An Extensive Assessment of the Energy Management and Design of

Battery Energy Storage in Renewable Energy Systems

A. K. ONAOLAPO, B. T. ABE

Electrical Engineering Department,

Tshwane University of Technology,

eMalahleni Campus,

SOUTH AFRICA

Abstract: - Many benefits are derivable when renewable energy systems (RES) are integrated with battery energy

storage systems (BESS). However, appropriate energy management techniques should be adopted to realize

optimal benefits. Many BESS operations’ optimization approaches are available in RES with various techno-

economic, environmental, and dispatch-related outputs. BESS operations are optimized using different methods.

Past studies have mainly concentrated on certain renewable energy systems designed for specific purposes, such as

distributed generation or large-scale. This paper thoroughly examines and analyzes various battery management

systems by considering the relationship between the optimization methodology and the intended application. This

strategy enables the identification of connections between favored optimization approaches and specific

optimization goals. Some approaches are more effective in solving economic goal optimizations, whereas others

are commonly used for technical goal optimizations. The selection of the solution methodology is also

demonstrated to be highly contingent upon the degree of mathematical formulation of the problem. An analysis is

conducted to assess the strengths and limitations of the described optimization techniques. The conclusion is that

hybrid approaches, which combine the benefits of multiple techniques, will significantly impact the creation of

future operating strategies. This paper provides a comprehensive analysis of optimization approaches and battery

applications, aiming to assist researchers in efficiently identifying appropriate optimization strategies for emerging

applications in the new generation.

Key-Words: - Battery energy management, Control approaches, Hybrid goals, Optimization algorithms, Renewable

energy systems, Smart Grid, Technical goals, Economic goals.

Received: April 9, 2023. Revised: February 11, 2024. Accepted: April 14, 2024. Published: May 9, 2024.

1 Introduction

In modern power systems, battery energy storage

systems (BESS) are crucial due to their contribution

to smart grid development, provision of technical

support to power systems, and effectively tackling

the problems of renewable energy intermittency, [1].

Researchers have examined BESS in various ways to

improve its integration into renewable energy

systems (RES). These systems include distributed

renewable systems, renewable energy power plants,

microgrids, and hybrid renewable energy systems

(HRES). Other significant uses of battery storage in

power systems that have gained attention include

frequency and voltage management, transmission

network expansions and improvements, and

alleviating congestion in transmission networks, [2].

Although batteries have been recognized as a

highly efficient solution for dealing with the sporadic

nature of renewable energy, the significant upfront

expense of BESS continues to hinder their broad

adoption, [3].

Another important consideration is the duration

for which the battery remains functional, making

maximizing its usage throughout its lifespan a crucial

worry for most applications. Some authors have

extensively discussed the search for the most

efficient battery size in a recent article, [4]. This

evaluation complements the sizing assessment by

examining devices with a fixed battery capacity. An

essential factor is that the implementation of the

BESS (i.e. which specific goals have been given

priority) significantly influences the intended

functioning of the BESS, indicating that the choice of

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the BESS's application goal is crucial. Hence, this

study scrutinizes a substantial body of research on

battery optimization, explicitly emphasizing the

anticipated functionalities that batteries must fulfill

and exploring the most effective methods for battery

management.

Many reviews and research on energy storage

systems (ESSs) have been published in recent years,

with a specific emphasis on various facets. Several of

these sources include comprehensive explanations of

BESS technology for energy storage regulations [5],

battery cost modeling [6], life cycle cost analysis [7],

battery management systems [8], and large-scale

applications [9], [10]. Prior analyses on battery

optimization focused on individual renewable energy

systems, where battery storage played a crucial role.

These include distributed energy systems,

microgrids, and large-scale wind power plants or

solar energy. A thorough examination was conducted

on several ESSs to improve wind power utilization.

This includes addressing oscillation damping, voltage

regulation, and fluctuation suppression, [11]. Authors

in [12], similarly examined the use of ESSs for

integrating wind power. It not only discusses the

appropriate ESS technology but also covers ESS

control, operation, and design, specifically for wind

power facilities. Additionally, some studies focus on

specific services, such as managing power output

smoothing in solar PV and wind power plants, [13],

and using batteries for frequency regulation in

contemporary power systems, [14]. These articles,

which concentrate on specific themes, provide the

benefit of delivering a more precise overview within

a narrower scope. Nevertheless, it cannot offer a

comprehensive perspective on BESS's extensive

array of uses. It is important to note that studies

specifically focus on battery management systems,

[15]. These reviews aim to enhance the management

and control of battery cells at a more fundamental

level, which is not the main emphasis of this study.

Furthermore, the evaluations have encompassed

large-scale applications and applications inside

distributed energy systems. Authors in [16], provide

a comprehensive analysis of the use of BESS in

home Photovoltaic (PV) systems. The evaluation

emphasizes the economic feasibility of using BESS

in this application.

Furthermore, several studies have provided a

concise overview of the applications of ESS in

distributed PV generation, explicitly highlighting the

significance of BESS technologies, as well as

optimization strategies, [17], [18]. In addition, hybrid

energy storage systems (HESS), namely the

integration of super-capacitor and BESS, have

garnered considerable interest due to their mutually

beneficial characteristics. Extensive research has

been conducted on utilizing HESS in microgrids,

[19], [20], [21]. A further evaluation is conducted on

HESS and its suitability for use in smart grid systems

and other applications involving electric vehicles

(EVs), [22]. Additionally, more recent articles

specifically concentrate on EV batteries and the

enhancement of virtual power plants (VPPs), [23].

While this analysis includes specific approaches and

optimization goals for efficiently running VPPs and

HESS, it does not primarily focus on the particular

factors related to the operation of EV batteries, VPPs,

and HESS. A prevalent characteristic in the studies

above is their concentration on a particular energy

system or a specific utilization of BESS or ESS.

Nevertheless, there are still inquiries regarding the

primary goal of integrating a battery into RESs and

the rationale for selecting a particular approach or

model to enhance the battery's performance. This

study seeks to address these inquiries by compiling

the operational goals and methodologies of battery

research. This research highlights the primary

relationships between the BESS modeling methods,

specific optimization goals, and the appropriate

approaches to solve the problems. The choice of the

BESS modeling approach is dependent on its

intended purpose and application complexity. Also,

the integration of battery degradation modeling is

related to its application objectives and operation

duration. Another essential portion considered in this

assessment is the connection between the

recommended approaches and the selected

optimization goals for BESS optimization. Particular

optimization approaches are suitable for solving

specific problems. In addition, analysis of BEM

strategies and goals is essential for pattern

identifications of BESS application goals and

optimization methods, enabling researchers to

examine fundamental concepts of running BESS in

different RESs. This includes the analysis of BESS

methodologies, operational goals, and modeling.

