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Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning

Electrical Engineering Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia
Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
Department of Computer Science & Engineering, Swami Keshvan and Institute of Technology, Management & Gramothan (SKIT), Jaipur 302017, Rajasthan, India
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia
Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21500, Egypt
School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, Tamil Nadu, India
Authors to whom correspondence should be addressed.
Energies 2023, 16(6), 2655;
Submission received: 26 January 2023 / Revised: 24 February 2023 / Accepted: 9 March 2023 / Published: 11 March 2023


Distributed Power Generation and Energy Storage Systems (DPG-ESSs) are crucial to securing a local energy source. Both entities could enhance the operation of Smart Grids (SGs) by reducing Power Loss (PL), maintaining the voltage profile, and increasing Renewable Energy (RE) as a clean alternative to fossil fuel. However, determining the optimum size and location of different methodologies of DPG-ESS in the SG is essential to obtaining the most benefits and avoiding any negative impacts such as Quality of Power (QoP) and voltage fluctuation issues. This paper’s goal is to conduct comprehensive empirical studies and evaluate the best size and location for DPG-ESS in order to find out what problems it causes for SG modernization. Therefore, this paper presents explicit knowledge of decentralized power generation in SG based on integrating the DPG-ESS in terms of size and location with the help of Metaheuristic Optimization Algorithms (MOAs). This research also reviews rationalized cost-benefit considerations such as reliability, sensitivity, and security studies for Distribution Network (DN) planning. In order to determine results, various proposed works with algorithms and objectives are discussed. Other soft computing methods are also defined, and a comparison is drawn between many approaches adopted in DN planning.

1. Introduction

Modern power systems have been transitioning from conventional centralized models into more decentralized grids that take advantage of Renewable Energy (RE) sources and Energy Storage Systems (ESSs) [1]. The intrinsic and variable nature of the RE sources influences the power network’s unwavering quality because of the abundant age of energy at some other point [2]. When researching planning [3] and Distributed Power Generation (DPG) operational segments, there are many critical challenges to consider. The existence of DPG demands the definition of multiple variables, including the optimization technology to be used, the capacity of units, the optimal placement, and the network connection. Power Loss (PL), voltage profile, stability, and reliability are accurately studied about DPG. As DPG penetration increases, system developers benefit from an optimization method to demonstrate the optimal solution for a given DN.
Today, ESS is critical for increasing power network stability and security [4]. For example, many ESSs are used in electric loops [5], such as the Pumped Type Hydro Energy Storage System (PTHSS), Compressed Air Energy Storage System (CAESS), Battery Energy Storage System (BESS), Super-Capacitor Energy Storage System (SCESS), Superconducting Magnetic Energy Storage System (SMESS), and Flywheel Energy Storage System (FESS). Many technologies are combined to create a hybrid power system, as shown in Figure 1.
Hybrid power and ESS are what the word “hybrid” refers to in the field of power engineering. Photovoltaics, wind turbines, and other engine generators, such as diesel generator sets, are a few examples of power generators that work in hybrid Power Systems (PSs). RE technologies such as photovoltaics and wind turbines are used in hybrid PS. Some benefits of a hybrid PS include the following: When the power grid fails, a hybrid PS keeps the lights on. The “size constraints” placed on solar PV by local power networks are avoided with a hybrid PS. Day and night, power is if by a mixed PS. Using a hybrid PV allows to maximize the electricity rate. The hybrid ESS of the flywheel and battery is used as the best-known and most experienced ESS. FESS stores dynamic energy in a rapidly rotating circle linked with an electric machine’s shaft and recovers the energy stored in the network whenever required. ESS is used in electrical utilities’ generation, transmission, and DN. The DN also benefits from some ESS real-time applications [6]. Using these devices in DNs supports peak reduction, energy conversion or load balancing, RE mitigation, efficiency stabilization, transition time changing behavior, congestion, SG extension delay, short-long voltage reduction, and load control on storage. However, DPG has created problems with the security plans of the electricity system and the improper use of insurance-securing devices. An adaptive security system addresses the issues of transitioning to the DPG security system. The soft scaling method uses the metaheuristic methodology to identify the optimal sizing and allocation of DPG-ESS [7].
As a result, some methods have been developed for determining the optimum location and measurement of multiple targets [8]. By incorporating the DPG, some Quality of Power (QoP) issues need to be resolved, along with determining the DPG sources’ optimal placement and size [9]. As the power received from these resources is not pure and solely depends upon different weather conditions, the energy obtained also fluctuates and has harmonics that need to be resolved before incorporating DPG sources. In the case of optimal sizing and placement, the algorithms become more complex as the size of the network increases and more distribution points are presented. That’s why the main aim of this work is to highlight these issues, and hence, these have been discussed in this paper. Furthermore, this survey analyses the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) [10], and other relevant techniques for DPG and ESS placement and sizing in DN. The performance of the Hybrid Optimization Algorithm (HOA) [11] for the optimal allocation of DPG sources is showcased in this survey.
Moreover, by considering the HOAs, more sophisticated results are produced, even for a complex dynamic DN with abundant network constraints and parameters. Aside from optimal allocation, optimization techniques are used in network planning by determining predicted load and Fault Analysis (FA). It improves system stability by designing an efficient and dependable PS based on an optimization technique analysis. During FA, the system parameters obtain distorted to some extent. Hence, the optimization algorithm reorganizes itself and finds a near-optimal solution in the meantime, so, the network is stable even in the case of FA. This article investigates the DN planning conditions for optimal installation and MOA techniques for DPG-ESS. SG is an electrical system that is managed and controlled remotely or automatically. It is an electricity supply network that monitors and responds to local variations in usage through digital communications. Implementing SG presents challenges due to outdated technology, transmission and distribution PL, poor QoP, using RE sources, and security flaws. Energy productivity, dispersed generation, mass-scale renewables, clean power, demand response for dealing with air pollution, used to calculate environmental footprints, support for increasingly smart apparatuses, and infrastructure for new power plants are all SG impacts [12].
Existing work has been performed on DPGs, ESS, optimal sizing and distribution, and Metaheuristic Optimization (MOA), but review studies had not been written on DPG-ESS optimal sizing and location with the MOA technique. This proposed work with objectives is studied, and a conclusion is drawn about using algorithms. “Number of literature on PL > investment cost > PL = operational cost > annual net profit > others,” it is concluded. Differentiating between optimal location and DPG placement is associated with high and impractical costs. The study is led to find out what was good about the algorithms and how to fix what was terrible. All possible improvements were based on optimizing the size and location of DPG-ESS.
The article is organized as follows. Section 2 exhibits DN planning with DPG-ESS. Section 3 introduces and analyses all aspects of DN planning considerations. Section 4 explores the many research studies made on DN planning, along with the findings of the research investigations. Section 5 brings the review analysis to a close.

