Dynamic optimal distributed resource planning for renewable energy based interconnected microgrids using teaching learning algorithm

Abstract

Distributed Generating (DG) units, Energy Storage Systems (ESS), Distributed Reactive Sources (DRS), and resilient loads make up the microgrid (MG), which can operate in both connected and isolated modes. Because the amount of power generated by Renewable Energy Sources (RES) such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES) is unpredictable, it becomes difficult for MGs planners to make judgments. In this article, the uncertainties caused by RES are resolved through the successful application of a hybrid optimization approach and the integration of hybrid DGs. The Teaching Learning Algorithm (TLA) is used in this study to determine the best site for DGs and reconfiguration, and heuristic fuzzy has been merged with TLA to handle multi-objectives such as total generation and emission cost minimization, and bus voltage deviation. In addition, the impact of replacing RES with hybrid DGs on RES performance is investigated. The ideal structures are determined by solving four different scenarios with the suggested approach, allowing DSO to make decisions with greater flexibility. The proposed technique is validated using a benchmark IEEE 33 bus system that has been converted into a microgrid. WES, PVES, and hybrid DGs are validated using a 24-hour daily load pattern with 24-hour load dispatching characteristic behaviors.

Keywords

Renewable energy sources radial distribution system wind energy systems photovoltaic energy systems teaching learning algorithm

Nomenclature:

Notations

Description

Bus Voltage Deviation

Total number of load buses present in the system

V_feeder

Voltage at the feeder

P_L

Real power Transmission losses

Q_L

Reactive power transmission losses

P_D

Real power Demand

Q_D

Reactive power Demand

TEC

Total Emission Cost

TGC

Total Generation Cost

Diesel Engine

E_SB^t . s

Stored Energy

E_SB^min

Minimum Stored Energy

E_{SB}^{\max}

Maximum Stored Energy

P_{SB, ch}^{\max}

Charge Rate

P_{SB, dis}^{\max}

Discharge Rate

η _ch

Charge efficiency

η _dis

Discharge efficiency

r_{m}^{up}

Ramp up

r_{m}^{down}

Ramp down

Δt

Time period

1 Introduction

The power system is undergoing significant changes in all areas of design, configuration, operation, and control in the context of global energy infrastructures. The demand for installing Distributed Energy Resources (DER) and Energy Storage Systems (ESS) is fast expanding as a result of increased awareness of environmental issues and deregulation of the power industry, with more and more DG units being adopted and deployed near homes. DGs and ESSs placed near loads provide significant benefits, including increased system reliability, improved energy quality, reduced power loss, and lower power generation and emission costs. Nonetheless, the diversification of this paradigm shift necessitates the coordination and collaboration of distribution and control systems, which poses certain challenges in energy system design, operation, and control. Power distribution firms are faced with the difficult task of developing a reliable and cost-effective distribution infrastructure. Various energy management and control methodologies, as well as heuristic optimization methods connected to microgrids, have recently evolved, each providing quality and trustworthy support to ensure microgrids operate optimally. A distribution system [1] suggested at a complete assessment of the intelligent control methodologies that has been applied as well as future development. For successful management of MG with RESs based DGs optimization algorithms such as harmony search algorithm [2], stochastic optimization [3], resilient optimization algorithm for mixed integer programming [4], and cuckoo search algorithm [5] have been established. The hierarchical structures of microgrid control methods were described in [6], and it’s managed to the microgrid optimal operation and control. In MG, one purpose of energy distribution is to reduce costs and losses. A hybrid grey wolf and particle swarm optimization approach for minimizing power losses in the distribution system by selecting the best site for DGs [7]. An evaluation of the optimization approach is used for the combined reconfiguration and optimal DG placement successfully conducted [8].

However, the particle swarm optimization (PSO) with a fitness function based on the microgrid, it has been benefit to load response and WES [9]. In addition to that for executing optimal generation scheduling (OGS) and distribution feeder reconfiguration (DFR) using Mixed-Integer Second-Order Cone Programming (MISOCP) recommended on a short-term forecast basis it’s evaluated the lowering total microgrid operating costs under various DG and utility system [10]. To address the distribution system optimization problem under dynamic load conditions, an improved hybrid fuzzy-genetic approach was developed in [11]. Using the Interval Optimization approach, [12] presented a Multi-objective Optimal Dispatch Model of Microgrid (MODMG) under power system uncertainty. In [13], a hybrid optimization technique based on fuzzy theory was developed to increase the speed of convergence and better handle operational constraints along with the objective function in order to achieve the goal of lowering the total fuel cost and real power loss of MG. To minimize economic and emission costs, an effective scenario-based stochastic programming approach was presented in [14], which decomposes the problem into deterministic problems. Table 1 shows a detailed analysis of the remaining literature that proposes solutions to the microgrid optimization challenge.