These principles, derived from a comprehensive

literature review, can serve as a reference for future

implementations of BESS in any RES.

Furthermore, new papers specifically concentrate

on the broader uses of BESS. A comprehensive

analysis was carried out to examine the utilization of

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adaptable ESSs for integrating renewable energy

(RE). The analysis categorized the different types of

energy storage technologies, including gas, biomass,

magnetic, compressed air, pumped, and batteries,

[24], [25]. This configuration facilitates the

comparison of various technologies but hinders the

solutions in ESS applications and the identification of

shared objectives. A compelling synopsis of the

integration of BESS was offered, employing

bibliometric analysis. Examining data using a survey-

based method may be challenging in uncovering the

underlying insights, but it provides valuable

information. The study in [26], comprehensively

summarized technologies integrating RE to support

the power system. However, the crucial solution

strategies for battery optimization were not

mentioned. This study provides a comprehensive

overview of various applications of BESS. The

analysis is organized based on the methodologies

employed to solve optimization challenges, the

objectives of the applications, and the modeling

methods. Furthermore, this analysis provides a

concise discussion of the fundamental connections

between the current developments in battery energy

management, the BESS optimization approaches, and

aims. This review focuses on the RE systems'

operations of ESS and BESS with specific capacities.

This study contributes to the body of knowledge

in the following ways:

 Analyse crucial optimization techniques and

algorithms to demonstrate their strengths in

BEM design.

 Evaluate optimization strategies to identify

appropriate, efficient approaches for different

battery applications.

 An in-depth assessment of the optimization

methodology used and the intended

application was conducted in this review to

identify the connections between favored

optimization approaches and specific

optimization goals.

The demonstration of the selection of solution

methodology is highly contingent upon the degree of

mathematical formulation of the problems.

2 The BESS Operation Design

Various BESS operations aim to encompass battery

power management and optimization for optimal

economic outcomes, adherence to a reference target,

and battery voltage and current control to ensure the

stability of the RES's output. Additionally, the time

frame for the battery's control and management goal

window might vary from milliseconds to hours.

Hence, it is crucial to distinguish between various

techniques employed to manage and regulate

batteries. A three-tier control hierarchy, commonly

incorporating battery storage, has gained widespread

utilization and acceptance in microgrid research,

[27]. The three-layer control architecture consists of

tertiary control at the top layer, prioritizing

maximizing the microgrid's economic outcome. The

secondary control acts as an intermediary between

the primary and tertiary control layers, while the

primary control, at the lowest layer, is primarily

concerned with the fundamental control of

converters. This paper presents a comparable notion

built on a three-layer control hierarchy for a

microgrid. Figure 1 illustrates the three-layer control

architecture utilized for battery control and

management.

The primary goals of each layer are depicted with

solid lines, while dotted lines indicate the

information and power flows. A BESS converter

controller's primary function is to govern the power

transfer from AC to DC and DC to AC while

charging and discharging, respectively, similar to the

fundamental control in a microgrid. The controller

operates with a temporal precision ranging from

milliseconds to seconds. Simultaneously, the

controller also aims to track the provided reference

from secondary control to uphold the RES's stability.

The secondary control, which operates at a temporal

resolution ranging from seconds to minutes, is

responsible for enhancing the dynamic properties of

the HRES, similar to the secondary control in a

microgrid. When a disturbance, such as a fluctuation

in RE, occurs in the system, the system controller

will work to prevent any changes in frequency and

voltage through primary control. This ensures that

the power quality of the renewable energy system is

maintained.

Furthermore, BESS will be controlled in an ideal

manner to track any reference provided by tertiary

control. Tertiary control, which operates at a time

resolution of minutes to hours, involves the energy

management center acting as a steady-state system

optimizer to determine the best operational strategy

for the RES.

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Fig. 1: BESS and the control hierarchy

Both economic and steady-state technical criteria

can be utilized to optimize the performance of the

RES. Energy management activities may be

accomplished using practical dimensions, including

arbitrage, peak-shaving, reducing operational costs,

optimizing overall profits, and more. Please be aware

that this evaluation specifically examines the

anticipated functionalities of batteries in a RES and

the methodologies employed to accomplish these

functionalities. The functions often examine

durations ranging from minutes to hours or the entire

duration of the project. These timings are categorized

as secondary and tertiary controls. Thus, this review

provides a concise overview of the uses of BESS,

with particular emphasis on research about BESS

functioning for secondary and tertiary control

purposes.

3 Battery Energy Management and

Modeling

A BESS, composed of battery cells, is interconnected

in series and parallel arrangements with converters to

enable charging and discharging operations. Various

battery technologies, including redox flow batteries,

lithium-ion batteries, NaS batteries, and lead-acid

batteries, [28], show promise for usage in grid or

renewable energy systems (RES). This review does

not thoroughly analyze the effects of materials-

physics models on battery energy management

systems (BEMS). However, it is essential to

acknowledge that the physics of various battery

technologies will affect the operational approaches

and results of battery storage, particularly in terms of

degradation profile, round-trip efficiency, and state

of charge (SoC) restrictions, among others, [29].

BESS modeling uses mathematical formulae to

illustrate the behaviors of the batteries. Modeling the

Battery Energy Storage System (BESS) is crucial to

controlling and managing BESS. BESS models were

built at varied levels of sophistication and detail to

cater to various BESS management needs. Using

simplistic models for overall energy management

issues and employing more precise models for

intricate control challenges is a rational approach; for

instance, in research aimed at comparing the results

of utilizing various battery technologies, a

fundamental and universal battery model was

employed, with distinct characteristics assigned to

each battery type, [30].

Regarding simulation settings and modeling

methodologies, BESS models may be categorized

into fundamental and dynamic models, including

analogous circuit models. The fundamental model is

commonly used for modeling energy management in

steady-state conditions at minute/hour resolution.

Dynamic models offer extra benefits when

performing transient state dynamic control

simulations. This section also reviews the modeling

of BESS for battery deterioration, which is another

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critical component of BESS functioning. The primary

emphasis of this part is on using BESS modeling to

optimize battery energy in RES. The main goals of

battery energy optimization are technical

advancement and improved economic outcomes for

RESs. This view is different from battery

management, which aims to optimize the life cycle

and particular battery performance, such as the

research on temperature management, SoC

forecasting, fractional order models, [31] and so on.

The outcomes of battery management research can

serve as inputs for battery energy optimization. These

inputs could include the degradation profile,

resistance-capacitance model parameters, SoC’s

upper and lower limits, and round-trip efficiency.