2. Distribution Network Planning with DPG and ESS

Multiple units and a connected network are required for centralized Power Generation (PG) systems to produce enough energy. Power is transmitted through these systems and provided to residential, commercial, and industrial consumers. Decentralized DPG topology is used to link these generators to the DN. Decentralized energy distribution stations are positioned closer to users and supply electricity to local areas without using large-scale energy facilities. Decentralized energy grids, as opposed to large-scale conventional utility plants, can transfer, store, and harvest electricity or heat in smaller units closer to customers. A decentralized system will be refreshed in a few decades because the power generation of fossil resources is demanding because of high capital costs, infrastructure costs, volatile losses, the depletion of non-renewable sources, and ecosystem impacts. Thus, DNs with high RE generator penetration has become more popular. Depending on the country and utility provider, they have different interpretations. Due to its low impact on the SG, DPG is referred to as “approved for bus connections,” “DN linked at a distribution voltage level,” or “from a few kW to 50 MW,” among other identities. US power stations have recommended a new DPG advanced technologies and application benchmark. Thus, DPG could be identified by connection point, power, and technology type, as shown in Figure 2.
The location of electricity generation and project scale are the primary distinctions between centralized and dispersed generation. Although the two types coexist in the same system, centralized DPG differs significantly. In the centralized method, more extensive facilities concentrate energy production and supply it to consumers who may be kilometers away. In the case of DPG, smaller units produce power close to or at the point of consumption. It uses various RE sources, including solar, wind, hydro, and biogas. The voltage, phase angle, and frequency of DPG sources in SGs must match. Instability within an organization caused by input mismatches can harm profit and loss. The SG powers its connected area in the microgrid even if the primary power supply from PG inputs falls due to a fault or redundant circumstance. The capacity of SG PS is limited, and may not be able to support peak consumer load.
In this case, the optimal location and size of the DPG are critical for supplying uninterrupted power to end users. Energy can move both ways in the microgrid, in any case. Both protective relays are turned on when the effectiveness operator continues distribution lines. To maximize its effectiveness, SG uses a broad range of resources. The SG’s reliability and stability depend on the DPG’s placement. Location identification is more challenging and unpredictable in mesh and connected networks because of the lack of model factors. Electrical energy is stored as heat, electrochemical, electromagnetic, mechanical, and electrochemical energy. The following are some of the implications of an ESS in practical applications: (1) It is suitable for high-power and high-energy applications; (2) It is possible to integrate it into existing power plants; (3) Its installation is simple. With continued acceptance and availability, the cost of batteries falls. Capital cost, power and energy efficiency, rotational speed, performance, low latency, self-release downsides, and workflow lifetime are some of the ESS features required in various situations. This section briefly discusses some ESSs used for combined wind control. Artificial Intelligence (AI)-based ESS for REs is detected at all stages.

2.1. Distribution Network Expansion Planning with DPG Sources

A highly infiltrated RE combined with a low-cost ESS could reduce the unpredictability and flexibility issues that plague electrical networks. In SGs, ESS is required to balance the discrepancy between RE generators, improve them, and save excess energy from renewable hot spots for subsequent use in non-generation or low energy consumption. Many systems have recently been made to explore the possibility of designing an ESS with REs. Photovoltaics (PV) is one of the quickest sustainable RE sources to build up comprehensively, with the ultimate objective being to make nature free from carbon outflows and make it more feasible. It is conceivable, considering the safety measures that managerial frameworks around the globe have taken. One-day PV advancement is demonstrating more productivity and pragmatism at a moderate expense. However, insignificant working expenses, zero discharge, and consistently declining DPG costs make it an accurate pattern for expansion plans in DPG capacity. The solar PV generation schematic structure is shown in Figure 3.
The effects of PV energy on the different candidate units were discussed [13]. In general, the mode of operation of a PV system can be in a grid, off-grid, or hybrid manner. The PS network distributes power from the PV system in “on-grid mode.” In “off-grid” mode, the PV system only powers the house, not the PS. In the hybrid PS function, the PV powers homes and the power distributor during off-peak hours. PV energy is DC. Thus, inverter topologies must convert DC to AC for end users. Centralized [14], string, and multi-string inverters have significantly increased wind turbine limits and costs. Energy costs dropped by 15% [15].
In order to plan new generation units that meet technical and economic constraints, Generation Expansion Planning (GEP) is the best option. GEP is challenging due to the generation unit’s size, duration, and nonlinearity. Due to a lack of knowledge, businesses are forced to deal with this issue in a hazardous atmosphere because the competition among generational companies to maximize their profits forces them to conceal their tactics. Different nonlinear solutions have been presented to address this complex problem in an unclear context. The techniques include game theory, a two-level game model, a multi-agent system, a genetic algorithm, and particle swarm optimization. The GEP issue looks at the game plan of ideal choice vectors over an arranged skyline that decreases speculation and operational costs under fitting obstacles. The general issue with integrating DPG into existing networks is that DN is a passive network that transfers power unidirectionally from the centrally generated high voltage (HV) level to loads at the medium voltage (MV) and low voltage (LV) levels. Unit-wise issues have been connected with control theory decisions. It is confined to the measure of adventure and operational expenses. The resulting Consolidated-Unit-Commitment and Capacity Expansion (C-UC-CE) model is a high-dimensional Mixed Integer Linear Programming (MILP). By then, the model of age advancement is nitty-gritty [16,17].
The control of dynamical systems in designed equipment and processes is the subject of control theory. A model or algorithm must manage the system inputs to ensure control stability, minimize delay, achieve proportional gain, and maintain stable error. It is conducted so that performance is optimized.

