A review on stand-alone photovoltaic-wind energy system with fuel cell: System optimization and energy management strategy

Accepted Manuscript A Review on Stand-alone Photovoltaic-Wind Energy System with Fuel Cell: System Optimization and Energy Management Strategy

Abba Lawan Bukar, Chee Wei Tan PII:

S0959-6526(19)30633-X

DOI:

10.1016/j.jclepro.2019.02.228

Reference:

JCLP 15958

To appear in:

Journal of Cleaner Production

Received Date:

13 July 2018

Accepted Date:

22 February 2019

Please cite this article as: Abba Lawan Bukar, Chee Wei Tan, A Review on Stand-alone Photovoltaic-Wind Energy System with Fuel Cell: System Optimization and Energy Management Strategy, Journal of Cleaner Production (2019), doi: 10.1016/j.jclepro.2019.02.228

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A Review on Stand-alone Photovoltaic-Wind Energy System with Fuel Cell: System Optimization and Energy Management Strategy Abba Lawan Bukar a, b, Chee Wei Tan a, * a

Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, 81310 Skudai, Johor, Malaysia. b

Department of Electrical Engineering, Faculty of Engineering, University of Maiduguri, PMB 1069, Borno State, Nigeria.

Abstract- This article review system optimization and energy management strategies (EMS) for a standalone photovoltaic and wind energy system integrated with fuel cell. The goal of optimization is to determine the combination of the system components in order to compose a cost-effective system. The EMS is aimed to coordinate the power flow of the system components while satisfying load demand and other constraints. System optimization and EMS are combined such that it is unusual to discuss them individually from a system-level design perspective. Therefore, in this paper, the general optimization formulation framework, the classification and the review of various types of optimization methods are elaborated. Furthermore, the literature on EMS application and research is reviewed. As the core of this review, all system optimization methods and EMS are analyzed and compared by reviewing several cases from the literature. In addition, recommendation for future research as well as the associated challenges are highlighted. It is hoped that this review will serve as a fundamental platform for researchers intending to explore renewable energy system integrated with fuel cell for further improvement. Keyword: Fuel cell, Photovoltaic, Wind turbine, Optimal sizing, Optimization techniques, Energy management strategy.

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Nomenclatures 𝐴𝑐𝑎𝑝_𝑐𝑜𝑠𝑡

Annual capital cost

$

United States Dollar

𝐴𝑀𝑎𝑖𝑛_𝑐𝑜𝑠𝑡

Annual maintenance cost

𝑆𝑂𝐶max

State of charge maximum

𝐴𝑅𝑒𝑝_𝑐𝑜𝑠𝑡

Annual replacement cost

𝑆𝑂𝐶𝑚𝑖𝑛

State of charge minimum

𝐵𝑇𝑐𝑎𝑝

Battery capacity

𝑇𝐿𝑛𝑆

Total time load not supplied

𝐶𝐶𝑖

Capital cost

𝑇𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛

Total operation hour of the system

𝐶𝑖𝑛𝑠𝑡

Installation cost

𝑊𝑇𝑐𝑎𝑝

Wind turbine capacity

CO2

Carbon dioxide

𝐶𝑃𝑖

Component procurement cost

Abbreviations

D

Duration in which load is not meet

ABC

Artificial bee colony

𝐷𝐺𝑐𝑎𝑝

Diesel generator capacity

ACS

Annualized cost system

𝐸𝐿𝑐𝑎𝑝

Electrolyzer capacity

ANN

Artificial neural network

𝐸𝑇𝐴𝐸𝐺

Total annual electricity generated

AM

Analytical method

𝐹𝐶𝑐𝑎𝑝

Fuel cell capacity

BBO

Biogeography-based optimization

H2

Hydrogen

BT

Battery

𝐻𝑇𝑐𝑎𝑝

Hydrogen tank capacity

COE

Cost of energy

𝐻𝑇𝑇𝑎𝑛𝑘

Hydrogen tank

CS

Cuckoo search

𝑖

Components

DG

Diesel generator

𝑖𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛

Annual expected inflation

DHS-SA

𝑖𝑟𝑎𝑡𝑒

Annual interest rate

DOD

Discrete harmony annealing Depth of discharge

L

Average annual load

ELF

Equivalent loss factor

𝑛

Number of components

EENS

Expected energy not supplied

𝑁𝑝𝑣

Number of photovoltaic panels

EL

Electrolyzer

𝑁𝑊𝑇

Number of wind turbine

EMS

Energy management strategy

𝑂𝑀𝐶𝑖

Fuel cell

𝑃𝑉𝑐𝑎𝑝

Operation and maintenance cost of FC component 𝑖 Photovoltaic capacity FL

𝑄(𝑡)

Amount of load not satisfied

FPA-SA

𝑅𝐶𝑖

Replacement cost of component 𝑖

GA

Flower pollination algorithm-simulated annealing Genetic algorithm

GC

Graphical construction

NPC

search

simulated

Fuzzy Logic

Net present cost

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GCS

Grid-connected system

NSGA

Non-dominated sorting genetic algorithm

HBBS-BC

Hybrid big bang-big crunch

O&M

Operation and maintenance

ICA

Imperialist competitive algorithm

PM

Probabilistic method

LCC

Life cycle cost

PSO

Particle swarm optimization

LCOE

Levelized cost of energy

PV

Photovoltaic

LD

Load demand

RE

Renewable energy

LLP

Loss of load probability

SA

Simulated annealing

LOEE

Loss of energy expected

SACS

Simulated annealing chaotic search

LOLE

Loss of load expected

SAHA

Simulated annealing harmony search

LP

Linear programming

SOC

State of charge

LPS

Loss of power supply

TA

Tabu search

LPSP

Loss of power supply probability

TSC

Total system cost

MBA

Mine blast algorithm

VSI

Voltage stability index

MOABC

Multi-objective artificial bee colony

WT

Wind turbine

NM

Numerical method 1. Introduction The fear of depleting oil reserves, coupled with alarming environmental pollution and climate change caused by fossil fuels has prompted the search for a cleaner and sustainable form of energy to generate electricity. Sustainable form of energy includes solar, hydro, wind, and hydrogen (H2), which can be transformed via wind turbine (WT), water turbine, photovoltaic (PV), and fuel cell (FC) to generate electricity. To deal with the intermittent nature of wind and solar energy, the system can be combined with other sources to form a hybrid system. Moreover, storage device can be incorporated into the system to alleviate the shortfall in generation and to maintain system stability (Mahesh and Sandhu, 2015). Conventionally, batteries were the most preferred backup supply but in recent years, batteries are combined with super-capacitors for quick transient response. Super-capacitors have higher dynamic behavior and can withstand shocks in demand as compared to battery (BT) storage (Suberu et al., 2014). In addition, supercapacitors have long lifespan and operate without emitting harmful substances. Despite all the distinct qualities of super-capacitors, its energy density is low and thus restricts the usage

