3.4.1. Double-Layer Capacitors (DLC)

DLCs, also known as super-capacitors, are a 60-year-old electrochemical double-layer capacitor (DLC) technology. The extremely high capacitance values, on the order of thousands of farads, and the capability to charge and discharge very fast due to extremely low inner resistance are the two important properties. This technology offers a lot of space for advancement because it might result in substantially greater capacitance and energy density than standard capacitors, permitting for more compact designs. Durability, dependability, no maintenance, prolonged lifetime, and functioning across a wide temperature range are further benefits. With the exception of the chemical used in capacitors, which deteriorate in 5–6 years regardless of the number of cycles, the lifetime surpasses one million cycles without degradation. The efficiency is often more than 90%, with discharge times varying from seconds to hours. DLCs are not suitable for long-term energy storage due to their high self-discharge rate, low energy density, and hefty investment needs [28]. As a UPS, a DLC is excellent for bridging small power disruptions. The electric automobile might be used in a unique way, as a buffer system for acceleration and regenerative braking [4].

#### 3.4.2. Superconducting Magnetic Energy Storage (SMES)

SMES devices store magnetic energy in a magnetic field that is generated by a superconducting coil held less than its critical temperature. A temperature of around 4 ◦K was required at the early age but now materials with higher critical temperatures (about 100 ◦K) have been developed and are now accessible. Particle detectors for high-energy scientific experiments and nuclear fusion use large SMES systems with more than 10 MW of power [29]. The main benefits of SMES are high overall round-trip efficiency (85–90%), the

extremely high power output and the extremely fast reaction time: the required power is practically instantly accessible [30]. The energy can be stored basically as long as the cooling system is running, but longer storage times are restricted by the refrigeration system's energy demand.

#### *3.5. Thermal Storage Systems*

Thermal storage systems capture heat from a wide range of sources and preserve it in an insulated storage for later use in industrial and residential applications. Thermal storage systems are used to act as an intermediary between thermal energy demand and supply, making them crucial for the integration of renewable energy sources [31].

There are three forms of thermal storage: sensible heat storage, latent heat storage and thermochemical adsorption and absorption storage [17]. A storage medium can be a liquid or a solid. Thermal energy can only be stored by varying the temperature of the storage medium. A storage system's capacity is determined by the specific heat capacity and mass of the medium used. For latent heat storage, phase change materials (PCMs) are utilized as storage media. Organic (paraffins) and inorganic PCMs (salt hydrates) are also viable options for such storage systems. Latent heat is the energy transmitted during a phase transition, e.g., ice melting [17]. It is also referred to as "hidden" heat since there is no temperature difference during energy transmission. The most well-known latent heat—or cold—storage method is the ice cooler, which uses ice in an insulated container or chamber to keep food cool on hot days. The solid–liquid phase shift is used in the majority of PCMs currently in operation, such as molten salts as a thermal storage device for concentrated solar power (CSP) plants [32–41].

#### *3.6. Superconducting Magnetic Energy Storage*

A superconducting magnetic energy storage system (SMES) is a tools that stores electricity from the electrical grid within the magnetic field of a coil contained of superconducting wire with very little energy loss. The SMES systems are categorized into three groups: power supply, control systems and contingency systems [16].

#### **4. Review: A Journey towards the Future with Guidance from the Past**

A detailed literature review was conducted on deregulated power systems with the integration of renewable energy sources and energy storage devices. The main objectives of the reviews are the maximization of system profit, maximization of social welfare, and minimization of system generation cost and loss by optimal placement of energy storage devices.