2.1.1. Generation Expansion Model

Except for data transfer restrictions, the bus-bar framework model considers future choices, such as the desired units to produce annually, the estimation in a given period, and the entire team. This decision-making is accepted by central facilitators [18]. In order to obtain additional objectives, it might be helpful to illustrate optional methodologies and real-world designs. The operational costs of warm generators are less.
The investment cost in year ‘x’ ( C ln v x ) is represented in Equation (1),
C ln v x = g = 1 N G l = 1 x C l , g i n v I G l , g
where N G denotes the generator entities,
I G l , g is an additional unit of a generator, and C l , g i n v is the generator cost of generator ‘g’ in year ‘x’ [USD/MW].
In the above instance, the annual operating cost is connected to Equation (2).
C O x = t = 1 T g = 1 N G C x v a r P x , t , g + g = 1 N G C g s S x , t , g + C U D . L S x , t
where C O x is the operating cost,
C U D is the unit span cost [USD/MWh],
C x v a r is the variable cost of generator ‘g’ in year ‘x,’
P x , t , g is the PS provided by ‘g’ at hour ‘t’ in year ‘x’ [MW],
C g s is the investment cost of generator ‘g’ [USD] and L S x , t is the load shedding at hour ‘t’ in year ‘x’ [MW] [19].

2.1.2. Demand Constraints

During the planning hour and the crucial PG program, the development of all DPG entities is combined into many systems. As a result, interest rates are determined based solely on the output of these DPG plants, disregarding the producers of the special regimes depicted in Equation (3),
D X = i N p u m p P X , i p u m p p u m p P X , p u m p + P s r p X , F o r : X = t , t r i , h
whereas D(X) denotes the interest projection rate in the specific hours (h) to prepare for time (t) and trimester tri (MWh). The term SRP refers to a single regime producer. All power plants, excluding siphoning plants, follow the “Npump” method, and all pumping power plants are arranged according to the pump technique.
Considering the above investigation work by the decision-makers, the ongoing portions are typical in the model appraisal. Furthermore, in the GEP, two types of plants have been considered to introduce the structures, such as Solar Plants with Storage Capacity (SPWS) and the other that does not have storage capacity (i.e.,) Solar Plants with Non-Storage Capacity (SPWNS). The Forged Outage Rate (FOR) has been perceived as 76% for and 6% for SPWS. Each plant is divided into high-emission and low-emission plants, and the primary sources are High Emission Plants (HEP)-Coal, Oil, Low Emission Plants (LEP)-Solar, and Nuclear plants. The GEP shows appraisals made at four levels out of solicitations. The two definite cases are envisioned with the inclusion of sunlight-based plants, either as an exchange for oil plants or as an elective hypothesis in the second level [20]. The third level determines whether or not sunlight-based plants must limit their cut-off. Similarly, in the fourth level, affectability review on the system’s GEP for various blends of cut-off spotlights on daylight-based entryways (5–10%/10–20% of the total), treatment and discipline costs for discharges from HEP and FOR acknowledged SPWNS and SPWS, for six and 14-year masterminding horizons. A schematic outline of the case appraisals is depicted in Figure 4.

2.2. Distribution Network Expansion Planning with ESS

Distribution Expansion Planning (DEP) aims to reduce the computation weight of the issue. Nevertheless, this could jeopardize DEP’s game plans’ accuracy, particularly in tremendous scope control systems, where power may stream over significant distances. The term “responsibility” refers to the act of determining whether or not a person is responsible for their actions. It has consistently achieved a higher number of line speculations. However, it has compressed the effects of setbacks on ideal transmission advancement [21].

2.2.1. System Modeling

The limitations placed on ESS have been damaged, which now contribute significantly to voltage stability and power flow balancing [22]. Five primary factors need to be considered while deploying Storage Devices (SDs): total capacity in terms of power and energy, charge-discharge rate of efficiency (η), self-discharging rate, and State-Of-Charge (SOC). The ESS constraints and parameters are modeled as follows.
The charging cycle is assumed in Equation (4)
S t r t = 1 δ Δ t S t r t 1 + P c Δ t η / C r
The process of discharging is given in Equation (5)
S t r t = 1 δ Δ t S t r t 1 + P d Δ t / C r η
where Δ t η and Δ t are the charging rates of SD and discharge rate, respectively. ‘P d and ‘P c are the discharging and charging capacity of SD in terms of power, and η is its exact efficiency.
P t t = P c t P d ( t )
where Pt(t) is the power exchange between SG and SD, as given in Equation (6).