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(Thanaa et al., 2006). Similarly, batteries are storage device having low dynamic response and their lifespan depend on the number of discharge and charge cycles. Moreover, batteries tend to produce harmful gases during operation and their characteristic deteriorates with time (Baumann et al., 2010). However, batteries have gained more prominence because of their ability to withstand high density of discharge current, high load capacity and can tolerate dynamic change in demand. Therefore, these characteristics of batteries guarantee good stability in the system response (Suberu et al., 2014). In recent times, with the evolution of FC, H2 technology has gained more popularity and it is believed to have a great a future (Das et al., 2017; Segura and Andújar, 2015). The use of H2 to energize the FC is an indication that H2 has surpassed the strength of diesel generator systems which are used as long time storage. FC is a technology with low maintenance, high performance, and emissions-free system (Das et al., 2017). Since H2 can be produced through an electrolysis process and can be stored in H2 tanks, it can then be termed as renewable energy (RE) storage (Tie and Tan, 2013). H2 tank as an energy storage system absorbs excess energy with the aid of the electrolyzer (EL) and inject energy into the FC to compensate for shortfall in generation. The inclusion of FC to hybrid RE systems decrease BT size, prolong BT lifespan and improves the general system performance (Bizon et al., 2015). Therefore, when it comes to RE system, whether stand-alone or grid-connected, after the system configuration is being determined, the main challenge is the sizing of the system components and the development of an efficient energy management strategy (EMS) to satisfy the desired objectives without compromising the system performance (Rozali et al., 2013; Mohammed et al., 2015). In other words, component size and EMS are the main factors that determine both the operational and the initial costs of the RE system. In current literature, quite a number of reviews have been published regarding RE system optimization. Sinha and Chandel (2015) and Al-falahi et al. (2017), conducted a review covering recent trends in optimization methodologies for a standalone PV-wind based system. They discussed further on the various optimizations algorithm and RE system evaluation criteria. Also, Siddaiah and Saini (2016) and Upadhyay and Sharma (2014), have presented a review on standalone system configurations, modeling, sizing optimization methodologies, and the control aspects. But, given the prospect of FC as a backup, and as mentioned in the foregoing, it is not

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reasonable to separately discuss system optimization and EMS from a system level design perspective. Therefore, there is still need for a detailed review article, putting together system optimization and energy management schemes used in a PV-wind based hybrid system with FC. To the knowledge of the authors until now there is no specific article that brings together and discussed the aspects. This paper, therefore, is confined to the optimization and EMS of a standalone PV-WT system integrated with FC. 2. Review methodology and scope This section presents the methods used in selecting published literature on system optimization and EMS of a hybrid standalone PV-WT system integrated with FC. It is important to differentiate what has been done previously and to underline areas for future research. For a comprehensive review of the literature, three major online services, namely, Web of science (http://webofknowledge.com/),

Scopus

(https://www.scopus.com/)

and

Google

Scholar

(https://scholar.google.com/) were used. The inserted keywords used include “Optimization of PV-wind-FC”, “PV-wind-FC optimal sizing’’, “Fuel cell power management’’, “PV-Wind-Fuel cell control strategy’’ and “Energy management strategy of PV-WT-FC’’. In such an enormous research database, it is clear that a huge number of articles will be found. Therefore, the following criterion is used to filter the vast research papers. (I) Priority is given to quartile journals II) Journals with impact factor III) Journals indexed in Scopus IV) Published conference papers. The paper must deal with the optimization and energy management for a standalone PV-WT energy system with FC. Considering the space constraint, the papers were read carefully and analyzed in-depth to select the most significant ones, afterward, the outline of the paper is proposed. The arrangement of the sections is done in such a way that each section is linked to its proceeding section. 3. Overview of renewable energy system architecture A typical structure of the RE system is presented in Fig. 1. The whole system is called a hybrid energy system. The system comprises two or more RE sources, supplying power to the electrical load. Hybrid systems are further classified into two categories namely; standalone and gridconnected systems. The classification of the RE system according to size is presented in Table 1. Type

Size

Typical load

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Large

>100 kW

Regional loads

Medium

5kW up to 100kW

Isolated off-grid communities

Small

<5kW

Remote homes

Table 1: Renewable energy system classification based on generation capacity 3.1 Stand-alone system Standalone system is also known as an autonomous system. The system is separated from the grid-network and is responsible for fulfilling demand at all times. This type of system is associated with reliability problem, due to the nature of resources used (Bukar et al., 2017). For this reason, the system has only economic and technical viability in off-grid application where it is difficult to extend the grid to a location (Halabi and Mekhilef, 2018). A flow chart of the standalone energy system planning and design is shown in Fig. 2. 3.2 Grid-connected system A standalone RE system connected to a large independent network, typically the public utility grid is termed as a grid-connected system (GCS) (Wang et al., 2018). In GCS, excess energy generated from the standalone RE system is fed into the grid. Similarly, during shortfall in generation, the grid is used to compensate for the shortfall.

DC/DC

DC-BUS

PPV

DC/AC

AC-BUS

PREN AC/DC

DC/DC

PWG

PINV-Load

LOAD

PFC-INV Fuel Cell

DC/DC

PBAT

Electrolyzer

PREN-EL

Hydrogen Tank

PTANK-FC

PEL-TANK

Fig. 1. Schematic of hybrid stand-alone PV-WT-BT integrated with fuel cell

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  

Assessment of meteorological condition Solar radiation Wind velocity Temperature

Analysis of electric load profile/energy demand

Hybrid system configuration

Modelling of system components

Sizing analysis using optimization techniques Operation analysis of resulting system considering various energy management strategy

Fig. 2. Basic steps for standalone energy system planning and design 4. Optimization formulation framework In this section, the optimization problem formulation framework will be discussed by following its general formulation procedures. The outline of the steps involved is illustrated in Fig. 3. 4.1 Optimization design variables The design variables are sometimes called control parameters. The variables are selected before implementing the optimization algorithm. Based on the nature of the variables, an optimization problem can then be categorized as discrete, continuous or combination of the discrete and continuous. The variable can be single or multivariable depending on the nature of the optimization problem (Yang, 2010). In the RE system optimization, different system configurations exist which comprises various numbers and types of components, namely: the energy generators (e.g. PV and WT), the energy storage systems (e.g. BT and HT), and the

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energy conversion devices (e.g. FC, EL and inverter). The size of the energy storage system can be represented by its capacity and its rated power. For energy generators, factors such as numbers, rated power etc. are used to determine the size of the generators. Regarding this study, the optimized variables used in the consulted references can be found in Table 5. Start Choose design variables Formulate constriants Formulate objective function Set up variables bounds Choose an optimization algorithm Obtain solution(s) Finish

Fig. 3. Flowchart of optimization problem formulation procedure 4.2 Constraints Constraints are important in the quest to determine the optimal solution in any type of optimization study. An optimization problem can be constrained or unconstrained (Mirjalili et al., 2018). For constrained optimization problems, the constraints that would be applied to design variables have to be identified before applying the optimization algorithm to the problem. Constraints set the boundaries of the search space; in general, all N design variables are controlled to lie within the minimum and maximum limits. Various constraints have been applied to the optimization variables in the sizing optimization problems. The constraints can be in the form of equality or inequality, and exists in linear (example upper and lower bound) or nonlinear (dynamic model) forms (Yang, 2010). RE system sizing is a challenging problem that

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depends on the characteristics of the component, the system configuration, as well as the considered objective function and constraints. Thus, it is necessary to choose a proper optimization algorithm that can solve such a complex nonlinear problem. For the purpose of this study, the constraints applied to the system components for different system configurations can be found in Table 5. 4.3 Objective function For any optimization methods, an objective function must be defined, which usually takes into account environmental, technical, and economic aspects. More specifically, in the RE system design perspective, the system cost, O&M cost, replacement cost, and fuel consumption are grouped under the economic aspect. The exhaust emission is considered in the environmental objective function. Loss of power supply probability (LPSP), the loss of power peak probability, and system performance degradation index form part of the technical objective. Design objective can be single or multiobjective depending on the nature of the design problem. In most cases, for multiobjective problems, a single optimization algorithm is used which integrates two objectives term in a weighted sum manner, that means all the competing terms are included in a weighted sum form. However, the different terms in the objective such as pollutions and fuel consumption cannot be summed up directly because of their different amplitudes and meanings. Therefore, to properly define the optimization problem, the terms are normalized into the same scale. In another study, the objectives are homogeneously converted into one single quantity. For instance, in Borhanazad et al. (2014), the reliability of the system and the cost of energy (COE) are all converted into cost and weights are used to penalize the various terms. Similarly, multiobjective optimization method offers in one simulation sets of multiple Pareto-optimal solutions by treating each objective separately (Mirjalili et al., 2018). This approach makes it possible to choose components according to the importance of each provided objective (Tharwat et al., 2017). Over the years, researchers have used various optimization objective functions while designing the RE system. Some of the most widely used objective functions are briefly explained in the proceeding section.