K.C. Divya's study [42] focuses on the incorporation of non-conventional energy sources into the power grid and the usage of energy storage devices for profit maximization. The role of electric hybrid car battery storage systems has been considered. This article proposed that energy storage using battery will play an important role in the sustainable and cost-effective functioning of smart electric grids integrated with renewable energy. There is no single storage system that can fulfill all of the criteria for an ideal EES. Various storage systems are compared by Chen in terms of technological specifications and characteristics, applications, and implementation status [43]. Among the developed technologies, CAES is beneficial in terms of the lowest capital cost. R. Banos looked at some of the major difficulties of renewable energy sources in this research [44], such as generation discontinuity, which is an environment-dependent and continuous development in optimization techniques utilizing computing resources. The current state of the art computational optimization methods is reviewed in this paper, providing a comprehensive picture of the most recent research developments in this subject. Heuristic techniques, Pareto-based multi-objective optimization, and parallel processing have all been discovered to be interesting study areas in the realm of renewable and sustainable energy. In the article [45], Aqeel Ahmed Bazmi discusses the importance of modeling and optimization in the power and supply sectors, as well as the future prospects of optimization modeling

as a tool for sustainable energy systems. Modeling and optimization have been found to be effective and valuable methods for solution development in the power and supply sector, particularly for policymakers establishing policies based on extensive assessments of competing technologies and large quantities of scenario studies.

Zhimin Wang [46] developed a unique methodology for energy management in home area using EESs to facilitate energy storage, with the goal of providing wholesale energy at reduced cost and supporting LV distribution networks for network investment reduction. The aim is to create the optimum possible DRs-to-energy-price and network-congestion balance feasible, hence improving customer and network operator advantages. The authors of this work [46] suggest a novel dispatch approach for consumers and DNOs to share ownership of residential energy storage batteries. Ref. [47] discusses various applications of EES technologies in power systems, with a focus on their collaboration with renewable energy sources. The function of ESSs in intelligent micro power grids is also highlighted, as the stochastic nature of renewable energy sources might have an impact on power quality. Each type's applicability in power systems is examined and compared to others. An energy storage system's technological and physical features are also examined in depth. Yanine and Sauma's [48] research focuses on supervisory management of micro-generation systems when connected to the grid and when energy storage is not involved. The goal is to increase energy efficiency, thriftiness, and sustainability. Suggestions have been made that future advancements in smart micro-grid operation should be increasingly focused on recognizing that SHES can be intelligent. Mwasilu [49] conducted a complete evaluation and appraisal of the most recent research and advancements in electric vehicles (EVs) interaction with smart grid, depicting the future electric power system model. The smart V2G system's viability is also addressed. The interactions of electric vehicles with the smart grid as a future energy system model are thoroughly examined in this work.

Zhang [50] presented a two-stage EES-based optimum wind power dispatch system with risk analysis to increase financial advantages through day-ahead operations. Through simulations, the suggested strategy demonstrated promising outcomes in terms of improving financial benefits and risk-reduction capability. Muruganantham, Gnanadass, and Padhy's research [51] demonstrates the several obstacles that DN suffers while adopting RES. This research investigates the significance of energy storage in distributed networks and how to manage the demand. This research provides a high-level overview of the DN's evolution and issues. This provides a quick overview of distribution power flow algorithms, electricity pricing systems and the simultaneous working of DGs and DN. Huang, Xu and Courcoubetis [52] conducted an investigation on three joint market mechanisms to analyze EES investment and operation for locational marginal pricing. The numerical analysis brings out the significance of building integrated storage investment and working mechanisms, while market regulation/schemes focusing simply on EES are unable to produce socially optimal solutions. Das and Bass [53] presented an overview of optimal ESS deployment, size, and operation in power networks in their study. Flywheel energy storage (FES) should also be considered in several distribution network situations. There are many different types of ESSs, each with its own set of benefits and drawbacks. The best ESS for you will be determined by the projected performance improvements, features, and application types. Researchers have already devised various meta-heuristic methodologies for optimization, but there is always room for improvement. Thopil, Bansal, Zhang, and Sharma observed in their research [54] that the abundance of coal-powered generation is not practical, mostly because renewable energy is not yet ready to be the dominant source of energy. Adopting a hybrid and bidirectional energy paradigm, in which customers remain connected to the grid while being fueled by renewable energy sources via smalland medium-scale distributed generators that may be put within the consumer's premises, is suggested as a realistic alternative.

