Social Welfare Maximization in Smart Grid: Review

Abstract

The modernized power grid system intelligently integrates various advanced Information and Communication Technologies (ICT's) with various renewable energy sources. Moreover, recently huge developments are happening in renewable sources, with enhancement in technologies eras, the role of consumers is shifting towards producers so-called "prosumers". Furthermore, main challenges are enlarged energy demands, uncertainty in weather conditions and varied occupant’s behavioural interventions in energy consumption. So, residential user-end is having potential for energy optimization by energy efficiency, conservation, and active participation. Besides, the dynamic fair energy pricing with respect to generation cost and load demands is motivational parameters to users. On the same, this paper focuses on analysing a novel, attractive business model that can actively participate prosumers through dynamic pricing and incentive based demand response programs for the maximization of social welfare. Further this review paper focusses on various optimization models to achieve social welfare maximization. And also bibliometric analysis has been done through the Scopus database.

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Social Welfare Maximization in Smart Grid: Review
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IOP Conf. Series: Materials Science and Engineering 1099 (2021) 012023
IOP Publishing
doi:10.1088/1757-899X/1099/1/012023
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Social Welfare Maximization in Smart Grid: Review
Gaikwad Sachin Ramnath1 and Harikrishnan R.1
1Department of Electronics and Telecommunication, Symbiosis Institute of
Technology (SIT), Symbiosis International Deemed University (SIDU), Pune, India
E-mail: rhareish@gmail.com
Abstract. The modernized power grid system intelligently integrates various advanced
Information and Communication Technologies (ICT's) with various renewable energy sources.
Moreover, recently huge developments are happening in renewable sources, with enhancement in
technologies eras, the role of consumers is shifting towards producers so-called "prosumers".
Furthermore, main challenges are enlarged energy demands, uncertainty in weather conditions
and varied occupant’s behavioural interventions in energy consumption. So, residential user-end
is having potential for energy optimization by energy efficiency, conservation, and active
participation. Besides, the dynamic fair energy pricing with respect to generation cost and load
demands is motivational parameters to users. On the same, this paper focuses on analysing a
novel, attractive business model that can actively participate prosumers through dynamic pricing
and incentive based demand response programs for the maximization of social welfare. Further
this review paper focusses on various optimization models to achieve social welfare
maximization. And also bibliometric analysis has been done through the Scopus database.
1. Introduction
The traditional electric grid faces diverse problems such as technical, socio-economical, and
environmental. In present scenario, the main challenge like imbalance power supply, due to increase in
demand may lead to power outages and blackouts. Moreover, the peak demand management factor is
important to maintain grid stability and reliability. In addition to this, many times it requires more
generations to fulfil the peak demand. But this extra generation enables us to increase the financial
burden on-grid system as shown in Figure 1.
Figure 1. Major Challenges in Electricity Grid

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Thus, there is a need to deal with Social Welfare Maximization (SWM) model, which is a win–win
situation for energy provider companies, end-user and environment development. The implementation
of SWM majorly depends on optimization in energy, resources and cost. In order to implement SWM
model in the existing electricity grid, there is a dire need to upgrade itself with advanced Information
and Communication Technologies (ICTs). For the optimization of energy, resources and cost the
Renewable Energy Sources (RESs) has to be integrated with Energy Storage System (ESS). Moreover,
SWM model also helps to improve the overall grid stability and reliability. So, electricity grid with
SWM model is having more diverse aspects, complexity, and huge potential. Hence, all grid systems are
going towards modernization, called as "Smart Grid" (SG). The major changes in transformation of
existing electricity grid are shown in Figure1. There are four main challenges related to transforming
the existing grid namely, regulatory and policy shifts, changing market demand, technology innovation
and role of consumer as prosumer [1]. Furthermore, effective smart grid management is essential to
obtain SWM model. Figure 2 shows the hierarchy of SG management. According to Figure 2, SG
management is broadly divided into two groups one is Supply Side Management (SSM) and other is
Demand Side Management (DSM).
Figure 2. Hierarchy of SG Managements
At the SSM, very little scope to do innovative changes for reducing the imbalance between demand and
supply. SSM generally includes the major work like installations of new plants or units which is more
time consuming and costly. But at the demand or load side, consumers having huge scope to adjust the
load using different load strategies. The major load strategies are load shifting, load curtailments, peak
cutting and valley filling and RES generation and storages [2]. So, under DSM, Demand Response (DR)
programs are acting significant role to balance the effective demand-supply. In addition to this, reducing
the peak with filling the valley is important objective of DSM as shown in Figure 2. The successful
implementations of DSM completely depend on the utility's effective DR programs and consumer's
active participation into it. Generally, pricing and incentive factors are more influencing for consumers.
In DR programs, a variety of programs are offered. In addition to this, a dynamic pricing with
proportional or fair pricing is more attractive in DR programs to consumers. The energy fair price rate
is a true cost, which is directly linked with production cost and time-varying load demand market
situations. Here is a scope to improve the efficiency of resources and energy for SWM [3].
Furthermore, data analytics and computational ability is required to improve the customer services
and social welfare in the era of big data. Figure 3 shows the data-driven methodology of DR programs.