3.1 Battery Fundamental Models

More details on the generally used BEM models, i.e.,

the fundamental models that track BESS SoC

variations due to the battery’s charging and

discharging operations. The SoC is a standard index

for measuring a battery’s energy level. SoCs range

from 0% to 100%, with 0% representing a discharged

battery and 100% representing a fully charged

battery. The quantity of stored charge concerning the

total capacity of a battery is referred to as its

SoC. Suppose we assume that the voltage of the

battery remains constant. In that case, the SoC may

be defined as the amount of energy stored in the

battery compared to its total energy capacity.

Moreover, the variations in battery condition

over specific time intervals may be characterized as a

time series comprising discrete values of SoC. An

increase in SoC signifies the batteries are being

charged, while a decrease in SoC means they are

being discharged. The mathematical representation of

this technique, which considers the efficiencies of

discharging and charging, denoted as ηd and ηc, is

summarized by equations (1) and (2), respectively,

[32].

     

,

arg

BESS

d BESS

P t t

SoC t t SoC t EC

whendisch ing





   

(1)

     

,

arg

BESS c

BESS

P t t

SoC t t SoC t EC

whench ing





   

(2)

where PBESS represents the power at which the

BESS is either discharging or charging. A positive

value of PBESS indicates that the battery is charging,

while a negative value indicates that the battery is

draining. This occurs over a specific period, Δt.

ECBESS stands for Energy Capacity of BESS. The

fundamental approach is often applied since it

focuses on BESS applications without specifying the

specific BESS/ESS technology type. This allows for

examining the impact of energy storage properties on

the entire system and identifying the best suitable

technologies for the specific application based on the

revealed required properties using the fundamental

model. To streamline the intricacy, several research

examined in this work assume that ηc and ηd are

constant variables. The values are contingent upon

the battery technology and operating parameters like

temperature, voltage, and current. Therefore,

alternative research has employed more intricate

methodologies to calculate these efficiencies, such as

employing curve-fitting techniques for estimation or

utilizing parameters derived from empirical findings,

[33].

3.2 Battery Dynamic Model

While the fundamental model effectively elucidates

the correlation between the power of charging and

discharging and the SoC of the battery, it

presupposes that alterations in SoC resulting from an

excess or shortfall of energy are consistently

attainable. It is presumed that any current or voltage

alteration or magnitude may be attained to

accommodate the anticipated variation in SoC.

Dynamic models can be used to accurately represent

the voltage and current characteristics, including

transients of the BESS when controlling them is

crucial, [34]. Equivalent circuits are a frequently

employed dynamic model technique. Various

dynamic models are often employed in the literature,

including simple and first- and second-order models.

Figure 2 displays a basic dynamic model for

batteries. The system comprises a voltage source

equal to the open-circuit voltage VOC and an internal

battery resistance R0, linked in series.

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Fig. 2: The battery’s basic dynamic model

The governing equation describes the battery’s

basic dynamic model, Eq. (3), which is advantageous

when the transitory characteristic of the system may

be disregarded.

minter al oc o

v v iR

(3)

To capture transient effects, first and second-

order dynamic models contain resistor-capacitor

(RC) networks, [35], as illustrated in Figure 3.

Applying Kirchhoff's rules to the circuit enables the

construction of a system of differential equations that

describes the time-dependent behavior of voltage.

Fig. 3: The battery’s second-order model

Utilizing battery models of second-order or first-

equivalent circuits enables users to comprehend the

intricate dynamic process of battery functioning via

differential equations’ solutions. State-space models

are a commonly employed dynamic method in which

differential equations are condensed into a concise

matrix equation by selecting an appropriate state

variable to determine solutions. The broad

availability of commercial software that rapidly

simulates very adaptive and complicated circuits,

such as PSCAD and MATLAB, has made these

techniques popular, [36], [37].

3.3 Models for the Deterioration of Batteries

The deterioration of battery performance is mainly

attributed to chemical processes in the electrolyte,

cathode, and anode, resulting in changes over time,

[35], [38]. Battery deterioration encompasses both

cycle and calendar deterioration. Calendar

deterioration refers to the natural deterioration of a

battery over time, regardless of whether it is used or

not. The SoC and the temperature of the battery

influence the pace of deterioration. Conversely,

cycling deterioration occurs whenever the battery is

charged and drained and is influenced by the extent

of discharge, the average SoC of each cycle, and the

average temperature of the cell. Regular cycles of

discharging and charging, as well as deeper cycles

(often defined as discharging below 20% of SoC,

which may vary depending on the battery type), may

decrease the battery's lifespan, particularly for

lithium-ion and lead-acid batteries. One significant

consequence of battery deterioration on system

modeling is the reduction in storage capacity,

referred to as capacity fade, [39]. In the battery

manufacturing sector, it is commonly acknowledged

that a battery, such as a lead acid or lithium-ion

battery, has reached the end of its useful lifespan and

should be replaced when its usable capacity falls

below 80% of its initial value. It is crucial to include

deterioration in the energy management process

when dealing with long-term projects, particularly

during long-term simulations. A measure different

from SoC, which is valuable for evaluating battery

deterioration, is the battery's state of health (SoH),

[40]. The SoH is often determined based on the

reduction in the rated capability, and it is expressed

as [35]:

100%,

act EOL act EOL

nom EOL

CC

SoH C C

CC



  



(4)

08

EOL nom

CC

(5)

in which it is observed that when the battery is in a

pristine condition, the true battery capacity, denoted

as Cact, is equivalent to the nominal capacity, denoted

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as Cnom, (Cact = Cnom), resulting in a SoH of 100%.

The SoH is 0% when the capacity of Cact has

achieved its end-of-life (CEOL) capacity, shown by

Cact = CEOL. Subsequently, the capacity losses will be

assessed by utilizing experimental data that

quantifies the influence of analogous cycles on the

reduction in capacity. As previously mentioned, the

point at which 80% of the initial capacity of a fresh

battery is reached is often regarded as the end of its

lifespan, as indicated by the equation (5). On the

other hand, the operational battery capacity can

indicate deterioration, which is the discrepancy

between the loss of capacity and the nominal

capacity of a fresh battery. As previously mentioned,

the battery's deterioration process involves cycle and

calendar deterioration, with the former being the

primary factor in battery deterioration and more

challenging to evaluate. Consequently, several

methodologies have been devised to assess the

deterioration of BESS caused by cycling. The

machine learning technique, [41], is a method that is

regularly employed for counting cycles over a certain

period. This methodology is frequently utilized in

power electronic systems. By employing this

technique, the SoC profiles may be transformed into

cycles of equal value.