Table 1
Comparison of literature

PV WT Hybrid DG Optimi- VD Demand Mathematical

DGs location zation uncertainty complexity

identification

Gazijahani, Farhad Samadi et.al., [15] √ √ √ √ √ Middle

Kavousi-Fard et.al., [16] √ √ √ √ √ √ Simple

Dall’Anese et.al., [17] √ √ √ √ √ Middle

Nikmehr et.al., [18] √ √ √ √ √ Middle

Golshannavaz et.al., [19] √ √ √ √ Middle

M.Hemmati et.al., [20] √ √ √ Complex

Mosayeb Bornapour et.al., [21] √ √ √ √ √ Middle

Maulik, Avirup et.al., [22] √ √ √ √ Complex

Neeraj Kanwar et.al., [23] √ √ √ √ √ √ Simple

Hamed Nafisi et.al., [24] √ √ √ √ √ Simple

Gazijahani, Farhad Samadi et.al., [25] √ √ √ √ Middle

C. Chen et.al., [26] √ √ √ Middle

Mohammadi et.al., [27] √ √ √ √ Middle

Jithendranath et.al., [28] √ √ √ √ √ Middle

Gabbar, Hossam A et.al., [29] √ √ √ √ Middle

Jian Chen et.al., [30] √ √ Simple

Ed McKenna et.al., [31] √ √ Simple

Proposed √ √ √ √ √ √ √ Simple

	PV	WT	Hybrid	DG	Optimi-	VD	Demand	Mathematical
Gazijahani, Farhad Samadi et.al., [15]	√			√	√	√	√	Middle
Kavousi-Fard et.al., [16]	√	√		√	√	√	√	Simple
Dall’Anese et.al., [17]	√	√		√		√	√	Middle
Nikmehr et.al., [18]	√	√		√	√		√	Middle
Golshannavaz et.al., [19]	√	√		√		√		Middle
M.Hemmati et.al., [20]		√				√	√	Complex
Mosayeb Bornapour et.al., [21]	√	√		√	√	√		Middle
Maulik, Avirup et.al., [22]	√			√		√	√	Complex
Neeraj Kanwar et.al., [23]	√	√		√	√	√	√	Simple
Hamed Nafisi et.al., [24]	√	√			√	√	√	Simple
Gazijahani, Farhad Samadi et.al., [25]	√	√			√	√		Middle
C. Chen et.al., [26]	√	√			√			Middle
Mohammadi et.al., [27]	√	√		√	√			Middle
Jithendranath et.al., [28]	√			√	√	√	√	Middle
Gabbar, Hossam A et.al., [29]	√	√			√	√		Middle
Jian Chen et.al., [30]			√				√	Simple
Ed McKenna et.al., [31]			√				√	Simple
Proposed	√	√	√	√	√	√	√	Simple

The majority of the studies in Table 1 solved the optimization problem by using optimal DG placement as the objective function. The need of controlling CO2, NO2, and SO2 emissions has been recognized by most developing countries, resulting in the use of renewable energy sources for power generation. Based on different variables such as efficiency, lifetime, payback period, installation and maintenance costs, power generation through WES and PVES is the most recommended among all other RES. However, the power generation of these renewable energy sources is mostly dependent on environmental conditions, resulting in seasonal and time-varying power generation. Furthermore, many studies ignore the ambiguities associated with WES and PVES. As a result, the suggested position for the DGs for optimal functioning displays inadequate power delivery performance for the majority of the hours of the day. The cost co-efficient of hybrid DG is discussed in the papers [31]. The appropriate replacement of standalone RES-based DGs with hybrid DGs can address this key issue [32]. Based on the modeling of the problem, the technique applied for DG placement, and the assumptions made to achieve the answer, the mathematical complexity of the solution process is classified as small, moderate, or big in the literature review. The impact of WES and PVES on the microgrid is investigated using a hybrid Fuzzy and TLA algorithm to solve the optimization issue in the microgrid while taking into account their unpredictability. The system’s distinctive data can be found from [33].

1.1 Contributions and organization

For the best placement of renewable energy-based DGs in the microgrid, this paper presents a hybrid Fuzzy-TLA algorithm. The following are some of the paper’s contributions:

Optimal solution based on overall generating cost, emission cost, and system bus voltage variance.

For simultaneous reconfiguration and DG placement, a single algorithm is used.

Performance analysis of the two most popular RES and their many combinations.

Performance improvement is realized by replacing RES with hybrid DGs.

The best daily dispatch investigation with genuine RES and load uncertainty features. The following are the six sections that make up this document. The considered scenarios of the work are described in section II. Section III introduces the considered objectives as well as the operational constraints. The proposed algorithm’s step-by-step method is detailed in Section IV. Section V describes the test system and examines the various case study outcomes. Finally, section VI summarizes the findings of the current study and introduces potential future research fields.

2 Considered scenarios

2.1 Optimal dispatch under wes integration

This scenario is realized by assuming that the microgrid is only augmented by the WES. The WES’s generating cost is calculated based on its installed capacity, life expectancy, and installation, operation, and maintenance costs. For WES [34], the generation cost functions are as follows:

${WE}_{cos t} = c_{WE}^{g} * (\frac{C_{WE, IN}}{K_{WE}}) + c_{WE}^{g} * C_{WE, OM}$ (1)

Where $c_{WE}^{g}$ an installed capacity of WES, K_WE is a designed lifetime of WES, C_WE,INandC_WE,OM represents the installation, and operation and maintenance cost of the WES, respectively.

2.2 Optimal dispatch under pves integration

Only PVES-based DGs are replenished together with the grid in this situation. Although the operational costs of PVES plants are low when compared to other renewable energy plants, the installed capacity, life time, and installation, operation, and maintenance costs of each plant are all taken into account when determining the per KW power output cost. The cost of electricity generation per unit fluctuates with plant capacity, which is stated as,

${PV}_{cos t} = c_{PV}^{g} * (\frac{C_{PV, IN}}{K_{PV}}) + c_{PV}^{g} * C_{PV, OM}$ (2)

Where $c_{PV}^{g}$ an installed capacity of PVES, K_PV is a designed lifetime of PVES, C_PV,INandC_PV,OM represent respectively the installation, and operation and maintenance cost of the PVES.

2.3 Optimal dispatch under combined PVES and WES integration

The combined effect of PVES and WES integration into the microgrid is examined in this scenario. In this situation, it is considered that the indicated optimal placement for the DGs has sufficient environmental support and is located in excellent conditions for their respective power generation. Equations (1) and (2) are used to compute the overall cost of power generation based on the number of PVES and WES connected to the microgrid.

2.4 Optimal dispatch under hybrid PV-diesel and WE-diesel integration

This situation is being considered in order to better understand the advantages of hybrid DGs. It is well known that wind and photovoltaic electricity generation differs depending on environmental factors. Even in the proposed optimal sites, renewable energy-based DGs perform poorly. By replacing stand-alone PVES and WES systems with hybrid systems, the efficiency of such circumstances can be increased. The hybrid DGs are designed to provide a steady power output despite environmental variables. The benefits of both controllable and uncontrollable DGs are combined in the hybrid DG. The Diesel Engine is assumed to be hybrid in this study, with PVES and WES operating separately. The cost function of the Diesel engine [34] is shown in equation (3),

${DE}_{cos t} = a_{DE} + b_{DE} P_{DE} + c_{DE} P_{DE}^{2}$ (3)

Where a_DE, b_DE and c_DE are the cost coefficients of DE. As this work addresses the total emission cost reduction of the microgrid along with generation cost reduction, the emission cost estimation of the Diesel Engine (DE) and grid are taken care. The emission cost of the plants is expressed in terms of CO₂, NOx and SO₂. The generalized equation for estimating the emission cost of the DE per kW power generation are expressed as,

${EM}_{D} E = ({CO}_{2}^{DE} + {NO}_{x}^{DE} + {SO}_{2}^{DE}) \times P_{DE}$ (4)

Similarly, the emission by the grid for per kW power generation is determined using the Equation (5).