Hirsch (a) defines a microgrid and (b) gives a multidisciplinary portrayal of today's microgrid drivers, practical applications, problems, and future possibilities in the review paper [55]. Proper planning and understanding is needed well in advance to find the most suitable architecture to integrate various distributed energy resources. Various factors including regulations, legal issues, quality of power and financial benefits, etc. will play major roles in deciding the sustainability of microgrids in the long run. Howlader's work [56] on independent ESS to minimize profit uncertainty for retailers in the ISO Market highlighted the problem of financial burden of hour ahead considering load mismatch. This has also concentrated on lowering the cost of IESS installation. This study demonstrates a novel energy market model where IESS is used to compensate for power adjustments. Furthermore, these IESS may be utilized to compensate for predicting errors and solve a variety of other problems. Kong and Jung's research [57] study presents a way for estimating the amount of ESS when there is inadequate data for future PV and WT providers. The predicted ESS size differs from the optimal size with the least amount of error. For future RES suppliers to enhance their profitability, the suggested approach employing polynomial regression is utilized to predict the ESS magnitude. Akbari-Dibavar [58] explored the suitable energy managing techniques of a net-zero emission MPGS incorporating RERs, hydrogen energy systems, and storage units in a deregulate scenario. The robust optimization technique was used to analyze the impacts of wind power uncertainty in order to provide an acceptable level of resilience for the system. Solar and wind power are employed for clean energy generation due to the sustainability characteristic of the micro power grid system (MPGS). Ahmad, Zhang, and Yan [59] provide unique insights into a critical and systematic review of renewable energy and power projection models used as an energy planning tool. The approaches are assessed in terms of prediction applicability, spatial and temporal forecasting accuracy, and relevance to policy and planning objectives. The study's findings help in the recognition of prediction methodologies and allow users to choose the best methods for meeting their intended aims and forecasting criteria. Forecasting capabilities are improving, and some countries are coming closer to developing fully automated smart grids.

Liu, Hu, Kimber and Wang's research gives a complete categorization and assessment of ESS electric grid applications [60]. The most recent optimization and control approaches for each application category were examined. In addition, a cost–benefit analysis for three categories of investors as well as a detailed comparison of market policies regarding ESS involvement in various wholesale markets has been performed. Given the vast variety of improvements in energy storage technologies, the energy storage technologies were critically analyzed in depth and then classified, and comparative studies were conducted to understand the features, limits, and benefits of each energy storage system. Tan, Ramachandaramurthy, Solanki, and Raveendran proposed alternative energy storage system frameworks based on their application [61]. This evaluation included several HESS combinations in which multiple ESS types were blended to provide a better form of energy storage. Mcllwaine, Morrow, Al Kez, and Best [62] undertook a rigorous study of EES and quality of power at the distribution level. The research combined with a Pugh analysis emphasized worldwide trends in power markets with increased renewable energy penetration. The investigation's findings suggest that further study is needed to classify, quantify, and evaluate the installing of bulk energy storage, during distribution.

When RE penetration is low, the electrical market functions efficiently; however, when RE penetration is high, the market is frequently disrupted. Divya Asija threw light on the advancement of renewable energy generation, the inclusion of renewables into the current unregulated power sector, the composition of present power market, main obstacles with RE integration in deregulated power markets, and driving factors [63]. A research study investigated the involvement of a composite energy system comprising wind energy and CAES in the electricity market from the standpoint of a private owner [64]. Due to the high level of unpredictability linked with market values, wind power levels, and regulatory inputs, the problem was modelled using distributionally robust optimization (DRO). The ideal outputs indicate DRO's performance in terms of higher realized earnings and less conservative results. Another study looks at the prospects, problems, and technologies of EVs in a V2G linking system in depth [65]. M.A. Hannan's study demonstrates the benefits of both the EV owners and the power system, as well as relevant suggestions for the future research areas to address existing research gaps and challenges. Dhillon, Kumar, and Singal [66] conducted a detailed analysis of the fundamentals of wind energy, PSP, Wind– PSP System and their present state, applications, and issues with operation in a deregulated market, as well as optimization strategies employed in the advance planning of Wind–PSP System. The researchers proposed optimization strategies such as EA-based, GA with LVQ, HIDSS, and NSGA-II to identify the best feasible solution of complicated computational problems with instabilities for Wind–PSP operation. Global market participants may create a new electricity market architecture in order to reap the benefits of long-term agreements with stakeholders.