Furthermore, Wiener process models are

employed to assess battery deterioration. Research

with similar objectives has been carried out to

evaluate these models for optimizing the BESS in a

microgrid linked to the primary power grid, [42].

Additionally, several expenses associated with the

decline in battery performance have been employed,

including wear, usage, depreciation, and lifespan

costs, [43], [44]. Other research concentrates on

reducing battery deterioration by preventing

overcharging and deep discharging, [45]. The

primary objective is to minimize battery deterioration

by implementing optimized charge and discharge

methods, which drives the advancement of BEMS.

Another approach that is gaining more attention is

the utilization of HESS, which involves the

integration of ultracapacitors (UCs) to prolong the

battery's lifespan by handling high-frequency events,

[46].

4 The Goals of BEMS

The BESSs are crucial in the functioning of RESs,

providing many benefits such as regulating grid

frequency, smoothing power output, enabling peak

shaving, and improving overall system profitability.

The capabilities of the available BESS and the

operational demands of the RESs heavily influence

the anticipated roles of the BESS in a particular

system. This section provides a comprehensive

assessment of research focused on BEMS, explicitly

targeting the management of BESS regarding its

techno-economic and hybrid goals.

4.1 Technical Goals

The BESS has been adopted to enhance the technical

performance of RESs, optimize RE outputs, and

reduce distribution networks' power loss. Table 1 in

Appendix outlines the relevant literature on BEMS

for accomplishing the technical goals. Generally, the

applications executing technical goals focus on

secondary and tertiary controls. These two control

levels are based on the BESS operating architecture.

Regarding BESS management, these applications'

aims may be categorized into three main areas: i)

improving overall performance, ii) enhancing power

profile, and iii) optimizing energy usage.

The optimization of tertiary control goals, such

as battery scheduling for the daily or hourly

performance of RESs, is mainly accomplished

through long-term horizon optimization. Energy

optimization may be classified as a tertiary control

target, with the accumulated energy measured in

MWh or kWh units as the primary indicator. For

instance, the dispatched battery in [60], was to reduce

the loss of power in the distribution lines, and in [61],

was to reduce the amount of energy consumed by the

utility. Line loss refers to the total amount of reactive

and actual power lost in a distribution or transmission

power network over a specific period. This indication

is crucial for demonstrating the effectiveness of

network operations. Indicators based on accumulated

energy diverge from financial goals as they do not

incorporate power pricing. In addition to stored

energy, maintaining energy balance is a crucial

objective for BESS in RESs. For instance, in [62],

the battery balances power demand and supply inside

a microgrid.

Another crucial category of technical indicators

in the optimization process is the characteristics of

the power profile, measured in megawatts (MW) or

kilowatts (kW). These qualities encompass

mitigation of variations, accurate monitoring of

desired task execution, and peak load reduction, [63].

Another widely employed purpose of utilizing the

BESS is to control and monitor the intended

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allocation of resources. The forecasts frequently

define the desired dispatch. In these applications, the

BESS is used to adhere to the power target, minimize

discrepancies between the intended and actual wind

power generation, or mitigate forecasting mistakes,

[64].

Furthermore, several studies are dedicated to

utilizing the BESS to mitigate the intermittent

behavior of renewable energy-producing systems

caused by the unpredictable nature of the resources.

These studies primarily focus on solar PV and wind

farm variability, [65]. In addition to the technical

goals that have longer timeframes in tertiary control

layers, such as daily or hourly resolutions, some

significant technical measures, such as voltage and

frequency regulation, will be included in the scope of

secondary control. In traditional power systems,

thermal generators in the network or the spinning

reserves are activated to regulate the frequency. At

the same time, other technologies, such as static var

compensation (SVC), can be employed to regulate

the voltage by compensating for reactive power, [66].

By employing sophisticated power electronic

techniques, the battery may assist both reactive and

active power, enabling it to regulate voltage and

frequency, [67]. Table 1 (Appendix) provides more

comprehensive research on battery energy

management, explicitly addressing technological

objectives.

4.2 Economic Goals

Given the significance of economic performance,

many researchers have used economic factors to

optimize their batteries. In addition, various

indicators are employed as economic goals in battery

optimization, including maximizing the long-term

value generated by the ESSs, maximizing the

operational profits of the system, and minimizing the

overall operational cost, [68], among others. Table 2

(Appendix) provides a comprehensive overview of

several variables related to BEMS, focusing on

economic goals, as summarized from a selection of

literature.

Table 2 (Appendix) shows that the most

commonly used aim among the numeric indicators is

maximizing the operation profits, equivalent to

minimizing the overall operation expenses. However,

several studies have differing definitions of operating

expenses, particularly the components that make up

these costs. As an illustration, the process of reducing

the overall expense of a microgrid involved

considering the cost associated with shutting down or

starting up the power sources inside the microgrid,

the cost of power exchange between the utility and

the microgrid, and the cost of fuel for diesel

generators. Thus, Table 2 (Appendix) illustrates each

research's optimization goals, relying on the specific

components of the established indicators. The precise

components within the objectives can be identified in

this matter. These studies consider several factors

relevant to battery applications, resulting in varied

approaches. One possible cause is the variation in the

components used in the simulated system. For

instance, including conventional generators in the

microgrid might significantly impact the overall cost

structure. The microgrid analyzed in [69],

incorporated fuel cells and micro-turbines, taking

into account the expenses related to fuel, as well as

the costs associated with starting up and shutting

down the units.

Conversely, the microgrid examined in [70], only

relied on photovoltaic (PV) power generation,

focusing on the expenses incurred due to battery

deterioration and the financial gains from the

electricity market. Another consideration is that

researchers select specific indicators in their studies

due to various systems with distinct operational

procedures. An instance of this is if the functioning

of hybrid systems includes involvement in the energy

market since this will directly determine whether the

inclusion of profit/cost trading in the electricity

market is necessary. Table 2 (Appendix)

demonstrates that most of the research focused on the

power profits/costs due to the simulated system's

connection to the utility. Nevertheless, in self-

contained HRES, BESS does not consider the profit

or cost associated with power. Including specific

indicators is necessary to reach other specialized

aims for the RES or to meet a predetermined

optimization goal for the BESS. An instance of a

specialized objective is using the BESS to engage in

the regulation and reserve market, [71]. The objective

of including this ambition in the economic goals for

BESS management is to enhance profits by active

involvement in the energy regulation and reserve

market. When considering specific objectives, the

feed-in tariff was included to minimize the overall

cost of delivering the load, [72], as this energy

system requires. In addition, there are additional

specific objectives, such as the expenses associated

with greenhouse gas emissions [73], unserved

demand [74] and renewable curtailment [75]. These

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components become extra profits/costs to be added to

the target due to the specific emphasis on the

associated factors. Furthermore, recent research has

shown a heightened focus on battery deterioration.