${EM}_{G} rid = ({CO}_{2}^{grid} + {NO}_{x}^{grid} + {SO}_{2}^{grid}) \times P_{grid}$ (5)

3 Objective functions and constraints

The following subsection discusses the three objective functions and along with the operational constraints [34] considered in this paper.

3.1 Minimization of total generation cost

The optimization has been approached by simultaneously implementing the reconfiguration and optimal DG placement on the microgrid. Therefore, the minimization of Total Generation Cost (TGC) is viewed as a function of per KW generation cost by the Grid, WES, and PVES.

Minimize;

$TGC = {Grid}_{cost} + \sum_{i = 1}^{nWE} {WE}_{cos t, i} + \sum_{j = 1}^{nPV} {PV}_{cos t, j}$ (6)

Where nWE and nPV represent the total number of WES and PVES integrated in the microgrid, respectively. Also, Grid cost refers the per kW power generation cost by the grid which is expressed in (7),

${Grid}_{cost} {= P}_{grid} {* K}_{grid}$ (7)

Where K_grid represents the cost co-efficient constant which is assumed as 0.044 $/kW-hr[34].

3.2 Minimization of bus voltage deviation

In order to ensure the operational security and voltage quality to the customer ends, the minimization of bus Voltage Deviation (VD) is considered as an additional objective with the TGC reduction. The minimization of the bus voltage deviation with the set reference voltage for all the load buses of the system is expressed as [35],

$VD = \sqrt{\frac{\sum_{i = 1}^{nb} {(| V_{i} | - | V_{feeder} |)}^{2}}{nb}}$ (8)

Where V_feeder is the voltage at the feeder bus, V_i is the ith bus voltage and the term ‘nb’ refers to the total number of load buses present in the system.

3.3 Minimization of total emission cost

The Total Emission Cost (TEC) of the system is determined through summing the emission produced by the diesel engine expressed in (4) and emission by the grid. This cost function is considered during the integration of hybrid DGs to the microgrid.

$TEC = EM_DE + EM_Grid$ (9)

3.4 Operational constraints

The considered operational constraints are as follows:

$\sum_{i = 1}^{NG} {PG}_{i} - P_{L} = P_{d}$ (10)

$\sum_{i = 1}^{NG} {QG}_{i} - Q_{L} = Q_{d}$ (11)

$\begin{matrix} max (P_{i}^{\min}, P_{i}^{0} - {DR}_{i}) \\ \leq P_{i} \leq min (P_{i}^{\max}, P_{i}^{0} + {UR}_{i}) \end{matrix}$ (12)

$P_{i} \in {\begin{matrix} P_{i}^{\min} \leq P_{i} \leq P_{i, 1}^{l} \\ P_{i, j - 1}^{u} \leq P_{i} \leq P_{i, j}^{l} \\ P_{i, n_{i}}^{u} \leq P_{i} \leq P_{i}^{\max} \end{matrix}, j = 2, 3, . . ., n_{i}, i = l, . . ., m .$ (13)

$P_{G_{i}}^{\min} \leq P_{G_{i}} \leq P_{G_{i}}^{\max} 1 \leq i \leq N_{G}$ (14)

$Q_{G_{i}}^{\min} \leq Q_{G_{i}} \leq Q_{G_{i}}^{\max} 1 \leq i \leq N_{G}$ (15)

$Q_{C_{i}}^{\min} \leq Q_{C_{i}} \leq Q_{C_{i}}^{\max} 1 \leq i \leq N_{C}$ (16)

$| S_{k} | \leq S_{k}^{\max} 1 \leq k \leq N_{E}$ (17)

$0.95 \leq Vi \leq 1.05, i = 1, 2, . . ., nbus$ (18)

$0.8 \leq PF \leq 1$ (19)

$P_{gen, i, s} \leq P_{gen, i, \max}, \forall i \in N_{droop}$ (20)

$Q_{gen, i, s} \leq Q_{gen, i, \max}, \forall i \in N_{droop}$ (21)

$\begin{matrix} 0 \leq & P_{gen, i, \max} - P_{gen, i, s} ⊥ \frac{1}{m_{p, i}} (ω_{i}^{*} - ω_{s}) \\ - P_{gen, i, s} \geq 0, \forall i \in N_{droop} \end{matrix}$ (22)

$\begin{matrix} 0 \leq Q_{gen, i, \max} - Q_{gen, i, s} ⊥ \frac{1}{n_{p, i}} ({| V_{k} |}^{*} - | V_{k, s} |) \\ Q_{gen, i, s} \geq 0, \forall i \in N_{droop} \end{matrix}$ (23)

$\sqrt{{(P_{gen, i, s})}^{2} + {(Q_{gen, i, s})}^{2}} \leq S_{gen, i, \max}, \forall i \in N_{droop}$ (24)

${\begin{matrix} P_{m}^{t, s} - P_{m}^{t - 1, s} \leq r_{m}^{up} Δ t \\ P_{m}^{t - 1, s} - P_{m}^{t, s} \leq r_{m}^{down} Δ t \end{matrix}$ (25)

$E_{SB}^{t, s} = {\begin{matrix} E_{SB}^{t - 1, s} - P_{SB}^{t, s} Δ t η_{ch} P_{SB}^{t, s} < 0 \\ E_{SB}^{t - 1, s} - P_{SB}^{t, s} Δ t / η_{dis} P_{SB}^{t, s} \geq 0 \end{matrix}$ (26)

$P_{SB, ch}^{\max} \leq P_{SB}^{t, s} \leq P_{SB, dis}^{\max}$ (27)