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First step is to collect the data set as per objective and application. The deployments of Advanced
Metering Infrastructure (AMI), Phasor Measurement Unit (PMU) and intelligent advanced technologies
are used for monitoring, controlling and data collection purposes. Moreover, the deployed smart meters
generate huge amount of data continuously in an average to every 15minutes. This smart meter data
shows the individual end-users energy consumption behaviour. Second step is to perform pre-
processing and data visualization on collected data. The data visualization will give the Energy
Consumption Curve (ECC) as a consumption pattern. In step three behavioural analysis of end-users
using optimization models is performed. Based on the optimization model performance, in step four
utility companies can develop the DR programs for effective demand side management. So, the DR
programs-based data-driven approach using machine learning and deep learning models will improve
the SG's overall efficiency with achieving the maximization of social welfare [3][4].
Figure 3. Data-Driven Methodology of DR Programs
This paper includes a systematic literature review on data-driven based dynamic pricing schemes for
residential end-users to get maximization of social welfare with bibliometric analysis.
1.1 Objectives of the review
There are two main objectives in this review paper. First is to cover the latest existing literature on SWM
model in smart grid with critical evaluation and discussion. Second is to perform bibliometric analysis
using Scopus database for finding the future directions.
1.2 Organization of paper
The organization of paper includes four sections. Section one is introduction with motivation of SWM.
Second section discusses the review of literature on SWM models. Section three includes the Scopus
database bibliometric analysis using keyword based search technique. At the end conclusion and
discussion is covered under section four.
2. Review of Literature
2.1. Introduction
The smart grid is a broader research area in power grid system. Figure 4 shows the major research areas
in a smart grid which includes Distributed Generation (DG), Integration of Renewable Energy Sources
(RES) with main grid, Improving grid stability by implementations of heuristic algorithms, Smart Grid
(SG) Communications, Power Quality, Demand Side Management (DSM), Cyber Security, Advance
Metering Infrastructure (AMI), Restructuring of power System, Power System Isolation Mode (Micro-
grid), Deregulation and Creating markets for betterment of grid infrastructure. From the above research
areas DSM is considered in this paper. The main objective is to balance the power demand and supply
by reducing the peak and filling the valley through active participations of consumers to get SWM.
Moreover, the user's satisfaction, comfort ness, minimum energy cost, and fairness are the general
properties of DSM algorithms. Under DSM, DR program is considered. The DR is a tool to enhance the

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efficiency, reliability, and elasticity of the electricity system by the active participation of users. So, DR
is a significant part of a smart grid.
As shown in Figure 4, DR programs are classified based on three parameters such as, control
mechanism, offered motivations and decision variables. The first control mechanism DR program
includes centralized programs and distributed programs. The second offered motivation consists of
price-based and incentive-based DR programs. The third DR program is control variable, which includes
task scheduling and energy management [4].
Figure 4. Literature Review Workflow
Different optimization methods for DR program in SWM have been disused. The object function
considered is nonlinear in terms of the difference between the total profit of utilities minus the total
energy price of generations and distribution-transmission networks. Moreover, the smart grid is having
multiple and distributed generation sources like renewable energy resources. So additional uncertainties
and constraint functions should be defined. Furthermore, various solutions have been proposed for SWM
with different optimization methods, objective function, constraint function and design vector that are
taken into consideration for problem formulation and for applied pricing scheme. The DR Program
(DRP) based optimization methods for the SWM model includes partial Swarm Optimization (PSO),
Convex Optimization Problem, Non-linear Programming, Mixed Discrete/Continuous Non-linear
Programming, Mixed Integer Non-linear Programming, Game Theory, Markov Decision Problem and
other remaining methods as shown in Figure 5 [4].