These studies incorporate the expense of battery

deterioration as an additional factor in the overall

profit/cost analysis, [76].

4.3 Hybrid Goals

While those above technical and economic goals for

BEMS encompass a significant portion of research in

this area, there is a rising body of recent studies that

concentrate on utilizing the battery to simultaneously

achieve both technical and economic goals, referred

to as hybrid goals. This is logical since the technical

and economic goals are frequently interconnected,

mainly when the enhancements from technical

viewpoints may be measured in terms of economic

values. Hybrid goals, which consist of many

objectives, typically use multi-objective optimization

approaches. Table 3 (Appendix) is a compilation of

publications focused on BEMS using hybrid goals.

A practical approach to implementing hybrid

goals involves combining various objectives using a

set of weights to form a unified optimization

problem. If the weights are equal to the unit costs of

the objectives, the sum of the costs for individual

objectives will equal the overall cost. Table 2

(Appendix) displays several research pieces that

encompass technical and economic goals,

categorized by profits and costs. These extra

technical aspects have been acknowledged as

regularly used to assess profitability. Many studies

encompassing technical and economic goals and

involving a range of profit/cost elements can be

classified as having hybrid goals through economic

aggregation. For instance in [88], the authors used

BESS to conduct research that employed control

approaches to reduce PV curtailment and financial

loss. An alternative approach to accomplish hybrid

objectives is to frame the problem as a multi-

objective optimization.

In contrast to the single-objective problem that

requires finding a single optimal solution, the multi-

objective problem entails identifying a collection of

Pareto optimal options. This implies that the battery

will have the capability to enhance one performance

indication without compromising the others. Pareto

multi-objective solutions are commonly used with

artificial intelligence optimization technologies, such

as genetic particle swarm optimization (PSO) and

genetic algorithms (GA). An example of a multi-

objective problem is minimizing the cost of power

generation and life cycle emissions using HRES and

BESS, [89]. An additional comparable instance of

employing Pareto optimum operation involves

balancing economic gains by minimizing CO2

emissions and operation costs, [90].

Furthermore, it is noteworthy that several studies

have used artificial intelligence approaches directly

without relying on Pareto multi-objective

optimization solutions, [91]. The hybrid goals can

also be combined through multi-stage optimization,

where a single target is a primary outcome at each

step. A two-layer optimization methodology was

employed in the given scenario, as described in [92].

The top layer focused on minimizing the operational

cost of the BESS, while the bottom layer aimed to

minimize power variations and forecast uncertainty.

Furthermore, alternative methods can be employed to

regulate the battery for hybrid purposes, including

implementing rule-based control. The hybrid

optimization objectives are addressed in a prioritized

manner. In [93], the BEMS prioritized the efficient

battery utilization. The second priority was

smoothing the residual distribution grid demand, and

the third priority was the peak shaving.

5 Optimization Approaches for the

BESS

After selecting the goals for deploying the BESS, the

subsequent crucial task is determining the method of

controlling the battery to accomplish those goals.

This involves solving the optimization issue,

provided the goals and constraints have been clearly

defined. BESS optimization approaches refer to the

technological solutions that address BESS

optimization challenges. Choosing an appropriate

methodology to address the issue is a crucial stage.

Some issues may need to be compatible with specific

approaches due to constraints. An optimization issue

needs to be better defined; mathematical solutions

cannot be used.

Further elaboration on this topic will be provided

in the next section. In the optimization problem, the

power profile of the battery over a specific period is

typically included as part of the decision variable(s).

To solve the optimization problem, one must find the

optimal values for the decision variables to achieve

the highest performance for the specified objective

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among the available solutions. This process will

determine the battery storage's discharging and

charging power profile. Various approaches have

been used to solve the optimization problems, from

straightforward rule-based techniques to more

intricate multi-stage optimizations. This section

overviews the strategies used to solve BESS

optimum management problems.

5.1 The Adaptive Model Predictive Control

(AMPC) Method

The adaptive model predictive control (AMPC) is a

control algorithm that regulates parameters based on

real-time feedback to optimize system performance.

The AMPC applies conventional concepts to solve

intricate micro-grid problems and employs

systematic structures in a well-organized manner.

The adaptive controller ensures power

synchronization throughout the network, enabling

efficient power supply production from each

microgrid unit. AMPC provides a solution by

creating an ideal energy storage configuration,

energy consumption, and power generation for each

optimization sample case. The subsequent sample

instance presents a novel optimization solution by

utilizing the result of the previous solution as the new

input. The feedback mechanism theoretically

produces an ideal design that effectively addresses

the microgrid's disturbances. The primary factors

contributing to disruptions and uncertainty in the

micro-grid system are the energy generated by RESs

(affected by variations in solar irradiation and wind

speed) and the energy demand. The traditional model

predictive controller (MPC) cannot handle the

fluctuations in RESs; thus, the advanced model

predictive controller (AMPC) is better suited for this

task. This functions by incorporating updates to the

system based on alterations to its internal working

circumstances. Figure 4 displays the AMPC

algorithm flowchart. The state-space expressions

often employed for AMPC modeling are represented

by [32]:

     

1x t Ax t Bu t  

(6)

   

y t Cx t

(7)

where x(t), u(t), and y(t) represent the charging state

of the BESS, the vector variables of the producing

units, and the output vector of the system's current

condition, respectively.

Fig. 4: Algorithm flowchart for AMPC

5.2 Artificial Bee Colony (ABC) Method

The artificial bee colony (ABC) algorithm is a

nature-inspired computational technique introduced

in 2005, [105]. It is an optimization approach that

emulates the behavior of bees in their quest for food

using mathematical algorithms. The bee colony

comprises three distinct types of bees. The initial

group is referred to as the employed bees (Bem); they

randomly explore the search region to discover

potential nectar locations (potential solutions). Upon

discovering a nectar position (NP), the bees commit

the specifics of this location (nectar quantity) to

memory and communicate the NP information to the

rest of the colony through a dance performed within

the hive. The dance length indicates the level of

nectar quality (fitness value). The second kind of bee

is referred to as the observer bee (Bon). These bees

observe the dance the working bees perform before

selecting a new NP. A wealthy NP garners a more

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significant number of observer bees compared to an

impoverished NP. The third kind of bee is referred to

as scout bees. These bees are worker bees whose nest

sites are discarded due to their low quality after a

specific number of attempts. Observer bees have the

potential to transition into worker bees if they come

upon a novel food source. During this search

procedure, exploitation and exploration co-occur.