2.2.2. Storage Objective Functions

Due to their variant nature, RE resources disturb the QoP by introducing harmonics and transients into the power profile [23]. The appropriate use of ESSs can reduce these effects; however, some limitations are imposed on them, such as high configuration costs. So, the primary goals, such as minimizing PL [24] and other ESS operations, are maximized with a little investment cost. The overall objective can be represented as follows in Equation (7)
O B J = m i n i = 1 k [ z i C i p s t + t T P t i t Δ t q t + t T z i P i t Δ t q t ]
where OBJ is the objective. ‘n’ represents the entire number of buses in the PS, ‘ p s t ’ is the price per energy from ESS, and q(t) is the unit energy cost at the current time ‘t’. The second term in the OBJ represents network losses, while P t i t represents the total volume of power supply at node ‘i’ and is expressed as follows in Equation (8),
P t i t = R { V i ( t ) ( i = 1 k Y i k V i k ) }
V i ( t ) represents the complex voltage at bus ‘i’, while Y i k and V i k are the admittance and voltage levels between bus ‘ i ’ and ‘ k ’ respectively. Equations (9) and (10) express the power constraints at the respective buses.
P i = V i j i V j ( G i j c o s θ i j + B i j s i n θ i j )
Q i = V i j ε i V j ( G i j c o s θ i j B i j s i n θ i j )
where P i and Q i are the total and reactive PL at bus i, θ i j is the phase difference profile between nodes, and G i j and B i j represent the corresponding conductance and susceptance of the power supply.
The negligence of the network issue of DEP is incorporated, disregarding the operational cost of moving impact. Thus, a lossless DEP achieves a system procedure with a lower development cost and higher framework disasters. DN deprivation permits adjusted development with limits on legal expenses [25]. The natural calamity points to different generations and additional inflows throughout the system. If disasters are unnoticed, congested lines may not appear to be embedded. Such sequences are excluded from the arrangement of the imaginary development choices. It is receiving the activation of an additional agreement. Network issues have a significant impact on generator dispatch solicitation. It consequently moves the framework’s progress game plan [26].
The recently referenced effects have been checked in two relevant investigations in the results territory. Generally, a DEP prompts a framework arrangement with lower general system costs. Thus, a trade-off has been refined between fragments with high prices. Some additional investments are completed to mitigate their negative consequences when misfortunes are acknowledged. The ideal control systems are studied to reduce disasters and improve energy output ranges.

2.3. DN Planning under Deterministic and Probabilistic Loading Conditions

Given the deterministic and probabilistic nature of load conditions, planning is conducted from two perspectives: the consumer and the market. Since demand response programs significantly reduce operational costs, the higher DPG penetration and the ESS landing are reinforced by an efficient solution. An incentive-based method has been presented to improve the operation factor. BESS is installed and fulfills the nominal frequency ranges [27]. Another heuristic methodology for optimal DPG formation arrangement has been developed [28]. It authorizes an acceptable level of reduction in PL. Resistance assessment is included without the monotonous computation of Optimal Power Flow (OPF) investigation. Its review is found to be direct. A dynamic system for DPG units’ ideal size and suitable hubs. Its measure is versatile, irrespective of any conditions.
Moreover, it depicts that the perfect territory for a DPG unit is to check its ideal size. Moreover, it improves voltage profiles [29] by reducing system obstacles. A reconfiguration methodology has been presented and recommended. It aims to disperse powers depending on the Power Supply Capacity Index (PSCI) to maintain security challenges because of the ascent in EV quantity with a space-based overall Active Distribution Network (ADN). Consequently, a widespread and natural design is formed to recover all-electric vehicle (EV) loads. After careful consideration, the PSCI ensures standard operating procedures for a terrible power stream with uneven development. Direct load control exists, but ESS has a flawless load prediction model. Peak load shifting could provide consumers with the most optimal energy schedule. There has been some fine-tuning observed. The performance of the heat transformer for retention is improved by vitality [30].
This method of intelligence relies on neural displaying to obtain a multi-variable limit. It improves the controlled and uncontrolled boundaries of the current system, as shown in Figure 5. As was mentioned in Section 1 that ESS has many DN applications [31]. An actual ESS application for DN is its planning [32]. Most operations, whether load balancing or mediation, take the first application into account [33].
Mediation maintains ESS when energy is low-cost. It balances the load during peak demand when electricity rates rise [34]. Thus, the load is distributed at a low cost throughout the service’s lifetime. As shown in Figure 6, the practice of relieving load or balancing is also known as vacuum filling or emission reduction. Energy to charge the ESS is provided by upstream networks (HV/MV stations) of traditional DN and other ADN sources during low-load times. Uncontrolled DPG is powered by renewable sources [35] and turned by small pumping resources. The charging and discharging rate in correspondence to time are demonstrated, while the real PS difference between DPG and load due to the charging and discharging of ESS is presented in Table 1. The optimization algorithm has adopted the YALMIP optimizer toolkit for ESS while validating the operational and investment costs. The charging and discharging behavior of SD, as well as its loading effects, have been thoroughly investigated.

3. Modernized Considerations for DN Planning

Many personnel have developed careful DPG planning in size, and sites are also positioned in DN using soft computing methodologies. Although DPGs produce dynamic and responsive force shifts across various DPG methods, their modest size improves PS productivity. Hence, about 10% to 20% of PL is dropped down through circulation by setting it in the fitting area. However, improper DPG installation leads to immaturity and overheating issues and delays active energy delivery to the system. At the same time, the insufficient size of DPGs fails to recognize their potential. Similarly, redundant redistribution could lead to substantial investment costs. Choices about the sorts, areas, and length of DPG advancements are firmly related, prompting countless usable arrangements dispersed over a significant non-meeting space because of the intricacy brought about by effective organization and energy stream execution. In order to make issues sensible, the factors referenced above have not generally been considered. The case’s adaptability and construction are determined by the specific application requirements, divided into mechanical and monetarily located applications. Here, three studies are presented: reliability, sensitivity, and security. Using these studies, distribution companies manage their networks according to the demands of DPG owners by availing cost beneficiaries.