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4.3.1

System cost analysis

The aim of sizing RE system is to obtain the actual size of each system component that will satisfy load demand at minimum cost and subject to some constraints. For the purpose of evaluating the cost objective function, the following costs are considered. 

Cost of maintaining the components.



Cost of procuring the hybrid system components such as the inverter, H2 tank (HT), WT, FC, EL, and PV module etc.



Cost of replacing faulty system components.



Cost of fuel throughout the entire lifespan of the system.

Different methods were used in evaluating the system cost. It can be based on net present cost (NPC), annualized cost of the system (ACS), Levelized cost of energy (LCOE), and Lifecycle cost (LCC). The description of the cost functions and their key formulae are presented in Table 2. 4.3.2

System Reliability

Reliability indices are used to measure system performance in various fields of engineering. Therefore, the concept of reliability cannot be associated with a specific definition (Li, 2013). In terms of power systems reliability, it is defined as the capability of the power system to deliver adequate electricity to customers in a secure way. Various reliability indices were reported in the literature (Mahesh and Sandhu, 2017; Yahiaoui et al., 2017). LPSP, Expected Energy not Supplied (EENS) or Loss of Energy Expected (LOEE), Loss of Load Expected (LOLE), and Equivalent Loss Factor (ELF) are some of the most widely and recently used reliability indices used in assessing the performance of RE system. The indices and their corresponding key formulae are defined in Table 3. 5. Classification of optimization formulation framework and methods Optimization theories and problems cover a large area of applied mathematics, Fig. 4 outlines the classifications of optimization problems. Due to highly nonlinear and stochastic characteristics of hybrid RE system, the optimal sizing problem turns into a constrained combinatorial optimization problem, comprising of multi-objective function and discrete/integer variables, with many nonlinear/linear constraints (Bansal et al., 2013, Katsigiannis et al., 2012). The modality of RE sizing problem is also assumed to be multimodal with many local optima and one global optimum solution (Katsigiannis et al., 2012). Therefore, it is important to investigate the optimal

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solution. RE system sizing problems can be solved using iterative, probabilistic, analytical or stochastic methods (Al-falahi et al., 2017). However, the degree of accuracy and the realistic optimal sizing output varies with the adopted method. In general, there are two distinct types of optimization methods, namely; deterministic searching methods and stochastic searching methods (Yang, 2010). A brief summary of the methods is presented in Fig. 5. The review of the most recent approaches adopted in literature is presented hereafter. Classification of optimization formulation framework

Decision variables

Objectives

Constrainity

Convexity

Modality Global Minima

Continuous

Discrete

Mixed

Binary

Local Minima Constraints

Single Variable

Unconstraint Unimodal

Multi Variable

Single Objective

Multi Objective

Convex

Multimodal

Non-convex

Fig. 4. Classification of optimization formulation framework Optimization Algorithm

Stochastic methods

Deterministic methods

PM

GA

Iterative

DE

LP

GC

BBO

Hybrid methods

AM

SA

PSO

ABC

SA-HA

TS

ICA

HBBS-BC

Fig. 5. Optimization algorithms

MBA

SA-CS

FPA-SA

DHS-SA

12

Cost Indices Net present cost (NPC)/ Total system cost (TSC)

Description Key formulae 𝑛 It is defined as the total sum of the 𝑁𝑃𝐶($) = 𝑁𝑖 × (𝐶𝑃𝑖 + 𝑅𝐶𝑖) + capital cost of components, the

∑

𝑖=1

installation

costs,

[

𝑅

∑𝑂𝑀𝐶 × 𝑖

𝑖=1

(1 + 𝑖𝑖𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛) (1 + 𝑖𝑟𝑎𝑡𝑒)𝑙

]

𝑙

+ 𝐶𝑖𝑛𝑠𝑡

Reference Tezer et al., 2017

replacement

costs, fuel costs, operation and maintenance cost during the useful lifetime of the system. All the cash flows are converted to the initial moment of the investment while considering interest and inflation rate. Annualized cost of the system (ACS)/ Total annual cost (TAC)

It represents the summation of the

Chauhan and Saini, 2014

system capital cost per annum ( 𝐴𝑐𝑎𝑝_𝑐𝑜𝑠𝑡), the annual maintenance cost(𝐴𝑀𝑎𝑖𝑛_𝑐𝑜𝑠𝑡),

and

annual 𝐴𝐶𝑆($ 𝑦𝑟) = 𝐴 𝑐𝑎𝑝_𝑐𝑜𝑠𝑡 + 𝐴𝑀𝑎𝑖𝑛_𝑐𝑜𝑠𝑡 + 𝐴𝑅𝑒𝑝_𝑐𝑜𝑠𝑡

replacement cost (𝐴𝑅𝑒𝑝_𝑐𝑜𝑠𝑡) of all component that form part of the system. Levelized cost of energy (LCOE)

Life cycle cost (LCC)

It is the ratio of the ACS to the 𝐴𝐶𝑆 total electricity generated by the 𝐿𝐶𝐸($ 𝑘𝑊ℎ) = 𝐸𝑇𝐴𝐸𝐺 system. 𝑛

The summation of all one-time (non-recurring) and recurring costs

𝐿𝐶𝐶($) =

∑

𝐶𝐶𝑖 + 𝑅𝐶𝑖 + 𝑂𝑀𝐶𝑖

𝑖=1

Amer et al., 2013

Tezer et al., 2017

13

during the useful lifespan of the system. The non-recurring and recurring

cost

include

the

operating cost, installation cost, initial purchase price of system components,

upgrading

costs,

maintenance, and salvage value. Table 2: A brief summary of the cost of objective functions and their corresponding key formulae

Reliability Indices

Description

Loss of power supply probability (LPSP)

LPSP is the ratio of the deficit in energy generation to the load demand for a period of time.

Expected energy not supplied (EENS)/Loss of load expected (LOLE)

It is the energy which is not supplied to the load. It

Level of autonomy (LA)

LA is a reliability index that expressed the

Key formulae 𝑁

𝐿𝑃𝑆𝑃 =

∑𝑡 = 1𝐿𝑃𝑆(𝑡) 𝑁

Reference Ibrahim et al., 2017; Maleki et al., 2016

∑𝑡 = 1𝐿𝐷(𝑡) 8760

∑

occurs as a result of sudden increase in the demand 𝐸𝐸𝑁𝑆 = 𝐿.𝐷 𝐾=1 that surpasses the existing generation capacity.