$E_{SB}^{\min} \leq E_{SB}^{t, s} \leq E_{SB}^{\max}$ (28)

Equations (10) and (11), respectively, indicate the microgrid’s actual and reactive power balances. (12) and (13) show the generating capacity of distributed generating units (13). Equations are used to express the capacity limits of electrical power equipment (14–17). In (18) and (19), the DGs’ minimum and maximum bus voltage limits, as well as their power factor limits, are defined. The DG units’ actual and reactive power handling capacity are expressed in (20) and (21, respectively. A set of constraints (22) and (23), respectively, are used to solve the DG units’ actual and reactive power handling capacities. The apparent power handling limits obtained from the real and reactive power handling limits is shown in (24). The expression for the ramp up and down rate constraint is shown in (25), where $r_{m}^{up}$ and $r_{m}^{down}$ represent ramp up and down limits, respectively; Δt is the time period. The constraints related to storage battery are expressed in equations (26–28), where $E_{SB}^{t, s}, E_{SB}^{\min}$ and $E_{SB}^{\max}$ are the stored, minimum and maximum energy in SB, respectively. $P_{SB, ch}^{\max}$ , and $P_{SB, dis}^{\max}$ are the charge and discharge rate of the SB; η_ch and η_dis are the charge and discharge efficiency.

Equations (6), (8), and (9) express three objective functions in this optimization problem (9). The recommended method shall deliver the most compromised solution among the assessed goal functions under certain system operating conditions. To merge the objective functions into a single objective function, a fuzzy decision set method is introduced. The membership functions are introduced in this regard for each objective function. The membership functions produced fuzzifier values for the specified configuration, which are utilized in the fuzzy decision set to make decisions. For each fitness function, fuzzy membership functions with maximum and minimum limitations are used. The developed fuzzy functions can be modified according to the contribution of the fitness functions according to the problem needs. The membership functions are defined as follows,

$\begin{matrix} μ_{TGC} \\ = {\begin{matrix} 1 & TGC \leq {TGC}_{\min} \\ \frac{{TGC}_{\max} - TGC}{{TGC}_{\max} - {TGC}_{\min}} & {TGC}_{\min} < TGC < {TGC}_{\max} \\ 0 & TGC \geq {TGC}_{\max} \end{matrix} \end{matrix}$ (29)

$\begin{matrix} μ_{TEC} \\ = {\begin{matrix} 1 & TEC \leq {TEC}_{\min} \\ \frac{{TEC}_{\max} - TEC}{{TEC}_{\max} - {TEC}_{\min}} & {TEC}_{\min} < TEC < {TEC}_{\max} \\ 0 & TEC \geq {TEC}_{\max} \end{matrix} \end{matrix}$ (30)

$\begin{matrix} μ_{VD} \\ = {\begin{matrix} 1.0 & for {VD}_{x} \leq {VD}_{\max} \\ \frac{{VD}_{\max} - {VD}_{x}}{{VD}_{\max} - {VD}_{\min}} & for {VD}_{\min} < {VD}_{x} < {VD}_{\max} \\ 0.0 & for {VD}_{x} \geq {VD}_{\max} \end{matrix} \end{matrix}$ (31)

During the arrival of each new configuration, the values of TGC, TEC, and VD are calculated, which are defined as the ratio between the prior best solution and the current solution in the preceding equations. From the membership values found in each feasible solution, the decision fuzzy set (32) is used to discover the compromised solution.

$μ D = \min (μ_{TGC}, μ_{TEC}, μ_{VD})$ (32)

4 Teaching learning algorithm for optimal searching

For optimal searching, the Teaching Learning Algorithm (TLA) [36] is used, which is one of the nature-inspired, well-proven, population-based optimization algorithms. It searches for the best answer using a population of solutions, avoiding the need for algorithm-specific parameters for convergence. When compared to other population-based algorithms like GA, ABC, PSO, HS, DE, and others, this trait is regarded a major strength for the TLA. Importantly, this method ensures a worldwide optimal solution for problems of various scales and characteristics. It is based on the data collected throughout the teacher and learner phases of each iteration. In every iteration, the best learner becomes the teacher as teacher is the most experienced, knowledgeable and highly learned person in the community. In this problem, in every iteration, the teacher is the optimal location of DGs and tie-switches based on the objectives. The learners learns the course according to the quality of the teacher and the quality of the learners present in the class for each course. The learners get the course input from the teachers and the interaction between the co-learners of the class. In this problem, every learner has a set of DG locations and tie-switches. The suggested algorithm’s step-by-step approach is depicted in Fig. 1 as a flowchart. The following is the pseudocode and technique for the proposed calculation:

Step 1: Initialization the values

Fig. 1

Flow chart of Teaching Learning Algorithm.

Set Max Iteration (MIT), Courses Offered (C = Number of DGs+Number of MGs), Total learners (L), gen_count = 0

Initial set of Learners

f(L,C)=random()

Step 2: Find the mean for all the courses offered

Find Mean (C)=f(L,C)

Step 3: Find the fitness for initial population

Find Obj(f(L))

Step 4: proceed the following iteration till (gent_count < MIT)

Find the teacher from the best individual of the generation

V_teacher = Min(f(L))

F_best = f(V_teacher)

Step 5: Find the teaching factor

TF=(1 + random()*(2-1))

Step 6: Production of improved learners and teachers

f_teacher(L,C)=f(L,C)+random()*(f(F_best,C)-TF*Mean(C))

Step 7: Production of better learners

if (Obj(f(L,C))>Obj(f_teacher(L,C)))

f_learners(L,C)=f_teacher(L,C)

else

f_learners(L,C)=f(L,C)

Step 8: Interaction between learners, m and n refers integers (<C) and m≠n

if(Obj(f_learners(L,m))>Obj(f_learners(L,n)))

f(L,C)=f_learners(L,m)+random()*(f_learners(L,m)- f_learners(L,n))

Step 9: Find a feasible Solution

else

f(L,C)=f_learners(L,m)+random()*(f_learners(L,n)- f_learners(L,m))

Step 10: move to next generation

gen_count = gen_count+1

The problem’s control variables were used to identify which courses should be taken for the TLA. The fitness function for optimal searching is taken to be equation (32). The proposed algorithm is written in the JAVA programming language and runs on a 4.1 GHz Intel Core i5 processor.