3.1. Reliability Studies with DPG-ESS

The BESS’s organizing model in the dynamic ADN has been invented [36]. A presumption-based static count method has the advantage of improving BESS reliability. It is developed for the direct and canny computation of the improvement in system resolve quality with BESS during the development phases. Decision factors consolidate arrangement factors and operational system control factors. A two-stage heading procedure for the ideal masterminding of DPG embedded with ESS has been introduced [37]. A perfect method for a local area-wide measurement determines the ideal generation, changes, power supply warming, cooling, and different administrations [38]. The configuration planning is presented and characterized at the local area level using the Energy Hub (EH). Accordingly, the planned issue limits the volume of capital and operational costs with factors addressing energy converters’ determination or capacity systems and connections. The diagram hypotheses are used to design a model as a problem related to the MILP. An intelligent task of DPG units in PS networks is to improve the dependability of the design systems. The speculation list considers both unwavering quality and monetary perspectives [39]. The computation for the ideal situating and dimensioning of ESS has been developed to improve a spiral system’s uncompromising quality by use of an optimization algorithm based on the Teacher-Based Learning Method [40]. The area and size of the ESS significantly affect the unfaltering quality of the network. Regardless, the number of ESS sets determines the system’s standard cost. Subsequently, the calculation is intended to limit the issue’s target capacity, such as costs related to non-supplied energy, an additional cost, for example, the venture and operational cost of ESSs, and PL in DN. The ideal placement of PV panels and BESS has been developed in DN [41]. This issue is multi-target progress in which a proper design is accomplished in two stages with the hereditary calculation innovation’s assistance.

3.2. Sensitivity Studies with DPG-ESS

At a primary level, the basic territories and started restrictions of the DPG are settled using the striking Loss Sensitivity Factor (LSF) algorithm. The ideal foundation cut-off points of the DPG are then developed to support the preferences and consistent quality of the endeavor system voltage and to limit line losses. Following that, the Multi-Objective Ant Optimizer (MOAO) is used to secure the Pareto-ideal courses of action. Ant Lion Optimization (ALO) for ideal placement and estimated DPG-based SG sources for different DN systems have been introduced, including the most suitable transports for DPG establishment presented using LSF. Creating an astute framework to encourage the use of wind energy is an ideal task for ESS and DPGs [42]. The stated objective is to limit the annualized investment costs, the standard benefits, and the variable costs in the two periods of energy planning. Ideal zones and assessment of hybrid systems smoothing out computation have been developed [43]. The minimal PL, voltage steadiness of the system, and sizes of PV and limit are the huge targets obtained through logical Crow Search Optimization (CSO) estimation. An ideal ESS and DPG assignment to help blend wind energy has been introduced [44]. The IEEE 15-bus test framework affirms the proposed model because of the distinct feature and the disproportionate cost of the dual phases. ADNs with Multi-Objective Particle Swarm Optimization (MOPSO) were created by employing the method for soliciting tendency by similarity to an ideal organization system in the presence of non-linear burdens [45].

3.3. Security Studies with DPG-ESS

The two-level arrangement, including short-term and long-term planning, has been demonstrated [46]. Its target capacity is to limit the yearly operational cost of the network, subject to organized security needs. The proposed issue is created using blended non-linear programming and is addressed by an adjusted PSO calculation. In order to adapt to natural conditions, the case incorporates the responsive force of diesel DPGs. The ideal location of ESS in DN to limit voltage deviations, line burden, and PL has been determined [47]. An IEEE-33 medium-voltage bus system inspects the proper organization of dispersed ESS. The Artificial Bee Colony (ABC) method is deployed to tune the boundaries of the objective capacity. It has been solved using a Python platform that computerizes reproduction occasions in a power factory. Jointly improving the limits and areas of decentralized creation units and the battery energy stockpiling system has been performed [48]. The perplexing issue of two-fold improvement is tackled imaginatively in two successive advances. An iterative method beguiled by optimizing ESS capacity and location presents an upper and lower cut-off of the rough issue’s ideal cost [49]. A proposition for an ALO algorithm for the perfect task and a valued DPG-based renewable for the radial DN has been developed [50]. To begin with, the most reasonable buses for the DPG establishment are recommended as user-defined factors. The proposed system controls the DPG positions and sizes of the selected buses. An ideal limit for the DPGs introduced to disregard blockage on the mass framework’s transmission lines has been determined [51]. The Flower Pollination (FP) algorithm has been incorporated to achieve the best limits at solidarity levels and 0.9 power factors. The ideal Active Power Filter (APF) evaluation has been calculated. A current injection technique has been employed to find feasible buses for the APF placement in the presence of non-linear loads and DPG. Wolf Optimizer (WO) has been used to distinguish its ideal size [52]. A hybrid ESS has been used to study the assessment of the localization limit price of the decentralized production units [53]. It would enhance the reliability of the radial DN. It comprises an ideal method dependent on the hereditary dragonfly calculation, which imputes every DPG unit depending on its commitment to improving dependability. In this article, “usual undelivered energy” has been used as a proportion of “unwavering quality.” This method has advantages for distribution companies, enabling them to manage their networks more reliably by providing suitable incentives to DPG owners. By offering proper incentives to the possessors of the DPG, this method allows the Distribution Company (DISCO) to manage the network more reliably. The area, determination, and ideal activity of ESS batteries and capacitor banks in DN have been analyzed [54]. In order to solve the problem, a non-linear blended number programming model is proposed. Through proper planning, the model aims to reduce PL in their DN.
The following are the problems developed by DN in the security plans of electricity systems:
  • Defect Levels-The distribution networks in urban areas are made achievable with the highest short-circuit level. It helps keeping the consumer voltage as close as feasible to the nominal level while reducing one consumer’s impact on another. Due to economic considerations, distribution transformers, circuit breakers, and cables must be rated as close as feasible to their maximum load. The installation of embedded generation could increase the short circuit level above what the plant can tolerate because there is so little space between operation and rating.
  • Variations in Voltage-Since radial circuit distribution involves supplying some dispersed clients. It is essential from an economic standpoint that they taper with time. A long rural connection with embedded generation at the end will likely raise the local voltage above the permitted limits.
  • Network Security: The planning requirements for embedded generation network security connections maintain the pre-connection level of supply security. It adversely affects the size and type of the embedded generator. It is possible that the local system runs in island mode and is powered by the embedded generator in fault scenarios, where the SG’s supply is disrupted. Security is improved in this instance via embedded generation.
  • Network Resilience: When a defect occurs, the system dynamics can obtain excited, and it is feasible that an embedded generator’s properties are such that the resulting oscillations could trip the local network. Before connecting, a stability investigation is performed using known generator dynamics, and if instabilities originate, stabilizing networks are created using control systems theory.

