=Paper=
{{Paper
|id=Vol-3058/paper74
|storemode=property
|title=A Review On Demand Side Management Schemes For Optimized Energy Utilization In Smart Grids
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-106.pdf
|volume=Vol-3058
|authors=Ashok Kumar Muthria,Lini Mathew
}}
==A Review On Demand Side Management Schemes For Optimized Energy Utilization In Smart Grids==
https://ceur-ws.org/Vol-3058/Paper-106.pdf
A Review on Demand Side Management Schemes for Optimized
Energy Utilization in Smart Grids
Ashok K. Muthria1and Lini Mathew2
1,2
Department of Electrical Engineering, NITTTR, Sector-26,Chandigarh ,160019,India
ABSTRACT
Smart grids are considered as the basic and fundamental technology through which
environmental pollution and the user’s energy cost is reduced. The management of smart grids is
done by various demands Side management (DSM) techniques to ensure that there is an efficient
flow of power. But it is a complex task in real time as energy demands of consumers rise
continuously in an unpredicted manner. A literature survey is conducted to get an overview about
the role of heuristic techniques in demand side management. The review states that such
algorithms are able to schedule the power cuts in an effective way which in turn minimizes the
load on the power grids. But as there are number of heuristic algorithms available it will be a
challenge to select the efficient approach. Moreover, the important factors such as load, cost etc.
are also drawn out from the survey to help the future research to give an efficient DSM system.
Keywords
Demand side management, electrical systems, energy management, energy efficiency, etc.
1. INTRODUCTION
With the increasing demand and use of the traditional fossil fuels like diesel and petrol and their high
prices, it becomes extremely essential to use alternative ways in order to meet the future energy demands
that are energy efficient and provide green and sustainable environment. The electricity grid is being
transformed into a dependable and intelligent cyber physical system (CPS) in which information and
communications technology (ICT) is integrated with the traditional grid to enhance their performance [1].
In addition to this various renewable energy resources such as wind, solar etc. are utilized along with the
effective and novel DSM methods to meet the increasing demands [2]. DSM is a power supply strategy to
enable customers to follow procedures and activities which are favourable to all parties. By doing so, all
the abnormal activities that change the load demand can be analysed and amended [3]. However, the
introduction of DSM raises the complexity in current power systems as DSM needs power system loads
and generators, to be controlled. Consequently, there will be extra costs utilized in installing sensors,
supplying encouragement to DSM and conducting general DSM tasks. In the smart grid, energy providers
can transfer and deliver the power generated to customers with low running costs by using DSM
techniques [4]. When demand for electricity is greater than output, the traditional approach raises the
power generating unit and generates user electricity to satisfy their energy needs. However, this approach
is not suitable due to the greenhouse effect.
_____________________________
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021,
NITTTR Chandigarh, India
EMAIL: ashokmuthria1976@gmail.com (A. 1); lini@nitttrchd.ac.in (A. 2)
ORCID: 0000-0001-5371-9092 (A. 1); 0000-0002-3344-5378 (A. 2)
©2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The issue of energy demand can be addressed by a proper DSM operation in which without adding an
extra generator, the DSM handles and monitors the necessary energy for end users. It manages demand
by introducing planning algorithms. The response to demand
reduces energy consumption and prevents power grid overuse [5]. In addition, by enforcing proper
scheduling practices, this would offer the cost advantage for customers and last for decades.
Various DSM techniques are shown in Figure 1. Energy conservation, demand response and strategic load
growth are the core components of DSM. The demand response is categorized into two categories i.e.
Market-based program and reliability-based program.
Figure 1: Tree diagram for different DSM techniques
1.1 Heuristic algorithms in DSM
Most of the techniques use both linear and non-linear programming method to solve the DSM problem.
However, these programming techniques cannot handle a large number of controllable devices which
have several computation patterns and heuristics. They may not find a feasible solution when the
computational time is too high or when the problems belong to non-convex programming, Mixed Integer
Nonlinear Programming or nondeterministic polynomial time (NP-hard) problems. These issues can be
solved by using heuristic-based evolutionary algorithm that provides a fast and best optimal solution [6].
The heuristic based methods like genetic algorithm, Ant Colony Optimization and Particle Swarm
Optimization (PSO) can search very large spaces of candidate solutions and find globally optimal solution
in polynomial time. In addition to this, various other optimization algorithms were used which are
discussed in section 2 and Table 1.