Chauhan and Saini, 2014; Khatib et al., 2016

Chauhan and Saini, 2014

percentage of load served during the operational period of the system. It is expressed as one minus the total number of hours in which the system

𝐿𝐴 = 1 ―

𝑇𝐿𝑛𝑆 𝑇𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛

cannot able to serve the load to the operational time of the system Equivalent loss factor (ELF)

ELF is the ratio of effective forced outage period to

1 𝐸𝐿𝐹 = the total time of the system operation, in hours. For 𝑇

𝐻

∑

𝐸(𝑄(𝑡)) 𝐿𝐷(𝑡)

𝑡=1

Upadhyay and Sharma, 2014

14

a standalone system, the acceptable value of the ELF is when ELF <0.1. Table 3: A brief summary of reliability objective functions and their corresponding key formulae

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5.1 Deterministic searching methods The deterministic methods consider graphical construction (GC) method, iterative method, linear programming (LP), numerical method (NM), probabilistic method (PM), analytical method etc. (Yang, 2010; Sinha and Chandel, 2015; Upadhyay and Sharma, 2014). These methods are not suitable for problems with many local optima (Yang, 2010). They are prone to local optima entrapment, such that they cannot converge to a global optimum solution. To avoid entrapment, the algorithm is repeated many times by arbitrarily selecting the initial condition. Consequently, the solution obtained may probably not be the global best. The merits and drawbacks of the deterministic methods are presented in Table 4. Due to the inferior performance of deterministic methods, it is used in a limited number of studies. In Hosseinalizadeh et al. (2016), an iterative method is applied to optimize a standalone hybrid PV-WT-BT-FC system for four different locations. The system is assessed in terms of minimizing total system cost and system reliability based on LOLE indices. Similarly, Smaoui et al. (2015) developed an iterative algorithm to optimize a standalone hybrid PV-WT-FC system for a desalination unit. The objective is to determine the combination of the system components that will result in the least capital cost and ensure that demand is satisfied. 5.2 Stochastic searching methods The stochastic optimization methods are widely used for RE research. The method alleviates the drawbacks of the deterministic approach (Mirjalili et al., 2018). Stochastic methods can be heuristic or metaheuristic. A detailed explanation of the heuristic and metaheuristic algorithm can be found in (Yang, 2010). A depiction of different types of stochastic optimization methods is shown in Fig 5. Unlike the deterministic methods, the stochastic methods use random operators to avoid local optima entrapment and have high chance of finding the global optimum solution to a problem. Among the stochastic optimization methods, the swarm based algorithms are the most popular (Mirjalili et al., 2018). Such methods mimic the problem-solving approaches used by creatures and have successfully been applied in existing literature for the optimal design of RE systems. The merits and drawbacks of the stochastic searching methods are outlined in Table 4, and the summary of sizing optimization methods and analysis is presented in Table 5.

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Particle swarm optimization (PSO) is one of the most popularly used optimization algorithms in the stochastic optimization family. It was developed by Eberhart and Kennedy in 1995. The algorithm mimics the communal behavior of social animals (fish and birds). PSO algorithm is fast in convergence, simple and very easy to implement (Sinha and Chandel, 2015). Sanchez et al. (2014) employ PSO algorithm to determine the size of a hybrid PV-WT-FC system. The system is required to supply the demand of a remote region in Chetumal, Mexico for a period of 20 years at minimum NPC and the reliability of the system is assessed based on LPSP. In Sharafi and ElMekkawy (2014), a dynamic multi-objective particle swarm optimization (DMOPSO) algorithm is proposed for the design of a hybrid standalone WT-PV-DG-BT-FC system. The aim of the design is to simultaneously minimize fuel emission, unmet load and the NPC of the system. The authors used a Pareto based search method in ranking the performance and the optimal system configuration. Sensitivity analysis is also performed on the system to test the influence of varying input parameters on the optimal solutions obtained. Baghaee et al. (2016) used multi-objective particle swarm optimization (MOPSO) to design a hybrid WT-PV system incorporated with HT. The aim is to design a reliable system that will supply load demand for a period of 20 years. The system is optimized based on ACS and LOLE, and it was found that the proposed algorithm yields a better result as compared to the deterministic sizing approach. Safari et al. (2013) used PSO to find the optimal size of a WT-FC-PV standalone system, the simulation is performed using real solar insolation data, wind speed, ambient temperature, and weekly residential load data. Artificial bee colony (ABC) algorithm is a meta-heuristic optimization algorithm developed by Basturk and Karaboga (Karaboga and Basturk, 2007). ABC optimization procedure is inspired by the intellectual searching behaviors of honey bees. In ABC, the potential solution to a problem is represented by the position food source and the quality of the solution corresponds to the amount of nectar. Maleki and Askarzadeh (2014a) used ABC to size a hybrid PV-WT-FC standalone system based on ACS and LPSP. The aim is to determine the size and number of each system component. Different system configurations were optimized, and it was concluded that system configuration comprising of PV-WT-FC is the most economical, followed by WT-FC, and then, PV-FC system. In another study, Nasiraghdam and

Jadid (2012) have taken a

combination of WT-PV-FC and used a multi-objective artificial bee colony (MOABC) algorithm to find the optimal size of the system. The purpose of the optimization is to minimize cost of

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energy, total power loss, and maximize the voltage stability index (VSI) of the distribution system. A 33 bus distribution system is used in testing the algorithm and result obtained were compared with MOPSO and non-dominated sorting genetic algorithm (NSGA). Harmony Search (HS) is a heuristic algorithm that mimics the improvisation process of jazz musicians (Geem et al., 2001). Brinda et al. (2018) implemented HS algorithm to optimize a distributed generating source comprising of FC-WT-PV. The objective is to minimize the cost of energy generation, minimize power loss, minimize CO2 emission, and maximize VSI. The authors used IEEE 33 bus system and five basic loops to evaluate the performance of the algorithms. The study shows that the proposed method performs better in comparison with other methods. Imperialist competitive algorithm (ICA) is an evolutionary algorithm developed by Lucas, Atashpaz, and Gargari in 2007 (Atashpaz-Gargari and Lucas, 2007). ICA is inspired by imperialistic competition. The algorithm starts with an initial population called country. The country is further divided into imperialists and colonies, and all together form an empire. The imperialist competition between the various empires forms the origin of ICA and during the competition period, the powerful empires take ownership of their colonies while the weak ones collapse. Gharavi et al. (2015) used ICA to design a standalone system comprising of WT-ELPV-FC. The goal is to minimize CO2 emission, NPC, and maximize system reliability based on ELF and LPSP. The authors used FL for solving the multi-objective problem and then applied ICA for optimization. The result of the study shows that grid-connected mode is cost-effective compared to the standalone system, but it emits high CO2. Fathy (2016) applied mine blast algorithm (MBA) to size a standalone hybrid energy system comprising of PV-FC-WT for a remote community in Egypt. The aim is to minimize the ACS, measured temperature data, wind speed data, and solar insolation data were used for the optimization. Three system configurations were studied, and it was concluded that the system comprising of PV-WT-FC is found to be cost-effective. The effectiveness of the proposed algorithm is compared with three other meta-heuristic techniques in previous work, namely artificial bee colony (ABC), cuckoo search (CS), and PSO. Cuckoo search (CS) is a natureinspired metaheuristic optimization algorithm developed by Yang and Deb (2009).