5 Simulation and results

5.1 Test system data

For the simulation investigation, a conventional IEEE 33-bus Radial Distribution System (RDS) is used. As indicated in Fig. 2, the system is served by 32 load buses with five tie-lines. The system’s electrical specifications are as follows: 12.66 base KV, 100 base MVA, 3.715 MW total real power demand, 2.3 MVAR total reactive power demand, and 202.66 kW real power transmission loss under normal operating conditions. Under any conditions, the system’s allowable minimum bus voltage is 0.9 per unit (p.u), with a maximum branch load of 400A.

Fig. 2

Single-line diagram of IEEE 33 bus RDS.

This research focuses on microgrid optimization for dynamically variable active and reactive power consumption from hour to hour. Figure 3 depicts the test system’s assumed active and reactive power demands for each hour of the day. Both feeder level reconfiguration and optimal placement of RES are part of the optimization plan. The microgrids of the system are built depending on the system’s available tie-lines. Individual loops and accompanying loads connected to each tie-line are taken into account for load sharing between microgrids via reconfiguration. The reconfiguration challenge entails determining the best combination of tie-switches. The reduced solution set for each microgrid is shown below,

{NOS:{S₃₃}, NCS:{S₄,S₅,S₆,S₇,S₁₉,S₂₀}}

{NOS:{S₃₄}, NCS:{S₁₂,S₁₃,S₁₄}}

{NOS:{S₃₅}, NCS:{S₈,S₉,S₁₀,S₁₁,S₂₁}}

{NOS:{S₃₆}, NCS:{S₁₅,S₁₆,S₁₇,S₂₉,S₃₀,S₃₁,S₃₂}}

{NOS:{S₃₇}, NCS:{S₂₃,S₂₄,S₂₅,S₂₆,S₂₇,S₂₈}}

Fig. 3

Power demands for the 24 hrs.

The planned effort also aims to determine the performance advantage of wind and photovoltaic energy sources when combined with an interconnected microgrid. It was done by examining four possible scenarios based on the utility customer’s dynamic loading behavior. The analysis is based on the usual load pattern on a day shown in Fig. 3 and the hourly variable efficiency of the typical WES on a day shown in Fig. 4. After successfully implementing the method under hourly varying generation and load patterns (as indicated in Table 2), the ideal solution is reached. In Fig. 4 show that hour-to-hour efficiency for the WES and PVES is illustrated. The information on energy systems is obtained from the literature [31, 32].

Fig. 4

Percentage efficiency of WES and PVES.

Table 2

Summary of results with WES under daily load pattern

Hrs.	% Efficiency	Total	Configu-	Optimal WES	P_WES in	P_Grid in	TGC in	Loss in	Min.	Bus
	of WES	Load	ration	location	MW	MW	$/hr	kW	Voltage
									in p.u
1	0.364	3.5293	7,9,14,32,37	31,31,31,32,32	0.1820	3.4352	187.5549	87.9045	0.9526	32
2	0.267	3.3435	7,9,14,28,32	31,17,32,32,17	0.1335	3.2896	181.1488	79.5608	0.9560	32
3	0.267	2.9720	9,14,28,33,36	32,31,32,17,17	0.1335	2.8992	163.9749	60.7445	0.9647	17
4	0.234	2.7863	9,14,28,33,36	16,32,16,16,17	0.1170	2.7220	156.1776	52.7840	0.9670	17
5	0.312	2.6005	9,14,17,28,33	32,16,32,32,32	0.1560	2.4876	145.8632	43.1146	0.9718	17
6	0.329	3.6407	7,9,14,32,37	31,32,32,31,31	0.1645	3.5695	193.4682	93.3470	0.9522	32
7	0.476	3.5293	7,9,14,32,37	31,31,17,32,31	0.2380	3.3754	184.9260	84.1568	0.9541	32
8	0.477	3.4550	7,9,14,28,32	31,31,32,32,17	0.2385	3.2969	181.4738	80.4974	0.9575	32
9	0.424	3.7150	7,9,14,32,37	32,32,31,31,31	0.2120	3.5972	194.6830	94.1552	0.9535	32
10	0.381	4.0865	7,9,14,17,28	17,32,31,17,32	0.1905	4.0194	213.2629	123.4266	0.9482	16
11	0.459	3.1578	9,14,28,33,36	31,17,16,32,32	0.2295	2.9908	168.0031	62.5456	0.9642	17
12	0.390	3.3435	7,9,14,28,36	17,17,32,32,32	0.1950	3.2243	178.2790	75.8388	0.9602	17
13	0.494	2.9720	9,14,28,33,36	17,17,31,32,32	0.2470	2.7800	158.7290	55.0196	0.9676	17
14	0.355	3.5293	7,9,14,32,37	31,31,32,32,31	0.1775	3.4400	187.7665	88.2137	0.9525	32
15	0.433	3.6407	7,9,14,32,37	31,17,31,31,32	0.2165	3.5140	191.0230	89.7753	0.9535	32
16	0.321	2.9720	9,14,28,33,36	31,32,17,17,32	0.1605	2.8708	162.7243	59.3213	0.9654	17
17	0.329	3.3435	7,9,14,28,32	32,31,32,17,17	0.1645	3.2566	179.7004	77.6424	0.9577	32
18	0.303	2.7863	9,14,28,33,36	32,17,32,16,17	0.1515	2.6859	154.5867	51.1264	0.9672	17
19	0.364	4.4580	7,9,14,28,36	32,31,17,17,32	0.1820	4.4151	230.6737	139.1266	0.9463	17
20	0.373	4.0865	7,9,14,17,28	17,17,32,32,32	0.1865	4.0237	213.4523	123.7320	0.9482	16
21	0.260	3.7150	7,9,14,32,37	32,31,31,32,31	0.1300	3.6850	198.5474	99.9832	0.9511	32
22	0.338	3.3435	7,9,14,28,32	32,32,17,17,31	0.1690	3.2519	179.4905	77.3720	0.9580	32
23	0.312	2.6005	9,14,17,28,33	32,17,32,16,17	0.1560	2.4876	145.8624	43.0976	0.9720	16
24	0.346	2.6005	9,14,17,28,33	32,32,31,16,17	0.1730	2.4699	145.0840	42.4053	0.9723	16