The ABC optimization technique assigns an equal

number of spectator bees and employed bees. The

quantity of potential NPs is equivalent to the quantity

of bees now working. Scout bees mostly conduct the

scouting process, consisting of three critical phases in

each iteration: (1) Conduct a search for different NPs

and gather information on the quality of each NP. (2)

Onlooker bees pick an NP based on the information

provided by the employed bees. (3) Employed bees

with inferior NPs are reassigned as scout bees and

sent to explore new NPs.

During the initialization step, a random

distribution of the initial population of solutions xi (i

= 1, 2, . . ., Bem) is formed, where i represents the size

of the population and Bem is the number of hired bees.

Each solution is associated with a dimension,

denoted as Dn, which represents the number of

parameters that need to be optimized. Following

initialization, the solutions' population undergoes

successive cycles (C = 1, 2, . . ., MCN) of the search

process for the three categories of bees, with MCN

being the maximum cycle number. During each

cycle, the hired bees alter the NP by considering the

local information (visible content) and the quantity of

nectar available. When the quantity of nectar at the

new location surpasses that of the prior one, the bee

stores it in memory and disregards the previous

solution. Otherwise, it maintains the previous

position. Once all the industrious bees have

completed the exploration process, they disseminate

the acquired knowledge among the observing bees

within the hive. The observer bees choose a specific

NP by assessing the shared nectar information. The

likelihood of choosing an NP is correlated with the

quantity of honey. The roulette wheel selection

approach allows for the evaluation of the likelihood

of picking a specific NP. The likelihood of choosing

a particular NP is expressed as [105]:

1

em

i

iB

i

fitness

P

fitness





(8)

where fitness represents solution i’s fitness value.

It is worth mentioning that a wealthy NP will

attract a more significant number of observer bees

compared to a less affluent NP. Before the observer

bees choosing another NP, they assess the fitness

value of the position i in comparison to i + 1. This

process continues until all observer bees are

scattered. Should the solution's fitness fail to improve

within a set threshold, the employed bees will

relinquish this solution and transition into scout bees.

The cycle recommences upon selecting a new place

until the ultimate criteria are fulfilled. The ABC

algorithm employs the following methods to

ascertain the neighboring NP about the present NP:

 

 

0,1

ijnew ijold ijold kj

x x rand x x  

(9)

where k

∈

(1, 2, . . ., Bem), k ≠ i, and j

∈

(1, 2, . . .,

Dn). During each cycle, the scout bees generate a

novel solution, provided by:

 

 

min max min

0,1

j j j j

inew i i i

x x rand x x  

(10)

The ABC algorithm is characterized by three

control parameters: the maximum cycle number, the

limit value, and the colony size, [105]. Figure 5

displays the flowchart of the algorithm.

5.3 Backtracking Search Optimization (BSO)

Method

BSO has similarities to various evolutionary

algorithms. The BSO consists of five sequential

phases. The five stages involved in the process are

initialization of the population, selection I of the

population, mutation of the population, crossover of

the population, and selection II of the population,

[106].

(1) Initialization of the population

BSO is insensitive to the beginning value of the

population, allowing for random generation of the

initial population value.

 

,~,

i j j j

Pop U low up

(11)

where 𝑷𝒐𝒑, is the population,

𝑖

∈ [1, 2,…,

𝑁

],

𝑁

represents the total number of elements in the

population.

𝑗

∈ [1, 2,…,], 𝐷 represents the population

dimension. 𝑙𝑜𝑤 and 𝑢𝑝 represent the lower and upper

bounds of the search interval, respectively. 𝑈 refers

to a function that generates values from a uniform

random distribution.

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(2) First (1st) selection of the population

BSO chooses new historical populations, referred to

as OPops, during each evolution generation. The

concept involves randomly choosing one individual

from a preceding population, ensuring that each

individual has an equal chance of being selected. A

random sample is selected from either the parent

population (𝑷𝒐𝒑) or the historical population

(𝑶𝑷𝒐𝒑) using two random integers. The algorithm

that is being suggested is outlined as:

,

Pop a b

OPop OPop a b







(12)

(3) Mutation of the population

The generation of a new population occurs through

the process of mutation, which is outlined as:

 

M Pop F OPop Pop   

(13)

The coefficient F is the scale factor that satisfies:

 

3 , 1,2,...,

i

F rand i N  

(14)

where 𝑟𝑎𝑛𝑑 is a randomly generated number within

the range of 0 to 1.

(4) Crossover of the population

Population crossover refers to the process in which

genetic information is exchanged between

individuals in a population during the reproduction

phase of a genetic algorithm. The control of the

number of crossing particles in the population is

achieved by manipulating the percentage parameters

using the BSOA method, as described by:

Fig. 5: Algorithm flowchart for ABC optimization

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,,

,

,,

,1

,0

i j i j

ij i j i j

M map

TPop map













(15)

where 𝒎𝒂𝒑 is a binary integer matrix with

dimensions 𝑁×𝐷. The first assignment is 1. The

expression is:

 

, 1:

,

0,

i u mr rand D

i rand D

map a b



















(16)

where 𝑟𝑎𝑛𝑑 (𝐷) is a randomly generated number

within the range of [0, D]. The parameter, 𝑚𝑟

represents the mixing ratio. r, a, and b are random

integers chosen uniformly from the interval [0,1].

𝒖

is an arbitrary integer vector consisting of the

numbers [1, 2, ..., 𝐷]. The BSO algorithm regulates

the size of the new population 𝑻 using Equation (16)

in the crossover process described above. Once the

new population 𝑻 is created, the border regulates the

elements within the population. If the element

surpasses the search border, a new population is

formed based on Equation (17).

(5) Second (2nd) selection of the population

The fitness values of the individuals at corresponding

positions in the new population 𝑷𝒐𝒑 and the

population 𝑻 are compared. If the fitness of the ith

individual in 𝑻 is lower than the fitness of 𝑷𝒐𝒑𝑖, then

𝑻𝑖 replaces 𝑷𝒐𝒑𝑖 and updates the contemporaneous

population 𝑷𝒐𝒑 and expressed as:

   

,

i i i

i

i i i

T fitness T fitness Pop

Pop Pop fitness T fitness Pop











(17)

The population 𝑷𝒐𝒑 has been upgraded and is

now entering the next iteration cycle. The algorithm

iterates the aforementioned procedure until it reaches

the maximum number of iterations or the fitness

value satisfies the predefined requirements. The BSO

algorithm produces the most efficient solution. The

BSO algorithm is shown in Figure 6.