Wind energy system modeling is a goal oriented problem that can be solved utilizing advanced computer methodologies. Many algorithms only engage with a sub-model and do not capture the entire picture. The research by Chinmoy, Iniyan, and Goic [67] has focused on essential cost modeling for wind energy projects as well as market associated risk and its mitigation issues. A thorough research on the use of approaches in power balancing in microgrids with renewable generators by Komala, Kumar, and Cherukuri categorized the methods into distinct categories depending on their principle of operation, infrastructure required, and component of the microgrid [68]. The different methodologies, as well as their mathematical models and virtues and drawbacks in application to power balancing in microgrids, have been evaluated. During a literature review, it was discovered that optimal usage of all forms of sustainable energy resources is critical to achieving sustainable energy development (SED). The key problem for SEH modelling is determining the best design/sizing and operating strategy for system components depending on the unpredictability of renewable sources, demand, energy market spot prices, and so on. Lasemi, Arabkoohsar, Hajizadeh, and Mohammadi-ivatloo discovered that uncertainty modelling based on RO and scenario-based stochastic optimization are the most common for SEH modelling [69]. Due to worst-case scenario analysis, a robust method would provide the greatest answer for risk-averse decision makers, whereas a probabilistic approach would provide the optimal answer for risk-neutral decision makers.

Singh and Parida [70] conducted an extensive study on the betterment of the integration of flexible demand as demand response, demand-side management (DSM), and grid proficiency. The evaluation of important data revealed that effective DG allocation will be good for the environment as well as economically favorable for utilities and customers. When DGs are incorporated into the system, the passive distribution or sub-transmission network becomes active, resulting in various technical and economic challenges. Khare, Nema, and Baredar [71] conducted a detailed evaluation of many facets of HRES, focusing on pre-feasibility analysis, optimal size, modeling, control features, and reliability issues. The use of evolutionary techniques and game theory in hybrid renewable energy systems has also been emphasized. Another study looked at current global PHES capacity, technological progress, and hybrid systems (wind-hydro, solar pv-hydro, and wind-pv-hydro) and offered the best options. According to Rehman and Al Hadhrami's research, PHES is the ideal technology for tiny autonomous island grids and huge energy storage, with PHES's efficiency fluctuating in practice between 70% and 80%, with some estimating up to 87% [72]. PHES sizes vary from 1000–1500 MW to 2000–3000 MW across the world. Photovoltaic-based pumped storage systems have only been used on a small scale (few homes only).

The purpose of this study is to provide a complete analysis of current improvements in the ADS's (Active Distribution Systems) operation from the perspective of operational time-hierarchy. In contrast to earlier review publications, prospective applications of ADS devices are evaluated in terms of operating time periods. This study by Ghadi and Ghavidel covers real-world system operations in which network components are initially planned for the stated period ahead, and then their operational status deviations from reference points are updated throughout three time intervals encompassing static, dynamic, and transient periods [73]. There is always a need for DN organizations to investigate current facilities and management systems and then provide some unique practical solutions in the related areas. A critical analysis conducted by Banshwar and Sharma [74] examined the prospects of RES in energy and Ancillary Services (AS) markets and concluded that changes in market designs and norms are still needed in the existing electrical market to integrate energy, AS, and variable energy sources. In another work by Kim and Suharto, storage methods and additional assessments of similar technologies conducted by other scholars were examined [75]. The work has explained the solution techniques to address different difficulties using a case study and also reviewed the assessment parameters.