2. RELATED WORK
Significant studies have been proposed, and this section presents the works done in this field. Hussain et
al. [7], implemented a home energy management system based on the genetic algorithm (GA) and the
harmony search algorithm (HSA), to minimize energy costs and the peak to average ratio. Xu et al. [8],
the uncertain optimization approach was turned into a convex optimization question by implementing the
probability theory. Vatul et al. [9], carried out the DSM strategy on two systems first was on the RTS 24
bus system with wind energy sources spread at some system nodes and second on the institutional load of
the built solar power plant to minimize the customer's utility bills. An immediate billing. Supriya P. [10],
addressed a Game Theoretical Approach for Demand Side Management based on Nash Equilibrium (NE)
utilizing consumer-based priority load control, by considering the power deficit. Nawaz et al. [11],
described the use of Hybrid Bacterial Foraging and Particle Swarm Optimization (HBFPSO) method for
determining the DSM strategy to meet all four fairly independent goals simultaneously, minimized cost,
PAR, CO2 emission, and user discomfort, to return optimal consumer power use schedule. Jian et. al.
[12], developed the generating firm, the grid organization and the society's avoidable expense model for
various DSM investment programs. Hamrouni. [13], An advanced protocol with the combination of two
algorithms Energy Scheduling and Distributed Storage (ESDS) and Microgrid Energy Management
Distributed Optimization Algorithm was given that provides benefits to users. Philipo et al. [14], designed
an algorithm to simulate the daily powers patterns of consumers or users of residential regions for load
shifting and scheduled load reduction. Gupta et al. [15], proposed a combined Multi-Objective Dynamic
Economic and Emission Dispatch model (MODEED) with DSM, to study on the generation side the
benefits of DSM. Vatul, et al. [16], proposed the instantaneous pricing system to minimize the supply gap
for consumers vulnerable to electricity prices. Lin. [17], presented an electrical energy management
system (EMS) architecture based on cloud analytics that has edge analysis with push notifications for
DSM. Hecht et al. [18], provided a significant assessment of DSM techniques to produces extremely
realistic findings that would be utilized for evaluating the efficacy of several load shifting methods.
Sarker et al. [19], reviewed the algorithms and approaches that were utilized in the DSM applications in
SG, and for efficient implementations, the researchers of this paper also reviewed the issues faces by
DSM techniques. Talwariya et al. [20], carried out residential load-scheduling by utilizing EMC (Energy
Management Controller). Pilz et al. [21], proposed a DSM strategy based on the potential technology for
consumer's residential neighbourhoods. Anand Vatul et al. [22], evaluated the DSM strategies on an
institutional load with installed RES to reduce peak demand. Tai et al. [23] suggested method using real-
time multi-agent deep reinforcement learning to lower the peak time value, the power cost, and the PAR
value. Kaddour et al. [24], implemented isolation forest, one class SVM and k-means algorithms to
identify abnormal usage of electricity by users. Other than this few more studies are available in domain
of DSM [25], [26], [27], and [28].
Table 1
Summary of related work done by various researchers
Author Name Methodology used Advantages Disadvantages
& Publication
year
Hussainet al., Implemented a home energy Reduced the electricity Causes loads in
[7], 2020 management system based on the cost of users. winter season as
issue of energy reallocation and used appliances.
GA and HAS.
Xu et al., [8], A random disturbance and quasi Improves the utility of Slow
2020 newton method is applied to consumers as well as convergence
minimize the electricity bills. reduce the cost of rate.
energy consumption.
Vatul et al., Analyzed the proposed DSM strategy Ensures optimum load -
[9], 2020 on two systems first was on the RTS and minimized the
24 bus system with wind energy electricity bills for
sources and on institutional load of customers.
the built solar power plant.
Supriya., [10], Presented a Game theoretical Performs well in smart NE is inefficient
2019 Approach for DSM based on NE grid systems for and mutually
utilizing consumer-based priority predicting loads. beneficial output.
load control, by considering the
power deficit.
Nawaz et al., Implemented HBFPSO method for Reduced the cost or PSO gets stucked
[11], 2020 determining the DSM strategy to electricity, peak average in local minima
minimize cost, PAR, CO2 emission, ratio. while as, BFO has
and user discomfort. slow
convergence
rate.
Jian et al., Introduced an extensive unit cost Reduced load and Energy density is
[12], 2018 model for the power grid, which electricity bills. low and
takes account of the allocation factor complicated.
to represent the different value of
load decreased in different time.
Hamrouni., Demonstrated that the energy Minimizes the load, Complex and
[13], 2020 consumption optimization, energy is optimized. costly.
distributed storage and generation
helps in combined form for demand
side management approaches.
Philipo et al., Designed an algorithm to simulate Save up to 4.87% of -
[14], 2020 the daily powers patterns of energy and 19.23%
consumers or users of residential reduction in electricity
regions. bills.
Gupta and Proposed a combined Multi- Provides benefits to Highly
Subramani., Objective Dynamic Economic and users and companies by dimensional,
[15], 2018 Emission Dispatch model with DSM. shifting the loads coupled
effectively. nonlinear multi
objective.