4. Result and Discussion

Furthermore, this work has searched for the multi-objective (MO) functions for cost reduction in terms of PL in the SG network through optimal placement of DPG-ESS, and the comparative analysis of different MO functions with the single-objective (SO) function is shown in Figure 7a–c. MO1, MO2, MO3, MO4, MO5, MO6, and MO7 represent different MO functions where a few SG network parameters, such as PL, voltage variation, and ESS placement and charging, are optimized.
In Figure 7a, this work investigated different OBJs’ comparative cost reduction analysis. It has been observed that SO and MO1 with one variable for cost optimization in the objective function perform well in minimizing installation and operation loss. Similarly, after drawing Figure 7b, this paper has observed that with minimum cost, good voltage stability is achieved, while in Figure 7c, natural and reactive PL for different OBJs’ are analyzed, and it is observed that SO and MO1 outclass all the remaining objective functions in a state of PL minimization.
The survey revealed several different algorithms and DNs. Additionally, it looked at the ESS capacity, DPG, ideal site, size, and constraints of the SG network. This study discovered that while some methods were costly and ineffective, others had minimum PL and voltage stability. DN ESS capacity makes use of numerous ideal spots and dimensions. ESS capacity and DPG require improvement. Figure 8a compares the DPG frequency optimization algorithms and ESS size and locations in DN. It compares four algorithms: PSO, ALO, GA, and others. The following related papers deploy PSO, ALO, and GA, and only one article comes under another algorithm.
From Table 2, it is observed that the objective type for many related articles belongs to MO. An almost equal number of searches had been performed on the SO-MO algorithms. Algorithms such as clustering and sensitivity, PSO, Graph Theory, ALO [55], Teacher-Learner-Based Optimization, Column-and-Constraint Generation, CSO, ABC Optimization, Iterative, FP, Hybrid Tangent-Gold FP [56], WO, and Genetic-Dragonfly algorithms are used to achieve SO-MO.
In Figure 8a,b, respective algorithms and objective functions are depicted carefully. The different MO functions are examined. This result proved that investment costs for many research works, annual net profit, and PL for additional research are involved. The literature on PL is more significant than that on investment costs.
Furthermore, the number of publications discussing investment cost outnumbers those discussing PL and operational cost. So, in short, the sequence of the number of literature based on objectives is presented as “Number of literature of PL > Investment cost > PL = Operational cost > Annual net profit > Others.”

Utilization of Soft Computing Methodologies for Strategic Planning in DN

One of the optimal problem-solving and decision-making methods is the heuristic method. With the aid of metaheuristics, the system offers a speedy resolution. DPG planning models use different mathematical attributes to improve the physical model’s efficiency, complexity, and reliability. An AI technique known as PSO can be used to approximatively solve numerical maximizing and minimization problems that are highly challenging or unsolvable. The PSO process flow begins with the initial populations of particles, as shown in Figure 9. The article offers a possible resolution to the problem. After that, the algorithm selects a particle and determines its target function. A confirmation has been received regarding the particle limits. The current particle is discarded, and the next test will be if the requirements are unmet. This is repeated to determine the objective functions of every particle. The algorithm and final solution are selected if the merging situation is acceptable. The AI algorithm iterates as often as necessary until a successful result is obtained [57].
There is a discussion of the objective, the bus system, and the loading conditions. Combined Heat and Power (CHP) improvements that are the best mix of reusable and non-sustainable have been researched using efficient energy predictions. Mini turbines and internal combustion engines are included. The enhanced PSO algorithm under which it operates dramatically improves the service organization’s commercial benefit over the urban landscape in its capacity as CHP owner and administrator. A binary clamorous shark-smell algorithm was simulated for multiyear frequency development with sufficient power flow. Some analytical and nature-inspired techniques are listed for different objectives and test systems, as detailed in Table 3 [57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72].
A hybrid PSO algorithm is helpful in SG optimal planning composed of fuzzy entities [73]. Different Voltage Stability Indices (VSI) have also been rendered with crossover indices [74]. Higher computing adequacy is projected for the outcomes using the IEEE-12 bus, IEEE-69 bus, and Practical TN-84 bus frameworks [75]. Finding better voltage stability to look at its zone globally at a high combination rate made it possible to look into a cycle of optimal DPG placement and reconfigure the DN simultaneously [76]. Smoothing out counts for addressing the plans in investigated and thought-incited issues, and a couple of centers are proposed to increase calculation capacity [77]. An Adaptive Neuro-Fuzzy integrated Salp-swarm Optimization technique obtained the minimal principal volume through organizational operations in power flow by tracking load differences for DPG-ESS [78].
The stochastic zone is essential to the functioning of the multi-target dynamic model [79]. The load transformers, DPGs, and static var Compensators were created using the Uncertain Random framework. Volatile and incoherent diffusion stacks represent the system because the effects of many alleged components on it are not considered. Finding the optimal Pareto region reduces emission rates and disasters [80].
A new method has been used for improved voltage levels under various circumstances [81]. As different objectives come in category-wise, the fuzzy multi-goal feature is helpful. From an optimization point of view, constraints are listed in Table 4a by referring to [82,83,84,85,86]. The overall comparative studies are shown in Table 4b by referring to [85,86,87].