5.2 Performance investigation of microgrid

5.2.1 WES integrated with microgrid

This test case was conducted out to see how well a microgrid performed using just WES in the grid. Only five WES, each with a 100 kW installed capacity are allowed in the microgrid, it shows the Fig. 5 according to the optimal WES analysis. The lifetime, installation cost, maintenance cost parameters for the WES cost function are assumed to be 12.5 years, it can occupy 2500 dollars per kW, and 372.57 dollars per kW-year respectively. The proposed hybrid method uses five factors for reconfiguration and five more for determining the best position for WES in the microgrid. The proposed algorithm is run with 50 iterations and 50 populations in mind.

Fig. 5

The Optimal Structure with WES.

Fig. 6

Comparison of Bus Voltages with WES under Different Configurations.

The resultant configuration improves the bus voltage when compared to the other configurations, as seen in Fig. 6. More particular, all load buses’ bus voltages are kept over the minimum voltage limit, with the 32^nd bus recording the lowest voltage of 0.9535 p.u. After integrating WES, the system’s transmission loss is decreased to 94.15 kW, and the overall generation cost is 194.68$/hr. Among the total of 5 WES, the 3 numbers of WES have the best placement on bus 31, and the 2 numbers of WES have the best location on bus 32. According to the findings, power generation via the WES is efficient and allows the system to handle additional loads. The WES, moreover, lowers the total cost of emissions. Furthermore, the convergence characteristics of several algorithms are investigated in order to assess the efficacy of the suggested method when the algorithms are run for the same iteration and population values. The convergence of the considered targets, such as TGC, TEC, and VD, is depicted separately in Figs. 7, 8, and 9, respectively. The suggested algorithm gets the optimal solution at the 35^th iteration with better convergence than the other techniques in the literature, as shown in the figures. Furthermore, it achieves a near-optimal solution by the 20th iteration.

Fig. 7

Convergence of the Different Algorithms for TGC.

Fig. 8

Convergence of the Different Algorithms for TEC.

Fig. 9

Convergence of the Different Algorithms for VD.

Table 3

Optimal solution with PVES under base load condition

Optimal	PVES optimal	Total Cost	Power Loss	Min Voltage	Bus
Configuration	location	in $/hr	in kW	in p.u	Number
7,9,14,32,37	31,31,31,31,32	235.06	108.67	0.9476	32

The algorithms DGA [24] and PSO [25] do not produce the best solution for the given problem. The proposed hybrid algorithm’s strength is demonstrated by keeping the test system running under dynamic load. The discovered ideal positions for WES in majority of the solutions presented in Table 2 are 16, 17, 31, and 32, and the NO’s for the optimal structure are S₇, S₉, S₁₄, S₃₂, and S₃₇.

5.2.2 PVES integrated with microgrid

Only PVES are present in the microgrid in this test instance. Only five PVES, each with a 100 kW installed capacity, are available for power generation, as in the prior test case. The suggested hybrid algorithm is optimized using the same boundary conditions as before. The lifetime, installation cost, and maintenance cost parameters for the WES cost function are assumed to be 20 years, 7000 dollars per kW, and 712.92 dollars per kW-year, respectively. After the proposed technique was successfully implemented, the optimal solution was found in the 33^rd iteration. In the presence of PVES, results are achieved for the microgrid. Table 3 shows the power loss reduction, ideal configuration, optimal PVES placements, and the configuration’s minimal voltage [37, 38].

The bus voltages and branch currents achieved in the optimal design are compared to the other system configurations depicted in Figs. 10 & 11. In comparison to the other two configurations, the bus voltages are better, and the branch current loading is lowered, according to the figures. Cons was in charge of the optimization procedure.

Fig. 10

Comparison of bus voltages with PVES under different configurations.

Fig. 11

Comparison of branch currents with PVES under different configurations.

The acquired findings under varied load situations are summarized in Table 4. Table 4 shows that the optimal PVES sites are 16, 17, 18, 31, and 32 in the majority of the found solutions, with the NO’s for the optimal structure being S7, S9, S14, S32, and S37.

