performance comparison using metrics, such as peak-to-average ratio (PAR), reduction in peak demand and the cost savings was analyzed with binary particle swarm optimization (BPSO), binary gray wolf optimization (BGWO) and enhanced binary gray wolf optimization (EBGWO) algorithms.
2. Architecture
2.1. Proposed Architecture of RDSM–EBGWO
The main objective of the proposed RDSM–EBGWO is to curtail the total electricity cost by shifting and scheduling the institutional loads. The RDSM–EBGWO controller also helps in the improvisation of the institutional load consumption profile and UCI. To accomplish the objectives mentioned above, the load profile of KCET is studied and is categorized into four classes, namely uncontrollable non-shiftable loads (UNSLs), controllable Non-shiftable loads (CNSLs), uncontrollable shiftable loads (USLs), and controllable shiftable loads (CSLs).
UNSLs are loads that are fully uncontrollable and left for the user’s choice. The UNSLs loads are a fan, lights, chargers, elevators, and local area network (LAN) communication, which are also called critical loads. The details, such as power consumption, period of operation, start and end time of the loads, are fetched from a separate UNSLs status collector. The architecture monitors whether the power consumption simultaneously exceeds the maximum demand limit (MDL). If the UNSLs loads exceed the MDL limit, a warning message is given to the particular load type.
Temperature-dependent loads such as air conditioners (ACs), water heaters, space heaters, geysers, etc., are classified under CNSLs. In this paper, only cooling loads are modeled, as cooling loads contribute to 95% of the temperature-dependent loads in KCET. The proposed RDSM–EBGWO controller has a dedicated status collector to monitor and log the parameters of ACs. The parameters collected by the CNSL status collector, such as setpoint temperature, the actual temperature at the instant of operation, tolerance limit, and AC cycle time, are shown in
Figure 1. With these values, the RDSM–EBGWO controller shifts and schedules the operation of ACs optimally. The cycle time of AC is the number of times the AC switches ON/OFF within an hour of operation. An ideal AC, on average, has its cycle time as 4, i.e., AC switches ON/OFF every 15 min for an hour. The normal cycle time for the proper functioning of an AC on average is 4 times in an hour. When this cycle time is either increased or decreased, the efficiency of the AC diminishes or the AC malfunctions. The status collector sends a warning message to the RDSM–EBGWO controller whenever the cycle time of AC exceeds the limit.
The tolerance limit of AC is affected by the following:
These factors affect the tolerance limit cycle time of AC and play a major role in scheduling the AC. The cycle time affects the power consumption of AC to an ample level.
Figure 1 is a pictorial representation of the parameters and factors involved in the modeling of class 2.
The loads that can be controlled, shifted, and operated either continuously or discontinuously within its period of operation are categorized as shiftable loads (SLs). The shiftable loads are further classified as continuously operating uncontrollable shiftable loads (USLs) and discontinuously operating controllable shiftable loads (CSLs). Devices like inverters, batteries are categorized in USLs, which can be scheduled but cannot be interrupted during its period of operation. Laboratory loads, Plug-in hybrid vehicles, Pumps, compressors, etc., are modeled under CSLs that can be interrupted during its period of operation. The USL and CSL have separate status collectors that collect the status of the device and enlist them in their respective categories.
Figure 2 depicts the architecture of the proposed RDSM–EBGWO that has a separate control for each category of load types classified. The smart meters installed at the institutional premises collect the values of the parameters like the demand of the devices, instantaneous power drawn from the utility, etc. These values are logged in some data loggers to store and retrieve data as and when it is needed. With the parameters and data collected status collector marks the status of the device, and the RDSM–EBGWO controller uses this information to shift and schedule the devices optimally.
2.2. User Comfort Loads (UCLs)
The main motive of the proposed RDSM approach is to reduce the peak demand, thereby minimizing the electricity consumption cost. At the same time, the user’s comfort is also important for certain load types. The loads, such as ceiling fans, DC fans, LED lamps, compressors, and pure water pumps, are considered user comfort loads. The shifting and scheduling of these loads increase the user’s dissatisfaction level. To increase the comfort level of these load types, solar PV generation (SPV) is associated with the proposed RDSM architecture. A 30 kW SPV was set up in the institutional premises that supply the user comfort loads, thus considerably decreasing the power consumed from the utility. This approach increases the user’s satisfaction level by increasing the user comfort index (UCI), which indicates the satisfaction level of users for the particular load type.