5. Conclusions and Future Work

For optimum integration of Distributed Power Generation (DPG) and Energy Storage Systems (ESS), the planning measures for Smart Grid (SG) modernization is determined by the consumer and market views. A detailed analysis of the performance and results of existing algorithms paved the technique for the design of efficient hybrid Metaheuristic Optimization Algorithms (MOA). At this point, shared ownership is essential to optimally formulate an extension of the multiple allocating and sizing paradigms for DPG and ESS. The MO algorithms are then considered in an early planning stage, valid for deterministic and probabilistic loading conditions. It also shows how to investigate the many challenges related to DN using improvement methods. Regardless of the indisputable production qualities of RE plants, it is necessary to promote further electric civilizations’ needs with each RE plant’s final expansion. Over time, various philosophies have been developed for storing energy generated from DPG. More financial professionals are enthusiastic about these unusual energy sources’ potential to bring technological innovations. When planning the expansion of production with decentralized grid power plants in cooperation with ESS, special restrictions and reliability criteria are taken into account to simultaneously reduce planning costs and environmental pollution. This paper has examined the relationship between ESS uncertainties and DPG variations during the essential planning phases of reviewing the exhaustive analysis.
In future studies, SG’s comprehensive planning would help optimize electric vehicle charging and discharging.

Author Contributions

Validation, Supervision, R.A.; Resources, Writing—Review and Editing, H.M.A.G.; Data Curation, Writing—Review and Editing, P.D.; Writing—Review and Editing, M.A.; Supervision, Funding, Methodology, W.E.-S.; Writing—Review and Editing, M.M.F.; Project Administration, Writing—Review and Editing, M.H.A.; Writing—Original Draft Preparation, Conceptualization, Methodology, D.S.; Writing—Original Draft Preparation, Conceptualization, Methodology, S.S. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

Not applicable.


The authors are very much grateful to all the institutions given in the affiliation list for performing this research work successfully.

Conflicts of Interest

The authors declare no conflict of interest.


ACAlternating Current
ADNActive Distribution Network
ALOAnt-Lion Optimization
AIArtificial Intelligence
APFActive Power Filter
BESSBattery Energy Storage System
CAESSCompressed Air Energy Storage System
CHPCombined Heat and Power
C-UC-CEConsolidated-Unit-Commitment and Capacity Expansion
CSOCrow Search Optimization
DCDirect Current
DEPDistribution Expansion Planning
DPG-ESSDistributed Power Generation and Energy Storage Systems
DISCODistribution Company
DNDistribution Network
EHEnergy Hub
ESSEnergy Storage Systems
EVElectric Vehicle
FAFault Analysis
FESSFlywheel Energy Storage System
FORForged Outage Rate
FPFlower Pollination
GAGenetic Algorithm
GEPGeneration Expansion Planning
HEPHigh emission Plants
HOAHybrid Optimization Algorithm
HVHigh Voltage
LEPLow emission Plants
LSFLoss Sensitivity Factor
LVLow Voltage
MILPMixed Integer Linear Programming
MOAMetaheuristic Optimization
MOPSOMulti-Objective Particle Swarm Optimization
OPFOptimal Power Flow
PGPower Generation
PSPower Supply
PSCIPower Supply Capacity Index
PSOParticle Swarm Optimization
PTHSSPumped Type Hydro Energy Storage System
PVPhotovoltaic unit
RERenewable Energy
SCESSSuper-Capacitor Energy Storage System
SDStorage Devices
SGSmart Grids
SMESSSuperconducting Magnetic Energy Storage System
SOSingle Objective
SPWNSSolar Plants with Non-Storage
SPWSSolar Plants with Storage
VSIVoltage Stability Indices
WOWolf Optimizer
B i j susceptance of power lines
C g s the start-up cost of generator type ‘g’ [USD]
C l , g i n v speculation cost annuity of generator type ‘g’ in year ‘x’ [USD/MW]
C U D unit span cost [USD/MWh]
C x v a r the variable expense of generator type ‘g’ in year ‘x’
C O x operational expense
D(X)project interest in the specified number of hours (h) to prepare for (t) years/time and trimester tri (MWh)
G i j conductance of power lines.
I G l , g extra unit installed in year ‘y’ of generator type
L S x , t load shedding at hour ‘t’ in year ‘x’ [MW]
ntotal number of the bus
N G quantity of generator
Npumpthe course of action of all power plants except for siphoning plants
p s t price per energy from ESS
P t i t the total amount of power injection at node ‘i’
P x , t , g power provided by generator type ‘g’ at hour ‘t’ in year ‘x’ [MW]
Psrp (X)output yield of all unique system
pumparrangement of all pumping power plants.
P c charging power of SD
P d discharging power of SD
Preal power
Qreactive power
q t unit electricity cost at the current time ‘t’
Str(t)charge state rate of SD
srpunique regime producer
V i ( t ) the complex voltage at bus i,
V i k voltage level between bus ‘i’ and ‘k’
Y i k admittance between bus ‘i’ and ‘k’
θ i j phase difference profile between nodes,
η efficiency of SD
δ corresponding discharge rate