Tables 1–3 display the summary of reports for considered objective functions, applied system details, and used optimization techniques for the considered pieces of literature. Ghadi and Rajabi's [76] insightful work on the transformation of traditional passive DNs into ADSs, as well as the study based on grid operational features engaged in deregulated electricity market at the distribution level, has provided a new perspective. This study underlines the need to optimize current facility capacity through creative management strategies and practical solutions. Saboori and Hemmati [77] evaluated the challenges of optimal bus position, power rating and energy capacity estimation in distribution networks to improve the functioning of the optimal planning process. While analyzing, energy storage systems and models, as well as their applications and related objective functions, network modelling, solution methodologies and problem uncertainty management, were all taken into account. Zhou and Li's work provides an insight of the design and functional modules of smart HEMS [78], which is critical for a more secure and environmentally friendly energy supply for smart grids. For the purposes of analysis, various non-traditional sources have been considered.

Carreiro and Jorge underline the importance of energy management system aggregators in the Smart Grid framework, particularly in conjunction with demand response programs and technologies that include end-user participation in the provision of ancillary services [79]. They suggest that establishing algorithms, technological benchmarks, and low-cost systems requires deliberate collaboration among academics, industry, and regulators. Modern power management evaluates the performance of various green energy sources against several criteria rather than focusing on a single factor—consumption [80]. This study by Bhowmik and Ray examines the diverse work on separate techniques, integrated approaches, multi-criteria decision-making methodologies, and so on for the green energy planning and scheduling challenge. This study not only confirms that energy management tactics are superior to previous ways, but it also assists scholars and policymakers in implementing the processes. Sundararagavan, Sandhya's research [81] examines the prices of several energy storage systems and identifies the critical aspects that influence their economic feasibility. Rong-Gang Cong [82] identifies several important factors affecting the expansion of renewable energy generation in this article based on a review of current research. Following extensive research, a novel optimization model is developed to optimize future renewable energy generation through the best capacity planning, while taking into account various constraints such as economic, technological, and others. In paper [83], Helder Lopes extensively analyzed several energy storage devices with varying attributes and degrees of maturity. Power rating, discharge duration, energy density in terms of weight and volume, power density, effectiveness, time and cycle durability, and availability have all been compared. Aggarwal, Sanjeev Kumar [84] provides an overview of several price-forecasting approaches used in deregulated systems, as well as an analysis of important difficulties. Lixin Tang [85] presented a policy for a deregulated method to decrease CO2 emissions in generator scheduling for thermal power stations in his study. The proposal called for a new penalty component depending on emissions. The scheduling maximizes generation profitability based on income gained from sales, cost of generating, and the emissions penalty. Enrique B. CEDEO [86] examines the numerous relationships between the various sections of the deregulated power industry, proposing an integrated model for increasing generation and transmission capacity. The purpose of this methodology is to evaluate and find the best macroeconomic indicative investment ideas.


**Table 1.** Summary of reports for considered objective function in the literature.

In paper [87], Pavlos S. Georgilakis proposes a genetic algorithm (GA) solution to the price-based unit commitment problem, which is used by each producing business to maximize its profit in a deregulated market by optimizing its generation schedule. Luo Xing's [88] provides a comprehensive comparison of the most cutting-edge energy storage methods. The study helps to alleviate the problem of selecting acceptable EES technology for a given application and deciding where they would be best integrated into a power generation and distribution system. In his work, Moein Parastegari [89]