The

algorithm is inspired by the fascinating behaviors of cuckoo birds. Rezaei and Ghanbari (2015) applied CS algorithms in designing a hybrid PV-WT-FC with H2 backup. The objective of the

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design is to minimize the cost of energy generation while taking into account the reliability of the system. Maleki and Askarzadeh (2014b) have proposed four different heuristic algorithm aimed to determine the optimum size a hybrid PV/WT/FC system. The authors compared the performance of four different algorithms; simulated annealing (SA), tabu search (TS), HS and PSO. It was found that PSO produced promising results in terms of the total annual cost. In Dong et al. (2016), an improved ant colony optimization is applied to optimize a hybrid WT-PV-BT with H2 backup system. The goal is to determine the optimal number of units that will generate energy at minimum cost. 5.3 Hybrid methods Single stochastic optimization algorithms offer an efficient and accurate set of optimal solutions, with fast computational time. However, with the rapid growth in RE system utilization, there is need for highly advanced and accurate optimization approaches. Therefore, hybrid methods have recently been used for RE system design. Hybrid method is a combination of two single stochastic methods. The combination assumes the advantage of the complementary characteristics between methods to solve complex optimization problems. Examples of some recently developed hybrid methods are the discrete harmony search-simulated annealing (DHSSA), improved harmony search-simulated annealing (IHS-SA), hybrid big bang-big crunch (HBB-BC), hybrid simulated annealing-tabu search (SA-TS) (Siddaiah and Saini, 2016; Sinha and Chandel, 2015). Recently, Maleki et al. (2016) proposed simulated annealing-harmony search (SAHS) and simulated annealing–chaotic search (SA-CS) to determine the most advantageous standalone hybrid system to power a housing unit in Ardabil, Iran. The system configurations examined are WT-FC, PV-FC, and PV-WT-FC. In the study, the total swept area of the WT, number of HT and total area occupied by the PV panels are chosen as decision variables in the optimization. The systems are optimized in order to minimize life-cycle cost (LCC) and maximized the reliability of the system based on (LPSP). The results obtained shows that the hybrid WT-FC-PV system is cost-effective for supplying the housing unit and SA-AH algorithm produced a more promising solution as compared to other techniques.

19

Merits

Drawbacks

Deterministic

Stochastic

Efficient on a unimodal search landscape. Low computational cost. Reliable in finding the same solution. Require less function evaluator. Not efficient on a multimodal search landscape with many local optima. High dependency on the initial solution. Prone to local optima stagnation or premature convergence. Low change of finding the global optimum.

Performs well in both unimodal and multimodal search landscape. Avoid local solutions. Low dependency on the initial solution. High chance of finding the global optimum solution Computationally expensive. Slow convergence speed. Finding a different answer in each run.

Not effective for problems with computationally expensive derivation. Table 4: Merits and drawbacks of deterministic and stochastic optimization methods Ref. Hosseinalizadeh et al., 2016 Smaoui et al., 2015

Optimized parameters PV WT EL FC    

HT 

BS  











Sanchez et al., 2014 Fathy, 2016





















Baghaee et al., 2016













Safari et al., 2013













Analysis

Methods

Objective function

Economic and reliability Economic and reliability

Deterministic

Minimize COE and LOLE

Deterministic

Minimize NPC

Economic and reliability Economic and reliability Economic and reliability

Stochastic

Minimize NPC and LPSP

Stochastic

Minimize TAC and LPSP

Stochastic

Minimize TAC, LOEE, and LOEE

Economic and reliability

Stochastic

Minimize O&M and LPSP

Constraints

DG BT capacity, power balance, and LOLE PV power, number of PV, number of WT and HT storage capacity Power balance and LPSP Number of EL, FC, HT, WT, PVP, and inverters HT storage capacity, PV array installation angle and ELF HT storage capacity, power balance, LPSP, and BT SOC

20 Maleki and Askarzadeh, 2014a Rezaei and Ghanbari, 2015





















Maleki and Askarzadeh, 2014b Maleki et al., 2016















Gharavi et al., 2015





Dong et al., 2016



Sharafi and ElMekkawy, 2014

Economic and reliability

Stochastic

Minimize TAC and LPSP

HT storage capacity and LPSP



Economic and reliability

Stochastic

Minimize ACS, LOLE, and EENS





Economic and reliability

Stochastic

Minimize ACS







Economic and reliability

Stochastic

Minimize LCC and LPSP







Economic and reliability

Stochastic

Minimize NPC, LOEE, LOLE, and ELF











Economic and reliability

Stochastic

Minimize NPC and LPSP















Nasiraghdam and Jadid, 2012















Brinda et al., 2018















Abedi et al., 2011















ELF, PV tilt angle, number of PV, WT and HT storage capacity BT SOC/DOD, number of PV, WT and HT storage capacity PV area, number HTs, total swept area of WT blades PV angle, HT storage capacity, ELF, LOEE, and LOLE BT SOC, number of BT, PV, WT and HT storage capacity, BT storage capacity BT SOC, HT storage, capacity and power balance Thermal limit, bus voltage limit, power balance, and radiality constraint Bus voltage limit, power balance and radiality constraint PV power, number of PV, number of WT, HT capacity, PV tilt angle

Economic, Stochastic reliability and environmental Economic, Stochastic reliability and environmental

Minimize NPC, LLP, and CO2

Economic, Stochastic reliability and environmental Economic, Stochastic reliability and environmental

CO2, COE, and power loss

Minimize COE, power loss, emission, and VSI

Minimize NPC, LPSP and CO2

Table 5: Summary of size optimization methods and analysis carried out

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6. Energy management strategy Electrical energy generated from RE sources is time-varying and intermittent in nature. This drawback can only be overcome by considering various design criteria. One way is by integrating RE systems with storage devices as a backup source to form a hybrid system. However, hybridization of the systems requires strategic control (EMS) to coordinate the power flow of the system components. Depending on objectives of the design, the purpose of EMS is to ensure continuity of supply to the load, improve system performance, maximize use of resources, reduce system operation cost, prolong system lifespan etc. (Vivas et al., 2018, Olatomiwa et al., 2016). Most of the work reported in the literature regarding this study presents simulated strategies to encounter the load demand while obviating from economic and technical optimization criteria, for instance, the degradation of H2 equipment. In the proceeding section, the review of different EMS’s adopted in scientific literature is presented, elaborating the objectives of each of the strategies. 6.1 Strategies that consider only demand satisfaction The primary objective of this strategy is to fulfill demand, and therefore, the control algorithm of the strategy focuses on three design criteria, namely: H2 stock, BT SOC and power balance. The operating limits of BT, FC, and EL are established by the decision variables (H2 stock, BT SOC and power balance). The strategy is known for its simplicity in term of control and design, and it is governed by simple flowchart algorithms. Sizing application that adopts this strategy is simplified. Nevertheless, the solution obtained from the strategy is non-optimal from a technical and economic perspective, because of the non-use of optimization parameters based on the operating costs of the system and equipment degradation. Key features of the scheme and review of the control algorithm, optimization objectives, constraints and the description of EMS of the consulted literature is presented in Table 6 and 7 respectively. Regarding this review work, for the system comprising of PV-WT integrated with FC, the operating strategy is common in many of the reviewed work. For instance, in Bizon et al. (2015), Calderón et al. (2010), Haruni et al. (2013), Feroldi et al. (2013), Cozzolino et al. (2016), BT storage is responsible for absorbing transients and steadying power balance within its minimum and maximum SOC values. Based on the values of BT SOC, the EL and FC operate to absorb excess energy and supply during shortfall, thus, limiting the use of H2 against high energy

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deficit. The result of this scheme reduce degradation of the system elements and prolong the useful lifespan of the system but to the detriment of the BT. The strategy differs for systems having no BT storage since BT SOC is not a design parameter. The work of Li et al. (2013), Torreglosa et al. (2015), Hatti et al. (2011), Sanchez et al. (2014), Eid (2014), Achour et al. (2016), Zahedi (2007) is a typical example, in which the stop and start conditions of the H2 storage system depend on the sign and value of the power balance and this will result in high stops and starts of EL and FC, thus, reduced the lifespan of the system element due to degradation.