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Figure 1. The hierarchical flow of energy storage systems.
Figure 1. The hierarchical flow of energy storage systems.
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Figure 2. Constructional architecture of yybrid RE-DPG with ESS.
Figure 2. Constructional architecture of yybrid RE-DPG with ESS.
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Figure 3. Schematic diagram of a DPG-ESS unit.
Figure 3. Schematic diagram of a DPG-ESS unit.
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Figure 4. Case analysis for GEP.
Figure 4. Case analysis for GEP.
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Figure 5. Advantages rendered by BESS integration on DN.
Figure 5. Advantages rendered by BESS integration on DN.
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Figure 6. Load leveling features in the presence and absence of ESS.
Figure 6. Load leveling features in the presence and absence of ESS.
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Figure 7. (a) The cost is related to different objectives. (b) Cost saving and voltage index for OPF. (c) Reactive and accurate PL profile.
Figure 7. (a) The cost is related to different objectives. (b) Cost saving and voltage index for OPF. (c) Reactive and accurate PL profile.
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Figure 8. (a) Survey related to algorithms involved in DN planning. (b) Survey related to objectives in DN planning.
Figure 8. (a) Survey related to algorithms involved in DN planning. (b) Survey related to objectives in DN planning.
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Figure 9. Flowchart for PSO in DN.
Figure 9. Flowchart for PSO in DN.
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Table 1. Energy exchange between an SG, ESS, and load.
Table 1. Energy exchange between an SG, ESS, and load.
Net Energy
Exchange (MWh)
SG Power
50No action330360
60No action330360
70No action330360
80No action330360
Table 2. Objective analysis of this review.
Table 2. Objective analysis of this review.
Proposed WorkAlgorithmObjectivesOBJ Type
An Optimal Planning of Battery ESS [36]clustering and sensitivityannual net profit of BESSMO
Optimal Placement and Sizing of ESS [40]teacher-learner-based optimizationLow-operational costMO
BESS location [41]GALow-costMO
Optimal Distributed ESS [42]Column-and-Constraint Generation (C&CG)Less investment and operation costsMO
ADNs [45]PSOminimizing operational and investment costsMO
Two-level Planning for Co-ordination of ESS [46]PSOminimize annual operation costMO
Optimal Placement of Distribution ESSs [47]ABC Optimizationvoltage deviations and PL are lessMO
DPG-based RE Sources [50]ALOminimal PL and consequently maximizing the net savingMO
Optimal Capacities of DPG Units [51]FPless PL to obtain the optimal capacities of DPG unitsMO
Optimal DPG Planning with Integration of ESS [55]ALOPL, investment benefit, voltage stability factorMO
Table 3. Comparison of methods for DN planning available in the literature.
Table 3. Comparison of methods for DN planning available in the literature.
ArticleDPG ESSObjectives IEEE Bus Test System Loading Conditions
Placement SizingPlacementSizingMinimization Enhancement
CostLossVoltage ProfileStability
[63]Yes----YesYes-33, 69 Deterministic
[64]Yes----YesYes-38, 69Deterministic
[66]YesYes---Yes--33, 69Deterministic
[67]YesYes---YesYes-12, 34, 69Deterministic
[68]YesYes---Yes--69, 123Deterministic
[69]YesYes---Yes--69, 119Deterministic
[71]--YesYesYesYesYes-6, 70Probabilistic
Table 4. (a) Considerations of constraints for optimization modeling. (b) Comparative analysis for DN planning.
Table 4. (a) Considerations of constraints for optimization modeling. (b) Comparative analysis for DN planning.
( a)
ArticlePower Balance EquationsVoltage LimitsDPG
Operating Limits
Radial NatureLine
Capacity Ranges
ESS Charge Rate Limit
MethodsMeritsDemeritsMajor Applicability
Numerical [85]Non-iterative in natureInaccurate Deterministic Model
No convergence problemHard to get a generalized solutionSingle-Objective (SO) Problem
Easy to useHigh-level simplification Small DN
Derivative-freePremature convergenceMO
Few iterationsA local trap of solutionDynamic Models
Soft Computing [86]
Accuracy in solutionsNo commercial solver at easeMedium DN
Efficient computation Slower convergenceDeterministic model
Effective for complex problemsNon-robust in NatureLarge DN
MO-Soft Computing [87]Faster convergence Massive training dataMO
High accuracy in the solutionFinding global optima needs subsequent computationDynamic models
Greater robustnessNo commercial solver at easeMedium DN
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Abdulkader, R.; Ghanimi, H.M.A.; Dadheech, P.; Alharbi, M.; El-Shafai, W.; Fouda, M.M.; Aly, M.H.; Swaminathan, D.; Sengan, S. Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning. Energies 2023, 16, 2655.

AMA Style

Abdulkader R, Ghanimi HMA, Dadheech P, Alharbi M, El-Shafai W, Fouda MM, Aly MH, Swaminathan D, Sengan S. Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning. Energies. 2023; 16(6):2655.

Chicago/Turabian Style

Abdulkader, Rasheed, Hayder M. A. Ghanimi, Pankaj Dadheech, Meshal Alharbi, Walid El-Shafai, Mostafa M. Fouda, Moustafa H. Aly, Dhivya Swaminathan, and Sudhakar Sengan. 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning" Energies 16, no. 6: 2655.

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