5.4 Lighting Search Algorithm (LSA) Method

The LSA algorithm is shown in Figure 7. In 2005, a

sophisticated metaheuristic optimization technique

known as LSA was developed, [107]. The

phenomenon of lightning is utilized in the creation of

LSA. The search techniques of LSA for attaining

optimum solutions rely on step leader propagation.

The particles of LSA are referred to as projectiles,

which resemble the terms "swarm" or "particle" used

in other optimization approaches. The projectiles

represent the original population and are organized in

a binary tree formation. The projectiles might also be

arranged in a synchronous configuration with two

leaders at fork locations instead of the typical method

of utilizing a step leader. When a projectile moves

through the atmosphere and collides with molecules

and atoms, there is a dissipation of kinetic energy.

The mathematical representations of the kinetic

energy (Ep) and velocity (vp) of a bullet are given by:

Fig. 6: Algorithm flowchart for BSO

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2

11

1

p

E mc

v

c

















(18)

1

22

2

1

i

p

o

sF

vmc

v

c















  























(19)

where vp and v0 indicate the current velocity and

starting velocity of the projectile, respectively. Fi is

the ionization constant rate, c is the speed of light, m

is the mass of the bullet, and s is the route length that

the projectile traverses. Equations (18) and (19)

demonstrate that a projectile's kinetic energy and

velocity are significantly influenced by the position

of the leader tip and the projectile's mass. If a

projectile has minimal mass and has to go a long

distance, it will lack the energy to ionize or explore

the desired distance. Under those circumstances, the

missile is limited to going just a short distance for

ionization or exploitation. Hence, the step leaders'

comparative energies determine the LSA's ability to

exploit and explore.

6 Discussion

This section discusses the correlations between

optimization objectives and approaches and the

trends in battery energy management targets and

techniques based on an analysis of BEM studies

focusing on optimization techniques and targets.

6.1 Exploration of the Connections between

Optimization Targets and Approaches

Based on the preceding analysis, it is evident that the

choice of optimization approach is closely linked to

the goals of a BESS and the formulation of the issue

to be optimized. When dealing with targets that may

be combined, such as energy consumption, profits,

expenses, or the costs of non-physical entities, the

goals can be effectively expressed as an objective

function, considering necessary constraints. This

formulation includes the choice factors in both the

objective function and the constraints. This category

encompasses most technical and economic goals

related to energy optimization and specific

components of hybrid goals. All the sampled

optimization techniques (from the proceeding

section) can be effectively employed to tackle these

issues. For instance, economic goals and a range of

revenues and expenses to be considered may be

easily expressed in a conventional optimization

format. Thus, it is evident that many researchers have

utilized these strategies to address their challenges,

[32], [108], [109], [110]. The selection of an

approach heavily depends on the formulation of the

optimization issue. When the problem is defined

correctly, it will reduce computing complexity and

increase optimization accuracy. Some approaches are

more suitable for straightforward implementations,

while others are desirable when a high level of

computational accuracy is desired.

Fig. 7: Algorithm flowchart for LSA

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6.2 Discussion on Trends of BEM Targets

Analysis of BEM targets reveals that the goals

selected are contingent upon the expectations of the

BESS operator/owner. When a private owner

operates the BESS, there is a greater likelihood that

the battery will be managed to maximize its

economic potential. Alternatively, suppose the power

network or a non-profit corporation manages BESS.

In that case, it is more probable that the BESS will

address system issues, such as maintaining system

stability. However, in the latter scenario, greater

emphasis will be placed on enhancing technical

performance. Technical and economic goals hold

universal significance, and the investing approach

determines any preference for one over the other.

Therefore, a significant trend in BEM aims will

involve the application of multi-objective

optimization for BEM. This means considering

numerous, perhaps conflicting, purposes for battery

optimization rather than focusing on a single service

or goal for the BESS to accomplish. A growing body

of research has employed several goals in their

battery optimization studies, with a comprehensive

list of such studies included in Table 3 (Appendix).

In addition to multi-objective optimization, while

pursuing one optimization goal, another target can be

accomplished simultaneously without any additional

work during the optimization process of the first

goal. As such, the efficiency of deploying the BESS

is significantly enhanced.

6.3 Analysis of BEM Strategies Trends

A comparative assessment of the benefits and

drawbacks of battery optimization strategies is

necessary before choosing the optimization approach

for a particular problem. Each optimization strategy

possesses distinct advantages and disadvantages,

indicating the absence of a universally superior

strategy for solving all BESS management

optimization challenges. By examining the merits

and drawbacks of each optimization method, it is

evident that a significant advancement in

optimization strategies is integrating several methods

to leverage their respective benefits and surpass the

effectiveness of the original approaches. As

emphasized in this review, several studies have been

conducted that have integrated multiple techniques

for battery optimization. The elementary use of

hybrid approaches involves partitioning the problem

into several phases or segments to assign appropriate

strategies to certain phases based on the unique

problem and the benefits of the selected

methodologies.

6.4 Additional Anticipated Developments

In addition to the BEM goals and methods stated

above, a growing body of research focuses on

enhancing the control and performance of battery

systems. Future research is expected to benefit from

advancements in battery technology and improved

battery management. This includes more precise

modeling of battery properties and reduced battery

deterioration. The modern power grid has included

artificial intelligence algorithms and machine

learning. One of the primary uses is for complete

perception, including forecasting power prices,

predicting demand, forecasting renewable energy,

and monitoring electric equipment. In addition,

intelligent decision-making is crucial in several

applications, such as demand-side management,

defect detection, and power system planning.

Furthermore, these algorithms are progressing in

battery operation and bidding inside power markets.

The battery operations will encompass aggregated

solar batteries functioning as virtual power plants

(VPP). A VPP is a network of interconnected

household batteries that may be operated and

synchronized collectively as a single power plant.

The combined energy extracted from each battery

can supply a substantial reservoir of manageable

solar energy.

Furthermore, the use of blockchain technology in

the distribution network has garnered increased

interest due to its ability to facilitate peer-to-peer

trade of home batteries and Electric Vehicles (EVs).

With increased user engagement, future power

markets are expected to become more dynamic.

Consequently, there will be a need to create more

complex concepts and approaches for BEMs.