Table 4

Summary of results with PVES under daily load pattern

Hrs.	% Efficiency	Total	Configuration	Optimal	P_PVES	P_Grid	TGC in	Loss in	Min.	Bus
	of PVES	Load		PVES	in	in	$/hr	kW	Voltage
		in MW		location	MW	MW			in p.u
1	0.000	3.5293	7,9,14,28,36	29,27,32,32,17	0.0000	3.6304	227.3270	101.1755	0.9523	32
2	0.000	3.3435	7,9,14,28,36	32,32,32,16,11	0.0000	3.4319	218.5910	88.3783	0.9549	17
3	0.000	2.9720	9,14,28,33,36	18,17,31,26,16	0.0000	3.0404	201.3638	68.3517	0.9613	17
4	0.000	2.7863	9,14,28,33,36	29,32,30,12,32	0.0000	2.8453	192.7812	59.0420	0.9618	17
5	0.000	2.6005	9,14,17,28,33	24,13,10,29,8	0.0000	2.6508	184.2233	50.2951	0.9672	17
6	0.000	3.6407	7,9,14,32,37	32,32,10,32,32	0.0000	3.7465	232.4324	105.7571	0.9474	32
7	0.002	3.5293	7,9,14,28,36	32,32,32,32,32	0.0010	3.6293	227.2796	101.0982	0.9524	32
8	0.008	3.4550	7,9,14,28,36	32,32,32,32,32	0.0040	3.5471	223.6625	96.1910	0.9541	32
9	0.035	3.7150	7,9,14,32,37	31,31,31,31,32	0.0175	3.8062	235.0599	108.6726	0.9476	32
10	0.100	4.0865	7,9,14,28,36	17,17,16,17,32	0.0500	4.1714	251.1311	134.9264	0.9421	17
11	0.230	3.1578	9,14,28,33,36	17,32,17,16,32	0.1150	3.1114	204.4917	68.6907	0.9626	17
12	0.233	3.3435	7,9,14,28,32	32,32,17,17,32	0.1165	3.3076	213.1236	80.6191	0.9567	32
13	0.318	2.9720	9,14,28,33,36	31,17,32,17,32	0.1590	2.8724	193.9739	59.3993	0.9654	17
14	0.433	3.5293	7,9,14,32,37	31,31,32,17,31	0.2165	3.3983	217.1146	85.5739	0.9535	32
15	0.370	3.6407	7,9,14,32,37	31,32,31,32,31	0.1850	3.5476	223.6836	91.9197	0.9528	32
16	0.403	2.9720	9,14,28,33,36	32,17,32,31,17	0.2015	2.8277	192.0086	57.2335	0.966	17
17	0.330	3.3435	7,9,14,28,32	32,32,31,17,17	0.1650	3.2561	210.8573	77.6123	0.9578	32
18	0.238	2.7863	9,14,28,33,36	16,32,17,17,16	0.1190	2.7199	187.2650	52.6733	0.9672	17
19	0.133	4.4580	7,9,14,28,36	32,32,17,17,32	0.0665	4.5404	267.3662	148.9057	0.9431	17
20	0.043	4.0865	7,9,14,28,36	16,32,17,17,17	0.0215	4.2024	252.4961	137.4499	0.9405	17
21	0.003	3.7150	7,9,14,32,37	31,31,31,31,31	0.0015	3.8235	235.8211	109.9725	0.9474	32
22	0.000	3.3435	7,9,14,28,36	32,11,32,24,14	0.0000	3.4319	218.5910	88.3783	0.9549	17
23	0.000	2.6005	9,14,17,28,33	31,11,32,32,12	0.0000	2.6508	184.2233	50.2951	0.9672	17
24	0.000	2.6005	9,14,17,28,33	29,23,31,32,13	0.0000	2.6508	184.2233	50.2951	0.9672	17

5.2.3 Combined WES and PVES Integration with microgrid

The various combinations of WES and PVES are studied in this test scenario by constraining a total of five numbers of ES for power generation. With the suggested algorithm, the optimization was carried out with a population size of 50 and a maximum iteration of 100. The investigation is carried out under various load demands over the course of a day, taking into account the equivalent efficiency of WES and PVES in an hour. For all combinations with varied characteristic criteria, the suggested hybrid algorithm provides the best result. The best results are collected for various combinations of WES and PVES under various load circumstances [39 –42]. Table 5 shows the microgrid’s true power loss, total generating cost, and minimum bus voltage. The following observations were made as a result of the research: For many of the ES combinations, the best configurations are nearly identical under all daytime situations.

Table 5
Summary of results under combined WES and PVES integration

Hrs. (WES:4, PVES:1) (WES:3, PVES:2) (WES:2, PVES:3) (WES:1, PVES:4)

Loss TGC Min. Loss TGC Min. Loss TGC Min. Loss TGC Min.

(kW) ($/hr) Voltage (kW) ($/hr) Voltage (kW) ($/hr) Voltage (kW) ($/hr) Voltage

in p.u in p.u in p.u in p.u

1 90.4450 195.5043 0.9526 93.0794 203.4579 0.9500 95.7262 211.4120 0.9526 98.3647 219.3657 0.9500

2 81.2321 188.6332 0.9560 82.9850 196.1211 0.9567 84.7318 203.6088 0.9567 86.5383 211.0992 0.9567

3 62.1899 171.4493 0.9645 63.6802 178.9257 0.9631 65.1886 186.4029 0.9629 66.7549 193.8827 0.9625

4 53.9276 163.4936 0.9660 55.1014 170.8109 0.9659 56.3435 178.1312 0.9645 57.6568 185.4546 0.9632

5 44.4257 153.5297 0.9708 45.8211 161.1999 0.9699 47.2326 168.8709 0.9697 48.7218 176.5452 0.9685

6 95.7087 201.2557 0.9522 98.1131 209.0452 0.9498 100.5823 216.8375 0.9498 103.1584 224.6345 0.9498

7 87.3356 193.3875 0.9541 90.6370 201.8544 0.9509 94.0366 210.3257 0.9508 97.4709 218.7984 0.9508

8 83.3870 189.9006 0.9577 86.4000 198.3328 0.9542 89.5586 206.7714 0.9541 92.7972 215.2136 0.9546

9 96.8975 202.7513 0.9507 99.6706 210.8209 0.9510 102.5826 218.8967 0.9504 105.5833 226.9764 0.9484

10 125.6109 220.8314 0.9482 127.8717 228.4034 0.9482 130.1807 235.9774 0.9446 132.5208 243.5528 0.9440

11 63.7022 175.2977 0.9644 64.8959 182.5938 0.9643 66.1525 189.8928 0.9629 67.3974 197.1912 0.9629

12 76.7658 185.2467 0.9584 77.6960 192.2144 0.9573 78.6631 199.1838 0.9573 79.6365 206.1535 0.9562

13 55.8670 165.7767 0.9674 56.7357 172.8254 0.9665 57.6054 179.8741 0.9665 58.4973 186.9238 0.9661

14 87.6753 193.6357 0.9531 87.1453 199.5052 0.9530 86.6156 205.3747 0.9535 86.1035 211.2450 0.9530

15 90.2002 197.5550 0.9535 90.6263 204.0870 0.9531 91.0551 210.6191 0.9531 91.4870 217.1513 0.9528

16 58.8968 168.5808 0.9659 58.4769 174.4376 0.9659 58.0586 180.2944 0.9660 57.6455 186.1515 0.9664

17 77.6488 185.9323 0.9576 77.6304 192.1631 0.9578 77.6244 198.3945 0.9578 77.6183 204.6259 0.9578

18 51.4278 161.1220 0.9672 51.7440 167.6579 0.9670 52.0539 174.1936 0.9680 52.3654 180.7294 0.9676