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Figure 5. DRP-Based Optimization Methods for the SWM Model
2.2 Analysis of Latest Work Done
Some of the key findings of DR program with SWM work done in the last few years are discussed in
[4]. A new method of pricing scheme with benefits of ToU (Time of Use) and RTP schemes i.e. hybrid
price-based demand response method proposed to achieve SWM. Then in Day-Ahead (DA) scheduling
of a housing consumer micro-grid, with the uncertainty about decision variables and parameters are
implemented. Technical and operational constraints of SWM for consumer and distribution network
structure discussed. The effective forecasting generation quantity is based on consumer’s daily
consumption profile and weather conditions. Results shows, decrease peak to valley index and
coefficient of variation percentage with a raise in social welfare indicator, power sale at peak times,
compared to other methods. The rebound effect phenomenon is considered. From results, it shows that
if hour's load increases then it fails to give the fair rates [5]. Moreover, the author not included incentive
programs to motivate the participants. There is no provision for consumer's privacy and data security.
Moreover, centralized method will be impractical on increased number of consumers.
Incentive-based DR program has been proposed in [6]. The users’ decision of participation is
analysed in a DA way. The quasi-convex cost function is used for forecasting the base-load price. Based
on prices, the user shifts their load, and reduce their energy bills. Furthermore, the user’s energy price
is calculated according to the users with a same consumption in a particular period using Game Theory
(GT), Expected Utility Theory (EUT) and Prospect Theory (PT). Also formulated a social pricing/billing
mechanism for the complete load of the system. No integration of RES, as sur-plus energy may put on
the main grid and also use in peak load hour to reduce energy bill. The author [7] addressed the issue of
Electric Vehicle’s (EV’s) online charging auction market through DR. The design part protects the
privacy issue of seller and buyer of EV’s by using differential privacy-based auction scheme. This
scheme includes two parts namely, Laplace based winner determination rule and an exponential-based
allocation rule. The results show satisfaction not only for economic and privacy parameters but also to
increase social welfare, satisfaction ratio, social efficiency and computational overhead.
The author [8] proposed a new SWM model which involved EV’s. The author initially addressed the
optimization problem and then moved into mixed-integer linear programming model. The energy
optimization in terms of cost, resources and greenhouse gas emissions by integrating the RES with main
grid was discussed. The results were verified using Monte-Carlo Simulation (MCS) tool to check the
robustness of proposed model.
2.3 Summary of Analysed Papers
The prosumer's active participation through incentive schemes in DRP is the key to succeed in the model.
So, according to the literature, limitations, and scope of DSM in DRP for SWM, many kinds of literature
lags to integrate the RES as a green-clean energy alternative source and ESS to maintain the grid stability
and elasticity. Furthermore, without considering weather conditions it is difficult to build an effective

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forecasting model. Along with this, the pricing should be calculated with respect to production cost by
considering the time of demand so-called "fair pricing". Table 1 include the common research gaps in
literature. This common gaps are the future directions in smart grid for developing robust SWM model
using different optimization algorithms. The optimal approach in SWM model is to integrate the RES
with main grid. The RES takes care of optimization of energy, resources, cost and green gas emissions.
Table 1. List of Common Gaps
Sr.
No.
Common Gaps
1
The weather parameter in load forecasting and load management were missed in [9][10]
2
The integration of RES with main grid as distributed generations was lagging in
[6][9][10]
3
The energy storage system, which helps to reduce the energy bill and improve the power
reliability was not considered in [2][11]
4
The fair time-varying rates, which mainly includes generation cost and demand quantity
was lagging in [5]
5
Pricing mechanism for all types of end-users was not used in [12]
6
The Rebound Effect phenomenon in RTP was lagging in [13]
7
The optimization of energy in cost, resources and CO2 emissions were missed in [9]
8
Less considerations of Plug-in Hybrid Electric Vehicles (PHEV) infrastructures in
micro-grid in [10]
9
The various uncertainty like change in loads, weather condition, end-user’s life style etc.
was not considered in [10]
10
Simulated dataset were used and less studies on ground level problems using real
dataset were followed in [2][3]
11
Machine learning and deep learning based models has not taken care of users data
security and privacy issues in [3][4][5]
2.4 Future Scope
The SWM model is the energy optimization model in smart grid. DR is a key element of smart grid
which helps to implement the SWM model. Moreover, DR programs can be defined more specifically
as modifications in energy practice by occupants from their ordinary utilization curve with respect to
variations in energy cost over a time interval, or to encouragement or reward type payments designed to
stimulate lesser energy utilize at higher wholesale market costs or when system reliability is in risk [11].
Wide future scope is briefed as follows:
1. Price reduction in occupant’s energy bill with the profit maximization of utility providers by
reducing total generation cost [4].
2. Demand reduction through energy optimization and energy efficiency models to maximize the
overall power system elasticity, reliability and stability [11].
3. To motivate end-users through incentives and activities to change their consumption habits [12].
4. To give fair dynamic rates which reflect the electricity value and cost at various time slots with
improving resource-efficiency [11].
5. Design DR program scheme to participate and attract interest of prosumer (Producer + Consumer)
[12].
6. Reduction of demand and total generation with integration of RES with main grid which enables
the Energy Service Provider (ESP) to meet their pollution obligations [11].
3. Bibliometric Analysis
The bibliometric analysis is done from the open-source Scopus database. This structural statistical
analysis helps to choose less saturated and emerging research area. The bibliometric study has the
following main objectives:

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• To study the evolution of work done.
• To enlist quality publications, journals, etc.
• To detects the most prolific authors, institutions, affiliations for future collaborative work.
• To study various funding agencies and their hot areas.
• To collect high impact factors, more cited papers from quality journals.
• To identify the change in the boundaries of the disciplines.
The Scopus database [13] was accessed on 24th October 2020 from the www.scopus.com website.
Based on keywords as query total, 2874 results found, including open access and others. The detailed
process and results are given in the next sub-section.
3.1 Methodology for Bibliometric study
Step I: Search query- "smart grid" or "social welfare maximization" or "SG" or "micro-grid" or "MG"
and "Dynamic Pricing" or "RTP" or "real time pricing" or "time-varying pricing" or
"Maximization for social welfare" or "Residential" or "Unified consumer" or "prosumers".
Step II: Result Analysis- The key word based search is used to enable the bibliometric analysis. Search
query has resulted the following graphs shown in the Figure 6 to Figure 11.
Figure 6 is representing the documents search in recent 10 years and highest publication 395 which
is achieved in year the year 2018. Similarly Figure 7 shows country or territory wise published
documents, with highest documents 554 by United States, followed by china on second and India on
third position. Publications are less in Australia and Canada compared to other countries.
Figure 6. Search Documents by Year Figure 7. Top Ten Country with Document
Figure 8 represent the statistical analysis of documents published by author across the world using
scatter plot. Highest publications of 81 documents has been done by author Javaid N. The highest
published documents found under conference paper followed by article type documents as shown in
Figure 9. The book and business article types are the least published documents.

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Figure 8. Documents by Author Figure 9. Documents by Article Type
A maximum of 98 documents were published by IEEE Transactions on Smart Grid journal of Institute
of Electrical and Electronics Engineers Inc. publisher as shown in Figure 10. All ten journals are Scopus
indexed in different quartiles. The Figure 11 shows the document search by affiliation with largest 96
affiliations is from COMSATS University Islamabad. The number of affiliations in publication indicates
the research culture in the institute.
Figure 10. Documents Published by Source Figure 11. Search Documents by Affiliation
A total 2874 documents which belong to various subject’s and top ten subject are shown in Table 2.
Among all documents engineering subject area is highest by 57% of documents and followed by
computer science and energy. Moreover, smart grid belongs to multidisciplinary area having 20
documents. Table 3 is providing the top ten funding sponsor institutes analysis.
The National Natural Science Foundation of China has given highest funding up to 107 documents
and National Science Foundation on second rank with 63 documents. It means that China is promoting
more in smart grid research and development activities. The funding related documents shows the
information on research domain for applying funding proposal. The objective and theme of each funding
sponsoring institute of various countries are different.