23

Objectives Merits

Supply the Demand  Simplicity

Technical Optimization  Medium complexity  Increase lifespan  Increase performance

Economic Optimization  Sizing application  Optimized O&M cost

Techno-economic optimization  Increase system performance  Optimized O&M cost  Lifespan is optimized

Demerits

 Lifetime not optimized  Performance not optimized  O&M cost not optimized  Flowchart algorithm  The algorithm based on power balance  BT short-time storage  FC-EL medium -time  The operation of FC and EL depend on SOC

 O&M cost not optimized

 Lifespan not optimized  Involve a complex optimization algorithm

 Involve a complex optimization algorithm

 Flowchart algorithm  The algorithm based on power balance  BT short-time storage  FC-EL Medium-time  Operation of FC-EL depend on SOC

 The optimization algorithm determines the power reference and priority of the system elements  The optimization algorithm to minimize the cost function

 Reference power is determined by optimization algorithms  Optimization algorithm to optimized multi-objective function

   

   

    

Key features

Design constraints

 BT SOC  H2 stock  Power balance

H2 stock BT SOC Power balance FC-EL-BT degradation

Cost function H2 stock BT SOC Power balance

Table 6: Key features of energy management strategies

Cost function H2 stock Battery SOC Power balance Degradation function

24

Strategies that consider only demand satisfaction Optimization System Control objectives elements algorithm PV-WT-FC-EL- Flow Chart BT-Diesel generator



PV-WT-FC-ELBT

Model predictive control

PV-WT-FC-ELDiesel generatorDump load

Simulink

Satisfy load demand

Design constraints

EMS description

Reference

A simple scheme, in which WT and PV serve Cozzolino et al., 2016 as the main generators. Priority is given to the BT storage to supply energy deficit and absorb excess energy. Operation of FC and EL depend on the minimum and maximum SOC of the BT. The diesel generator operates when storage elements are depleted. H2 stock, SOC, The scheme is based on H2 stock, power Torreglosa et al., 2015 power balance balance, and BT SOC. Operating limits are assigned to different elements in the system. In the case of excess energy, priority is given to EL to produce H2, followed by charging the BT storage. During shortfall in energy generation, stored energy in the BT and HT will be measured, and again, prioritizing the use of H2 stock. A proportional discharge and charge will take place when energy stored in the H2 tank and BT are both equal. H2 stock, In the scheme, PV and WT generate energy Achour et al., 2016 power balance to supply the demand. EL will absorb excess energy to produce H2, and FC operates to compensate shortfall in generation. During high H2 storage, excess energy will be discarded to a dump load. When H2 stock is low, diesel generator instantaneously operates to fulfill demand.

25

PV-WT-FC-EL

Flow Chart

Power balance

The study focused on the optimal sizing of Sanchez et al., 2014 the system components. WT and PV are the main generators in the system, the EL and FC respond for excess and shortfall in generation respectively. To compute for the optimal sizing functions, component cost, the reliability (LPSP) and constraints related to each component are considered in the problem formulation. PSO algorithm is then used to determine the optimal size of each of the components in the system. Table 7: A tabularized review of Strategies that consider only demand satisfaction

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6.2 Strategy that takes into account technical decision factor and demand satisfaction This strategy ensures the proper use of system components and demand satisfaction. The aim of the strategy is to minimize the degradation of the system components that are very vulnerable during operation (BT, EL, FC etc.). The approaches to this strategy in the literature differ from one another and depends on the objective of the study. To achieve the control algorithm, SOC of the storage systems, degradation parameters, and power balance are defined as design constraints. The advantage of this strategy over the preceding one is that it enhances system performance and prolong the lifetime of system elements. Conversely, economic parameters are not considered in the design and therefore, the system response is not optimized. Key features of the scheme and review of the control algorithm, optimization objectives, constraints and the description of EMS in the consulted literature is presented in Table 6 and 8 respectively. As for the previous strategy, the most common solutions incorporate BT storage system, and in the first instance, BT storage will operate to supply deficit or absorb excess energy. The H2 storage will only operate when the minimum and maximum operating limits BT is reached. In a standalone system, demand prioritizing is the most common approach to resolve high deficit in energy and dumping loads are used, example of the strategies are presented in Dash and Bajpai (2015), Brka et al. (2015), Carapellucci and Giordano (2012), More et al. (2015), Miland and Ulleberg (2012). Other schemes define the power reference and priority of the system elements based on the result of different algorithms. Examples of that are implemented in Dursun and Killic (2012), Yumurtaci, (2013). The major difference between this EMS and the ones studied in the foregoing section is the operating limits imposed on the storage elements to prolong its lifespan and improve system performance. To prolong the lifespan of the system element, various approaches have been used to minimize the effect of degradation caused by the stop and start cycles of the FC and EL. Carapellucci and Giordano (2012), Ziogiu et al. (2011), Tesfahunegn et al. (2011), used a scheme based on hysteresis operation mode, which is defined by the minimum and maximum values of the BT SOC. In spite of the improvement from system degradation point of view, the hysteresis bandwidth is fixed and modeled using a simple flowchart. Therefore, there is room for improvement in the usage storage system. Similarly, Brka et al. (2015) and Cano et al. (2015) use demand and weather forecast to determine if the use H2 storage system is necessary for the

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next iteration so as to reduce the unnecessary stop and start cycles. Nevertheless, the reliability of the scheme relies on a reliable system model and accurate forecast to perform the operation, thus, the system performance is subject to the accuracy of the models used. In the same way, Yumurtaci (2013) proposed a scheme which defines the power reference of H2 storage and retains the BT SOC at a fixed point. The strategy is implemented using Artificial Neural Network (ANN). This scheme will prolong BT lifespan, at the expense of intense use of EL and FC. Thus, resulting in a huge O&M cost because of higher degradation on the H2 storage system. Likewise, some strategies are presented with the aim of maximizing H2 production. To achieve this objective, an optimization problem is defined in Dursun and Kilic (2012) and Zhang et al. (2013) and was solved using optimization algorithms: Dynamic real-time optimization and FL. However, the works didn’t consider the lifespan constraints and this could provoke high degradation

of

the

storage

system.

28

Strategies that takes into account technical decision factor and demand satisfaction Optimization System Control Design EMS description objectives elements algorithm constraints PV-WT-FC-BT Flowchart SOC, power The strategy presented is based on energy production balance, and and demand forecast. Based on the current state of Weather the system and the result of the forecast, a decision forecast will be made whether to discharge/charge the BT or to stop/start the FC. The aim is to ensure efficient utilization of FC and prevent it from an unnecessary stop and start operation so as to reduce degradation. PV-WT-FC-EL- Dynamic real- Power balance, Present an active scheme based on energy production BT time Resource and and demand forecast. In the scheme, the BT SOC optimization demand and the result of resource/demand forecast will forecast determine a multi-objective function aimed to reduce stop/start cycles of the FC and EL, and as well  Prolong maximize H2 production. Based on the response of lifespan the algorithm, the storage element to supply or  Satisfy demand absorb energy, and their power ratings will be determined. PV-WT-FC-EL- Fuzzy logic Power balance The scheme is implemented using FL. The aim is to BT and SOC avoid excessive stop and start cycles of the storage elements to minimize degradation. The demand/generation forecast and the BT SOC are used as the decision parameters in the strategy to determine the stop or condition of the FC and EL. PV-WT-FC-EL- ANN SOC, The strategy aims to minimize BT storage BT H2 stock, and degradation. A controller is designed using ANN to power balance keep the SOC BT storage within a preset limit and to ensure demand is satisfied. PV-WT are the main