7 Conclusion

The primary focus of this analysis has been on the

strategies and methodologies for integrating BESS

into RESs. The applications of BEMs have been

summarized based on the utilized optimization

strategies, selected scheduling objectives, and

modeling methodologies. Based on the evaluated

research, most of them utilized a standardized model

for their battery systems. This model employed

simplified charge and discharge processes to depict

the connection between the SoC and the power going

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into and out of the battery. In addition, the primary

goals of implementing the BESS can be classified

into technical, economic, and hybrid goals. Economic

goals are more likely pursued by private owners

seeking more significant profits, while system

operators prioritize technical goals to enhance system

performance. Hybrid methods (combining both

technical and economic goals), which leverage the

distinct strengths and limitations of diverse

techniques, are expected to play a crucial role in the

advancement of optimization strategies in the future,

thereby having a system that is technically sound and

economically reasonable for both consumers and

utilities alike. Prior reviews have concentrated on

particular RESs, such as distributed generation or

large-scale renewable energy plants. In contrast, this

review offers a thorough overview of battery

management approaches and examines the

connection between the chosen optimization targets

and the preferred optimization techniques used in

these studies. The selection of problem-solving

methods is heavily contingent upon the degree to

which the problem is mathematically stated.

Moreover, it is evident that algorithms, which

possess remarkable adaptability, may be applied and

used in many situations, irrespective of whether they

pertain to technical, economic, or hybrid goals. The

study compares the benefits and drawbacks of the

optimization strategies mentioned. It concludes that

hybrid approaches, which combine the advantages of

multiple techniques, will significantly impact future

operation plan development. Although a

comprehensive review and analysis have been

conducted in this study, its limitation is that no

simulation or experimental studies were performed.

As the shift towards further incorporation of

renewable energy progresses, greater demands are

expected to be placed on the efficiency of the BESSs.

The growing prevalence of battery storage

applications indicates that future studies should

explore developing more sophisticated optimization

strategies for managing battery storage to achieve

numerous objectives.

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APPENDIX

Table 1. Specific research on BEMS for technical

goals

Ref

Techniques

Goals

ESSs

RESs

[47]

Battery and

Supercapacitor

Control Systems

Minimizing

the

fluctuations of

RESs

BESS,

Supercapacitor

Wind

Turbine

(WT), PV

[48]

BESS

Optimization and

scheduling

controller

Minimizing

carbon

emission

problems

BESS

Microgrids

[49]

Building

optimization model

Power balance

Lead-acid

batteries, flow

batteries, and hot

water tank

PV

[50]

Particle swarm

optimization (PSO),

Peak shaving

BESS

PV

[51]

Degradation costs

and constraints

Battery

degradation

control

BESS

Generic

[52]

Primal-dual

optimization

Power

Scheduling

community energy

storage

(CES)

Generic

[53]

Monte Carlo

simulation (MCS)

Power

Scheduling

BESS

Electric

vehicle

(EV)

[54]

Scheduling

procedure and

control algorithm

Peak load

shaving and

load

leveling

Generic

[55]

Mixed-integer linear

programming

(MILP)

Energy

Stability and

network

energy loss

reduction

Generic

WT,

EVs, PV

[56]

Lexicographic

and

robust ordering

optimization

Power

Scheduling

Generic

WT, PV

EV

[57]

modified IEEE 123

bus system

simulation

Day-ahead

scheduling

BESSs

WTs, PVs,

[58]

Mixed integer

quadratically

constrained

programming

(MIQCP)

Shift some

loads from

high-price to

low-price

Periods (load

scheduling)

Generic

WT, PV

[59]

CPLEX in general

algebraic modeling

system (GAMS)

Power

Scheduling

Virtual energy

storage systems

(VESS), thermal

energy storage

(TES), hydrogen

storage systems

(HSS)

EVs

Table 2. Specific research on BEMS for economic

goals

Ref

Techniques

Goals

ESSs

RESs

[77]

MILP, model

predictive control

(MPC)

Modeling of

degradation cost for

battery’s optimal

scheduling.

BESS

Generic

[78]

Quantile nearest

neighbor (QNN),

Artificial neural

networks (ANN),

genetic algorithm (GA)

Reducing

microgrid’s total cost

by

Improving the

system’s

power/voltage profile

Generic

PV

[79]

Non-dominated sorting

genetic algorithm II

(NSGA-II)

Minimization of

operating cost

HESS

WT, PV

[80]

Reinforcement

learning.

Minimizing ESS’s

life-cycle cost

Generic

[81]

Reinforcement

learning

Reducing the cost of

energy of a railway

system

Generic

[82]

PSO

Reducing BESS

operating costs

BESS

WT, PV

[83]

Converged barnacles

mating optimizer

(CBMO),

Cost reduction of

microgrid’s operation

Generic

PV

[84]

Load shifting

mechanism

Reducing the energy

cost of microgrid

BESS

WT, PV

[85]

Deterministic

optimization,

Rainflow-counting

algorithm.

Minimizing battery

degradation cost

BESS

Generic

[86]

Slime mold algorithm

(SMA)

Minimizing

microgrid’s operation

costs

BESS

Plug-in

hybrid

electric

vehicles

(PHEV)

[87]

Generalized reduced

gradient (GRG)

Investigating the

economic feasibility

of EVs for vehicle-

to-grid (V2G)

services.

BESS

EV

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Volume 19, 2024

Table 3. Specific research on BEMS for hybrid goals

Ref

Techniques

Goals

ESSs

RESs

[94]

Comprehensive

review

Optimal scheduling,

improving

environmental effects,

reducing costs

Generic

[95]

Improved

grasshopper

optimization

algorithm (IGOA)

Minimizing the total net

present

cost (TNPC) and the loss

of energy

BESS

WT,

PV

[96]

artificial electric

field algorithm

(AEFA)

Minimizing the cost of

system lifespan, designing

an optimal framework for

microgrid

BESS

WT,

PV

[97]

Cost optimisation

Minimizing

MGs

environmental impact.

and operating costs

BESS

PV

[98]

Comprehensive

review

extending battery life,

regulating grid load, and

reducing energy refueling

duration

BESS

EV

[99]

pseudo-two-

dimensional (P2D)

Investigating size

variation on BESS

degradation and energy

generation costs

BESS

WT,

PV

[100]

MILP

Investigating the effects of

battery degradation on

carbon emission and cost

of energy

BESS

EV, PV

[101]

water/ethanol

hybrid (WEH)

electrolyte

High safety,

high power density,

environmental

friendliness, and

low cost

BESS

Generic

[102]

Multi-objective

optimization

Environmental, economic,

and technical assessments

Generic

WT,

PV

[103]

Principles of

materials

breakdown and cell

fabrication

Boosting the energy

density of the battery pack

and reducing the

production cost of an

electric vehicle

BESS

EV

[104]

Generic

optimization

Investigating the impacts

of tariffs, electricity

prices, load profiles, and

costs

BESS

PV

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

This work was supported by the Tshwane University

of Technology PDF Research Funding.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en_

US

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