develops an optimization model for the energy market that includes auxiliary services. The model is used to jointly operate wind farms (WF), pump-storage units (PSU), photovoltaic (PV), and energy storage devices (ESD). The model takes into account WPG, energy and reserve prices, and PV generation unpredictability. A. Zahedi [90] investigated the potential benefits of grid-connected renewable energy-distributed generating in this review paper (RE-DG). It also looked at the factors that are driving the rising use of RE-DG, the technical challenges that come with high RE-DG penetration, and the effect of RE-DG connection points on system voltage. Piyasak Poonpun provided a study on the life-cycle cost of several grid-connected electric energy storage systems in the paper [91]. The results are given as a cost per kilowatt hour of stored and released power. Das [92] how energy storage can curtail risk factors in a competitive power system. In this study, Stephen Frank [93] examines numerous optimization algorithms that have been utilized to achieve optimal power flow (OPF), with an emphasis on their benefits, downsides, and computational aspects. It begins with an overview and then delves into the deterministic optimization methodologies utilized on OPF.

**Table 2.** Summary of reports for considered system details along with energy storage and renewable energy sources.



#### **Table 2.** *Cont.*

**Table 3.** Summary of reports for used optimization techniques in the literature.


Ramesh Kumar Selvaraju [94] investigated the efficacy of a deregulated electricity system combined with various energy storage technologies in this study. For determining the LFC controller gain values in a deregulated environment, the Artificial Cooperative Search technique, a new two-population-based optimization strategy, is devised. In paper [95], Patil examines the impact of wind energy system on a deregulated energy market from different perspectives. Bus sensitivity factor and locational marginal pricing have been given special attention. Different optimization algorithms have been investigated and slime mold algorithm has been implemented for the first time in this field. In another work [96], same author examines a hybrid system with energy storage and studies profit maximization in deregulated energy market with imbalance cost improvement. It also covers value at risk and cumulative value at risk factors. In paper [97], Ustun examined integration of EV storage with local solar generation to maximize renewable energy capture without overburdening local distribution network. Driving patterns and solar generation profile are studied along with local load profile to actively control EV batteries to maximize local renewable energy capture,

#### **5. Facts and Analysis of Renewable Energy: A Glimpse**

A more significant change in the generating mix is hidden by the total power generation's comparatively high resilience. In particular, generation from renewable sources (wind, solar, biofuels, and geothermal energy, etc.) saw its greatest ever growth despite the decline in overall power consumption. Strong gains in the generation of wind and solar energy were the main drivers of this expansion [98].

The proportion of renewable energy in the world's generation has increased at its quickest rate ever. Around 60% of the increase in worldwide power output over the previous five years has come from renewable sources, with wind and solar power being major among them (shown in Figure 8) [98,99].

**Figure 8.** Transition of renewable energy generation around the world.

An emerging market economy is a developing country's economy that is getting increasingly involved in global markets as it expands. A developing economy is one with a low human development index, low growth, low per capita income, and a preference for agriculture-based activities over industrialization and entrepreneurship. In other terms, a developing economy is also known as a developing country or a less developed economy. With increased infrastructure expenditure in Europe, China, and the United States, investments in power networks are anticipated to increase by 10% in future after dropping for the fourth straight year in 2020 due to the COVID-19 epidemic. As part of the effort to attain carbon-free power generation, measures to build more robust and digital grids are being incorporated with ambitious growth and recovery plans.

However, in the Net Zero Emissions by 2050 Scenario, the level of grid investment triples by 2030, particularly for smart grids and digital investments, which should make up around 40% of all investments in this decade (shown in Table 4 and Figure 9) [98,99].


**Table 4.** Investment spending in electricity networks by region, 2016–2021 in USD billion.

**Figure 9.** Investment spending in electricity networks by region.

The maximum net generating capacity of power plants and other facilities that employ renewable energy sources to create electricity is used to measure the capability of renewable power generation. The data shows the installed and connected capacity at the end of the calendar year for the majority of nations and technologies (shown in Figures 10 and 11) [99–101].

**Figure 10.** Worldwide renewable electricity capacity (MW) statistics.

**Figure 11.** Worldwide renewable electricity generation (MW) statistics.