Vatul et al., Proposed the instantaneous pricing Additional power is Difficult to
[16], 2019 system to minimize the supply gap generated by reducing implement and
for consumers vulnerable to the peak demand. costly.
electricity prices.
Lin., [17], Presented an electrical EMS Minimize the electricity Not secure and
2019 architecture that is based on cloud consumption cost and can result in data
analytics and has edge analysis with carbon dioxide loss
push notifications for DSM. emissions.
Hecht et al., Provided a significant assessment of Decreases the Insufficient
[18], 2021 DSM techniques. consumption of energy planning and lack
in grids and thus of information.
enhanced the efficiency
of load shifting method.
Sarker et al., Reviewed the algorithms and Minimize the carbon SG face
[19], 2020 approaches that were utilized in the emission, cost and peak challenges like
DSM applications in SG. to average ratio. Also, it reliability, data
improves the delivery and
convergence rate. interoperability.
Talwariya et Proposed heuristic-based EMC to Regulates load and -
al., [20], 2020 Carry out residential load- reduce the power
scheduling. consumption.
Pilz et al., Proposed a DSM strategy based on Robust and reliable . -
[21], 2020 the potential technology for
consumer's residential
neighborhood.
AnandVatul Evaluated the DSM strategies on an Reduces the gap Costly and
et al., [22], institutional load with installed RES. between supply and difficult to
2019 demand power. establish.
Tai et al., Proposed a real-time multi-agent Reduces electricity bills, Lead to
[23], 2019 deep reinforcement learning to solve peak to average ratio overloading.
issues related to DSM in HAN. and PAR value as well.
Kaddour et al. Implemented isolation forest, one Reduced load -
[24], 2021 class SVM and k-means algorithms to
identify abnormal usage of electricity
by users.
3. CONCLUSION
This paper presents a brief overview for the available DSM methods in power systems. It is observed
from the literature study that DSM problems may vary under different operating conditions. Number of
optimization techniques such as Heuristic approach, Game energy theory, Home energy management
(HEM) etc. were proposed by researchers to solve different DSM issues such as overloading of the power
grids, costs, power scheduling, demand response etc. Most of the researches were done on the basis of the
standard dataset to work on real-time scenarios. After analyzing the various papers based on DSM we
find that there is still a scope of improvement in these techniques in order make smart grids more
efficient. Moreover, if these improved DSM techniques will be used in future, a balance can be achieved
between the supply and energy demand of customers.
4. REFERENCES
[1] Tafseer Akhtar and B.B. Gupta, Analysing smart power grid against different cyber attacks on
SCADA system, International Journal of Innovative Computing and Applications, vol. 12, no. 4,
2021.
[2] S. D. Nazemi, K. Mahani, A. Ghofrani, M. Amini, B. E. Kose and M. A. Jafari, "Techno-Economic
Analysis and Optimization of a Microgrid Considering Demand-Side Management,” IEEE Texas
Power and Energy Conference (TPEC), College Station, TX, USA, pp. 1-6, 2020.
[3] D. P. Bertineti, L. N. Canha, A. P. Medeiros, R. M. de Azevedo and B. F. da Silva, "Heuristic
Scheduling Algorithm for Load Shift DSM Strategy in Smart Grids and IoT Scenarios," IEEE PES
Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America), Gramado,
Brazil, pp. 1-6, 2019.
[4] Jabir, Hussein J., JiashenTeh, Dahaman Ishak, and Hamza Abunima, "Impacts of Demand-Side
Management on Electrical Power Systems: A Review" Energies, vol.11, no. 5, 2018.
[5] Hossain, M. M., Zafreen, K. R., Rahman, A., Zamee, M. A., & Aziz, T, “An effective algorithm for
demand side management in smart grid for residential load”, 4th International Conference on
Advances in Electrical Engineering (ICAEE), 2017.
[6] Huang C.-L. Huang W.-C.ChangH.-Y.YehY.-C.TsaiC.-Y, “Hybridization strategies for continuous
ant colony optimization and particle swarm optimization applied to data clustering”,Applied Soft
Computing Journal, vol. 9, pp. 3864-3872,2013.
[7] Hafiz Majid Hussain, Pedro H. J. Nardelli, ''A Heuristics-based Home Energy Management System
for Demand Response", arXiv, Electrical Engineering and Systems Science (Signal Processing),
2020.
[8] Zhihong Xu, Yan Gao, Muhammad Hussain, PanhongCheng,"Demand Side Management for Smart
Grid Based on Smart Home Appliances with Renewable Energy Sources and an Energy Storage
System", Mathematical Problems in Engineering, vol.2020, pp. 9545439,2020.