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Table 2. Top Ten Subject Areas Table 3. Top Ten Affiliation
4. Conclusion and Discussions
SG is an organized incorporation of sophisticated computerization technologies, electric grid, and ICT.
Enabling technologies of SG progress synchronized monitoring, controlling and analysis of power
systems. The SG allows the bi-directional flow of power and information among the utility and
prosumers (producer + consumer). The efficiency, reliability, elasticity, and stability of the smart grid
achieved using DSM with DRP as a key element. In incentive-based and price-based DRP, prosumers
are actively involved in the smart grid system. So, it reduces various parameters like energy
consumption, electricity bill and CO2 emissions. All this possible due to various activities such as
integrating RES with the main grid, participating in peak demand management for maximizing benefits
of utility, task scheduling and real-time pricing (fair pricing) DRP at active end-user side.
In this paper, a review and bibliometric analysis using Scopus database done. A methodology on
dynamic pricing schemes to prosumers or consumers to obtain social welfare maximization is discussed.
This model will help to improve the power imbalance issues and maximization of social welfare through
active participation of consumer which forms the main objective of this work. As a future scope data-
driven model to maximize social welfare with energy data analytics and prediction model based on inter-
disciplinary approaches can be done.
5. References
[1] Makrisa P, Dimitrios J, Vergadosa, Mamounakisa I, Tsaousogloua G, Steriotisa K,
Efthymiopoulosa N and Varvarigosa E 2019 A novel research algorithms and business
intelligence tool for progressive utility’s portfolio management in retail electricity markets IEEE
PES Innovative Smart Grid Technologies Europe (ISGT-Europe) pp.1-5.
[2] Jamil A, Javaid N, Khalid M, Iqbal M, Rashid S and Anwar N, 2019 An energy efficient
scheduling of a smart home based on optimization techniques. Advances in Intelligent Systems
and Computing 773 pp.3-14.
[3] Nakabi T A and Toivanen P 2019 An ANN-based model for learning individual customer
behavior in response to electricity prices Sustainable Energy, Grids and Networks 18 pp.100212.
[4] Vardakas J S, Zorba N and Verikoukis C V 2014 A survey on demand response programs in
smart grids: Pricing methods and optimization algorithms. IEEE Communications Surveys &
Tutorials 17(1) pp.152-178.
Subject Area
Document
Business, Management
and Accounting
83
Computer Science
1251
Decision Sciences
97
Energy
1210
Engineering
1635
Environmental Science
333
Mathematics
397
Multidisciplinary
20
Social Sciences
162
Funding Sponsor Institute
Document
National Natural Science Foundation
of China
107
National Science Foundation
63
Engineering and Physical Sciences
Research Council
40
Seventh Framework Programme
35
European Commission
33
European Regional Development
Fund
23
Horizon 2020 Framework
Programme
23
Fundação para a Ciência e a
Tecnologia
21
Natural Sciences and Engineering
Research Council of Canada
21
Fundamental Research Funds for the
Central Universities
20

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[5] Monfared H J, Ghasemi A, Loni A and Marzband M 2019 A hybrid price-based demand response
program for the residential micro-grid. Energy 185 pp.274-285.
[6] Barabadi B and Yaghmaee M H 2019 A new pricing mechanism for optimal load scheduling in
smart grid IEEE Systems Journal 13(2) pp.1737-1746.
[7] Li D, Yang Q, Yu W, An D, Zhang Y, and Zhao W 2020 Towards differential privacy-based
online double auction for smart grid. IEEE Transactions on Information Forensics and Security
15 971-986
[8] Rezaei N, Khazali A, Mazidi M, and Ahmadi A 2020 Economic energy and reserve management
of renewable-based microgrids in the presence of electric vehicle aggregators: A robust
optimization approach. Energy 201 pp.117629-117647.
[9] Huang P, Xu T and Sun Y 2019 A genetic algorithm based dynamic pricing for improving bi-
directional interactions with reduced power imbalance. Energy and Buildings 199 pp:275-286.
[10] Xu X, Chen C 2019 Energy efficiency and energy justice for U.S. low-income households: An
analysis of multifaceted challenges and potential. Energy Policy 128 pp.763-774.
[11] Qdr Q J U D E 2006. Benefits of demand response in electricity markets and recommendations
for achieving them. US Dept. Energy, Washington, DC, USA, Tech. Rep.
[12] Yusta J M, Khodr H M and Urdaneta A J 2007 Optimal pricing of default customers in electrical
distribution systems: Effect behavior performance of demand response models. Electric Power
Systems Research 77(5-6) pp.548-558.
[13] Dataset: Scopus Database (http://www.scopus.com).