References Brka et al., 2015

Trifkovic et al., 2014

Cano et al., 2015

Yumurtaci, 2013

29

  

  

Improve performance Satisfy demand BT management

PV-FC-EL-BT

Maximize H2 PV-FC-EL-BT production Satisfy demand Minimize BT usage

Flowchart

Fuzzy logic

energy generator, while other system components are serve as energy storage. During normal operation, BT storage system responds to transient situations and the stop/start cycle of other elements. At any point in time, the controller maintains the SOC of the BT storage around 70%, and in case of deficit or excess energy conditions, the EL and FC respond to maintain power balance. SOC, H2 The goal of the scheme is to maintain power balance Dash and Bajpai, stock, and at all times. PV is the main generator in the system, 2015 power balance the BT absorb excess energy generated and supply the demand in the case of deficit in generation. The stop and start of the FC and EL are determined by the level of BT storage SOC. Depending on the SOC of the BT, two different BT storage operation are studied. For fast charging of BT in low SOC, current control is used, whereas to protect the BT at high charging condition, voltage control is used. In case there is excess energy, PV generation will be limited and it will be used by the EL to generate H2. The FC will be used to neutralize the power balance and supply demand. Power balance The strategy aims to minimize the use of BT, Zhang et al., 2013 and SOC maximize H2 production and ensure demand is satisfied. To achieve the stated targets, the BT storage will operate in a narrow range, thereby regulating it to respond for short demands or transients, whereas, FC supply high demand. The scheme gives priority to the EL to absorb excess

30

energy than the BT storage in order to maximize H2 production. FL is used to determine which part energy deficit or excess will be supplied or absorbed by the FC, the EL or the BT. Table 8: A tabularized review of the strategy that takes into account technical decision factor and demand satisfaction

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6.3 Strategy that takes into account economic decision factor and demand satisfaction The objectives of this strategy are economic analysis and demand satisfaction. The economic factors will help to determine an optimum solution from an economic point of view. However, the optimal solution obtained from this strategy may be unfavorable to equipment operating conditions because of not having technical criteria to evade problems related to operating regimes. Optimum system response from an economic point of view is the main advantage of this scheme. Conversely, the use of complex optimization algorithms in the scheme increases its complexity in real applications. Similarly, as technical parameters are not considered in the strategy, consequently, the system lifespan might not be optimized. Key features of the scheme and review of the control algorithm, optimization objectives, constraints and the description of EMS of the consulted literature are presented in Table 6 and 9 respectively. The solutions adopted from the literature are based on the use of various cost functions associated with the discharge and charge of the storage devices, which determine an optimization problem (Abedi et al., 2012; Cau et al.,2014, Dufo-Lopez et al., 2007, Castaneda et al., 2013; Rouholamini and Mohammadian, 2016). Various methods are used to solve the optimization problem, and the solution obtained determines the power reference and priority of storage elements that is most economical to be utilized. The major difference in the referred references (refer to Table 9) is the optimization methods applied to minimize the cost function.

32

Strategy that takes into account economic decision factor and demand satisfaction Optimization System Control Design EMS description objectives elements algorithm constraints PV-WT-FC-EL- Fuzzy logic & Power balance, The objective of the scheme is to reduce CO2, costs, and BT-Diesel differential LPSP, SOC, ensure power balance. Some expressions are presented to generator evolution CO2 emission, represent each of the target objectives subject to various and cost parameters such as cost function, BT SOC, CO2 function emissions, LPSP etc... The multiobjective problem is solved using differential evolution algorithm accompanied by FL. The result of this algorithm represents the reference power of each element in the system in different energy situations, given precedence to the use of RE generators and storage elements, followed by the diesel generator.  Minimize cost PV-FC-EL-BT Flowchart Cost function, The presented strategy is based on BT storage SOC and  Satisfy H2 stock, SOC, H2 stock. The parameters (BT storage SOC and H2 stock) demand and power will determine the stop and start of the FC and EL, and as balance well as the discharge and charge conditions of the BT storage, giving priority to the storage device with high energy stored in it. In a situation when both parameters are same, an economic decision factor representing cost function determines the storage device with the lowest cost to discharge or charge. PV-WT-FC-EL- Mixed-integer SOC, H2 stock The proposed strategy is based on demand and generation BT-Diesel linear power balance, prediction, which is the input to the control algorithm. generator programming weather Considering the result of the previous demand and forecast, and generation forecast, the state of the system (H2 stock and cost function BT SOC) and the degradation, O&M cost, the reference power of each element will then be determined.

Reference Abedi et al., 2012

Castaneda et al., 2013

Cau 2014

et

al.,

33

PV-WT-FC-EL

Gravitational search algorithm

Cost function The objective of the strategy is to minimize the cost of and power energy production for a residential application and ensure balance demand is satisfied. To achieve the said objective, cost function with respect to the energy production of each system component is presented. A gravitational search algorithm is used to solve the optimization problem. The result obtained represents the reference power of each system element that will result in low energy production cost. PV-WT-FC-EL- Flowchart & Cost function The study aims to optimize system operating costs and BT-Diesel Genetic and power ensure that demand is satisfied. A cost function associated generator algorithm balance with the use of diesel generator, EL, FC, and BT are used to achieve objectives. Moreover, the constraints used, the expected lifespan of the system components and the current system status would determine a nonlinear optimization problem, which is then solved using genetic optimization algorithm. The decision to discharge or charge the storage element depends on the element that has a lower operating cost. Table 9: A tabularized review of the Strategy that takes into account economic decision factor and demand satisfaction

Rouholamini and Mohammadia n, 2015

Dufo-Lopez et al., 2007

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6.4 Strategies that consider both economic and technical decision factor Lastly, this strategy seeks to improve system performance and ensure that demand satisfied. Both economic and technical criteria are considered in the scheme in order to reduce maintenance costs and prolong equipment lifespan. Solutions offered by this strategy are beneficial in both economic and technical point of view compared to the previous strategies, as the strategy integrate equipment degradation and cost parameters in a multi-objective function. The solution obtained determines the reference power supplied by each system components, thereby, ensuring optimal system performance and power balance. The main advantage of this scheme is the optimum system response from a technical and economic perspective. System performance parameters and lifetime are considered in defining the optimization function. Conversely, the optimization algorithms used are complex and thus, increase its complexity in real applications. Key features of the scheme and review of the control algorithm, optimization objectives, constraints and the description of EMS of the consulted literature are presented in Table 6 and 10 respectively. The difference between the proposed strategies is based on the optimization algorithms used to solve the optimization problem and other technical constraints. For instance, García et al. (2013) and Torreglosa et al. (2016), employed FL and Linear Programming respectively to solve the multi-objective problem. The cost function integrates all associated degradation cost and lifespan parameters of each system component. The precedence and power reference of each component is determined by the response of the optimization problem. Similarly, in Torreglosa et al. (2014), the cost function presented includes all the cost related to the degradation process. The precedence of charge and discharge of storage elements (BT and H2) depend on the level of stored energy in each element and associated total degradation cost respectively. In GarcíaTriviño et al. (2014), a work is presented in which PSO is used to optimized three different objectives (increase system lifespan, increase system efficiency and minimize O&M cost).