[9] V.A. Vatul, A. Aravinth, K. Narayanan, G. Sharma, T. Senjyu ,"A Novel Demand Side Management
by Minimizing Cost Deviation", Advances in Science, Technology and Engineering Systems Journal,
vol. 5, no. 5, pp. 262-268,2020.
[10] SupriyaP,"Modelling of a Demand Side Management Scheme using Simple Game Theoretic
Approach," Third International Conference on Inventive Systems and Control (ICISC), Coimbatore,
India, pp. 461-466,2019.
[11] A. Nawaz ,G. Hafeez , I. Khan, K. U. Jan, H. Li, S. A. Khan and Z. Wadud, "An Intelligent
Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced
Metering Infrastructure Frameworks in Smart Grid," IEEE Access, vol. 8, pp. 132551-132581,2020.
[12] G. Jian, C. Gao, M. Chen, B. Zhao, Y. Huang, J. Lei and B. Zhang, "The Model and Methodology of
Avoidable Costs in Demand Side Management," China International Conference on Electricity
Distribution (CICED), Tianjin,pp. 410-413, 2018.
[13] C. Hamrouni , "Smart Grid Users Benefits Based on DSM Algorithm Mathematical Optimization
Problems Studied", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4,
pp. 99-104,2020.
[14] GodianaHagilePhilipo, YusufuAbeidChandeJande, Thomas Kivevele, "Demand-Side Management of
Solar Microgrid Operation: Effect of Time-of-Use Pricing and Incentives", Journal of Renewable
Energy, vol. 2020, pp. 6956214,2020.
[15] Gupta, S. Siva Subramani , “Multi-objective dynamic economic and emission dispatch with demand
side management”, Electrical Power and Energy Systems, pp. 334-343,2018.
[16] V. A. Vatul, N. A. Aravinth and K. Narayanan ,"Demand Side Management: A different pricing
Model," IEEE 1st International Conference on Energy, Systems and Information Processing
(ICESIP), Chennai, India, pp. 1-4,2019.
[17] Y. Lin , "A Cloud Analytics-Based Electrical Energy Management Architecture Empowered by Edge
Analytics AIduino with Push Notifications for Demand-Side Management," IEEE 2nd International
Conference on Power and Energy Applications (ICPEA), Singapore, pp. 1-6,2019.
[18] Hecht, C., Sprake, D., Vagapov, Y. et al., “Domestic demand-side management: analysis of microgrid
with renewable energy sources using historical load data,” ElectrEng, vol. 103, pp. 1791–1806, 2021.
[19] EitySarker et al., “Progress on the demand side management in smart gridand optimization
approaches,” Willey, pp. 36-64, 2020.
[20] Talwariya A., Sharama S.K., Singh P., Kolhe M., “Bayesian Game Model: Demand Side
Management for Residential Consumers with Electric Vehicles,” Intelligent Computing Techniques
for Smart Energy Systems,Springer, vol. 607, 2020.
[21] Pilz, M., Al-Fagih, L. “A Dynamic Game Approach for Demand-Side Management: Scheduling
Energy Storage with Forecasting Errors,” Dyn Games Appl, pp. 897–929, 2020.
[22] V. A. Vatul, N. A. Aravinth and K. Narayanan, "Demand Side Management: A different pricing
Model,"IEEE 1st International Conference on Energy, Systems and Information Processing
(ICESIP), pp. 1-4, 2019.
[23] C. Tai, J. Hong and L. Fu, "A Real-time Demand-side Management System Considering User
Behavior Using Deep Q-Learning in Home Area Network,"IEEE International Conference on
Systems, Man and Cybernetics (SMC), pp. 4050-4055, 2019.
[24] Kaddour, Sidi Mohammed and Mohamed Lehsaini. "Electricity Consumption Data Analysis Using
Various Outlier Detection Methods." IJSSCI vol.13, no.3, pp.12-27, 2021.
[25] Kaddour, S. M., & Lehsaini, M. (2021). Electricity Consumption Data Analysis Using Various
Outlier Detection Methods. International Journal of Software Science and Computational Intelligence
(IJSSCI), 13(3), 12-27.
[26] Tafseer Akhtar and B.B. Gupta, “Analysing smart power grid against different cyber attacks on
SCADA system”, International Journal of Innovative Computing and Applications 2021 12:4, 195-
205
[27] Chui, Kwok & Gupta, Brij & Liu, Ryan & Vasant, Pandian. (2021). Handling Data Heterogeneity in
Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition
and Wavelet Packet Transform. Sensors. 21. 3133.
[28] Gupta, BB, Quamara, M. A taxonomy of various attacks on smart card–based applications and
countermeasures. Concurrency Computat Pract Exper. 2021; 33.