35

Strategies that consider both economic and technical decision factor Optimization System elements Control Design objectives algorithm constrains PV-WT-FC-ELFuzzy logic SOC, H2 stock BT Power balance, and minimize cost

 Cost reduction  Satisfy demand  Prolong lifespan

PV-WT-FC-ELBT

Flowchart

PV-WT-FC-ELBT-Diesel generator

PSO

EMS description

The strategy aims to prolong the lifespan of the system elements and to minimize system cost. The control algorithm is implemented using FL, which determine the storage device that will be responsible for supplying deficit and absorbing excess energy, and its reference power. To achieve the targets, a cost function associated with each system elements, their lifespan and the current status of the storage devices are used as inputs to the algorithm. SOC, The presented scheme is based on H2 stock minimize cost, and BT SOC. In the scheme, PV and WT power balance, serve as the primary energy sources, while and H2 stock other elements form part of the storage device. The discharge and charge of the storage device in the system depend on the device with lower and higher amount of energy stored in it. In case of equal resources, a techno-economic factor determines which storage device absorb or supply the energy in the system and to ensure power balance. SOC, and Three different strategies are designed to power balance demonstrate the capability of PSO algorithm in solving nonlinear optimization problem. The result obtained from the algorithm for

References García et al., 2013

Torreglosa et al., 2014

García-Triviño et al., 2014

36

the different configurations represents the reference power of the different system components, depending on the different objectives: cost reduction, increasing equipment lifespan, and efficiency. Therefore, no precedence is assigned to the use of different system elements, as such, the priority will be determined by the result of the algorithm. PV-WT-FC-ELLinear SOC, The authors present a multiobjective cost and Torreglosa et al., BT programming H2 stock, degradation function which is minimized by 2016 & Flowchart power balance, an optimization algorithm. The optimization degradation, and results obtained represent the reference cost function. power of the BT, FC, and EL, subject to H2 stock and SOC of the BT. Table 10: A tabularized review of the strategies that consider both economic and technical decision factor

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7. The implication of the review on sustainability issues and clean energy production Energy plays a significant role in modern society. Conventional fossil fuels are depleting daily due to rapid industrialization and growing human population. Importantly, fossil fuels, such as petrol, gas, and coal used for electricity generation have caused serious climate changes in the world which affect millions of lives every day. As a consequence, climate change and global warming awareness have increased, along with research for sustainable measures to reduce CO2 emission. In the meantime, RE sources have drawn much attention, particularly wind and solar due to their great potentials, and can be a suitable option to replace the conventional energy sources. The wind and solar, despite their intermittence nature, can be hybridized and incorporated with other sources and storage devices to minimize the negative effect. Prominent among the energy storage devices are the batteries, and recently, H2 technology seems more popular and have presented to have a future value (Das et al., 2017; Segura and Andújar, 2015). Studies have also proven that energy transformation based on electricity -H2- electricity relation is the beginning of a new model of energy storage (Acar and Dincer, 2019; Clegg and Mancarella, 2015). Moreover, the use of H2 as a fuel in FC is showing its strengths compared to diesel generator which is being used for a decade. FC is a technology with low maintenance and emissions-free system, and thus the inclusion of H2 to energize the FC decrease BT storage size, minimize BT storage degradation and increase system performance. However, to achieve those objectives and to ensure proper operation of the whole hybrid system based on RE system, guaranteeing demand satisfaction, it is necessary to optimize the system and use EMS, of which is the objective of the present review. Optimization determines the combination of the components to compose a cost-effective system, while the EMS ensure proper operation of the overall system. In the proceeding section, some of the challenges that hinder RE system design and the way forward are highlighted. 7.1 Challenges and the way forward I. Accurate estimation of meteorological data: Wind speed, ambient temperature, and solar insolation data are important for estimating the output power of WT and PV. Lack of accurate data in small time step affect the accuracy of the design and delay the design process. Recently, heuristic-based optimization techniques have been validated to overcome this problem. The techniques forecast meteorological information accurately as compared to nonlinear and linear-based methods (Olatomiwa et al., 2015; Zendehboudi et al., 2018)

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II. Load demand: The control of energy storage systems, diesel generators, and the overall system sizing depends on the amount of load demand. Therefore, an online monitoring system should be applied to carefully measure load demand. However, the design complexity, as well as the cost of such a system are the main drawbacks. In contrast, passive mathematical models of load demand could be the cheapest and simplest but not accurate (Ayon et al., 2017). For the time being, active load models together with artificial intelligence methods can be a good solution for such a problem. III. Accurate system components model: Accurate system models for PV, WT, FC, BT, and HT are important for controlling the overall system (Kermadi et al., 2018). The models must correctly describe the energy flow, taking into account all the parameters (meteorological variables) that affect energy production. IV. High system cost: Despite the significant benefits of the RE systems, their deployment still remains a disadvantage because of their high initial capital cost. In most case, incentive must be given to make the system economically-viable (Zhou et al., 2011). V. Energy management strategy: As the deployment of autonomous microgrid system increases, the need for robust communication and real-time EMS among individual energy sources has become an important task and, therefore, deserve additional consideration. VI. Hydrogen: The production of H2 and H2 economy should be a future research topic. An innovation in this area could revolutionize the way we live. VII. H2 production requires a substantial amount of study to greenize existing H2 production. At present, H2 production systems rely heavily on fossil fuels, therefore, it is essential to switch to renewable to make H2 supply a carbon-free. VIII. RE system, especially the PV need a break-through technology to fully harness the useful it power produce. The poor efficiency of the PV is a barrier to its utilization. Similarly, the life-cycle of storage elements, such as the FC, BT, and EL, need to be improved through innovative technologies. IX. DC distribution: Modern equipment and other household appliances used dc voltage for their operation. Researchers have also discovered the benefit of dc microgrid for localized load and the notion is to rewire homes to run on dc (Salomonsson and Sannino, 2007). It is, therefore, worthy to further explore its economic and technical viability.

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8. Conclusions RE systems are important solution towards reducing the negative consequences of climate changes and global warming caused by fossil fuels. In view of the forseegoing, this article has presented a comprehensive review on system optimization and EMS strategies used in the design of a standalone PV-WT energy system integrated with fuel cell. Since the two aspects are closely related from a system level design perspective, this paper has reported the general optimization formulation framework which covers the optimization design variables, constraints, and objective functions for better understanding of optimization fundamentals. Then, system sizing optimization techniques have been classified and compared. The deterministic methods require less function evaluator, but not optimal because they are not efficient in a multimodal search landscape and are prone to local optima entrapment. The stochastic searching methods are becoming popular for the optimal solution. Review of various EMS’s employed for standalone PV-WT energy systems with fuel cell have also been analyzed. The merit, demerit, and constraints associated with each of the strategies have been discussed as well. The strategies that consider both economic and technical decision factors are considered to be the most secured and efficient. The scheme seeks to reduce O&M cost and increase equipment lifespan, nevertheless, a more complex algorithm is required to implement the scheme. As the core of this review, RE system sizing techniques and EMS are studied and compared with several cases from the literature. In addition, areas for further research and challenges are outlined. To conclude, it is hoped that this review article is comprehensive and concise in providing the readers with useful information and the current research status in RE system, in the scope optimization and energy management. Acknowledgments The first author would like to thank the Nigerian Government through the Petroleum Technology Development Fund (PTDF) for financial support in the form of scholarship. In addition, the authors are also grateful to Universiti Teknologi Malaysia (UTM) for providing library facilities. Lastly, appreciation to colleagues who have either directly or indirectly contributed to the compilation of this work.

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