19 141.0064 238.0089 0.9448 142.9366 245.3462 0.9447 144.8914 252.6847 0.9438 146.8809 260.0246 0.9431

20 126.3178 221.2541 0.9482 129.0053 229.0604 0.9481 131.7361 236.8686 0.9445 134.5194 244.6791 0.9428

21 101.9069 205.9989 0.9493 103.8470 213.4511 0.9493 105.8517 220.9061 0.9492 107.8988 228.3630 0.9475

22 79.4729 187.3062 0.9557 81.5796 195.1221 0.9557 83.7909 202.9426 0.9572 86.0674 210.7661 0.9571

23 44.4257 153.5297 0.9708 45.8197 161.1999 0.9698 47.2326 168.8709 0.9697 48.7218 176.5452 0.9685

24 43.8336 152.9052 0.9713 45.3610 160.7309 0.9701 46.9191 168.5579 0.9700 48.5554 176.3883 0.9686

Hrs.	(WES:4, PVES:1)	(WES:3, PVES:2)	(WES:2, PVES:3)	(WES:1, PVES:4)
1	90.4450	195.5043	0.9526	93.0794	203.4579	0.9500	95.7262	211.4120	0.9526	98.3647	219.3657	0.9500
2	81.2321	188.6332	0.9560	82.9850	196.1211	0.9567	84.7318	203.6088	0.9567	86.5383	211.0992	0.9567
3	62.1899	171.4493	0.9645	63.6802	178.9257	0.9631	65.1886	186.4029	0.9629	66.7549	193.8827	0.9625
4	53.9276	163.4936	0.9660	55.1014	170.8109	0.9659	56.3435	178.1312	0.9645	57.6568	185.4546	0.9632
5	44.4257	153.5297	0.9708	45.8211	161.1999	0.9699	47.2326	168.8709	0.9697	48.7218	176.5452	0.9685
6	95.7087	201.2557	0.9522	98.1131	209.0452	0.9498	100.5823	216.8375	0.9498	103.1584	224.6345	0.9498
7	87.3356	193.3875	0.9541	90.6370	201.8544	0.9509	94.0366	210.3257	0.9508	97.4709	218.7984	0.9508
8	83.3870	189.9006	0.9577	86.4000	198.3328	0.9542	89.5586	206.7714	0.9541	92.7972	215.2136	0.9546
9	96.8975	202.7513	0.9507	99.6706	210.8209	0.9510	102.5826	218.8967	0.9504	105.5833	226.9764	0.9484
10	125.6109	220.8314	0.9482	127.8717	228.4034	0.9482	130.1807	235.9774	0.9446	132.5208	243.5528	0.9440
11	63.7022	175.2977	0.9644	64.8959	182.5938	0.9643	66.1525	189.8928	0.9629	67.3974	197.1912	0.9629
12	76.7658	185.2467	0.9584	77.6960	192.2144	0.9573	78.6631	199.1838	0.9573	79.6365	206.1535	0.9562
13	55.8670	165.7767	0.9674	56.7357	172.8254	0.9665	57.6054	179.8741	0.9665	58.4973	186.9238	0.9661
14	87.6753	193.6357	0.9531	87.1453	199.5052	0.9530	86.6156	205.3747	0.9535	86.1035	211.2450	0.9530
15	90.2002	197.5550	0.9535	90.6263	204.0870	0.9531	91.0551	210.6191	0.9531	91.4870	217.1513	0.9528
16	58.8968	168.5808	0.9659	58.4769	174.4376	0.9659	58.0586	180.2944	0.9660	57.6455	186.1515	0.9664
17	77.6488	185.9323	0.9576	77.6304	192.1631	0.9578	77.6244	198.3945	0.9578	77.6183	204.6259	0.9578
18	51.4278	161.1220	0.9672	51.7440	167.6579	0.9670	52.0539	174.1936	0.9680	52.3654	180.7294	0.9676
19	141.0064	238.0089	0.9448	142.9366	245.3462	0.9447	144.8914	252.6847	0.9438	146.8809	260.0246	0.9431
20	126.3178	221.2541	0.9482	129.0053	229.0604	0.9481	131.7361	236.8686	0.9445	134.5194	244.6791	0.9428
21	101.9069	205.9989	0.9493	103.8470	213.4511	0.9493	105.8517	220.9061	0.9492	107.8988	228.3630	0.9475
22	79.4729	187.3062	0.9557	81.5796	195.1221	0.9557	83.7909	202.9426	0.9572	86.0674	210.7661	0.9571
23	44.4257	153.5297	0.9708	45.8197	161.1999	0.9698	47.2326	168.8709	0.9697	48.7218	176.5452	0.9685
24	43.8336	152.9052	0.9713	45.3610	160.7309	0.9701	46.9191	168.5579	0.9700	48.5554	176.3883	0.9686

Table 6

Summary of results under hybrid WES and PVES integration

Hrs.	(WES:3, PVES:2)				(WES:2, PVES:3)
	Loss	TGC	TEC	Min.	Loss	TGC	TEC	Min.
	(kW)	($/hr)	(lb./hr)	VD	(kW)	($/hr)	(lb./hr)	VD
1	54.06	194.22	5403.56	0.00355	54.06	198.51	5458.15	0.00355
2	46.96	185.74	5067.30	0.00324	46.96	194.05	5118.49	0.00324
3	34.80	168.87	4313.45	0.00272	34.80	177.17	4357.02	0.00272
4	29.64	160.48	3947.09	0.00235	29.64	168.78	3986.96	0.00235
5	24.95	152.10	3545.19	0.00241	24.95	160.40	3581.00	0.00241
6	58.64	199.32	5650.53	0.00375	58.64	207.63	5707.61	0.00375
7	54.06	194.22	5375.39	0.00355	54.06	202.53	5429.69	0.00355
8	51.14	190.83	5218.54	0.00346	51.14	199.13	5271.25	0.00346
9	61.83	202.73	5737.27	0.00390	61.83	211.03	5795.22	0.00390
10	70.35	219.44	6442.45	0.00432	70.35	227.75	6507.53	0.00432
11	40.60	177.30	4407.98	0.00288	40.60	185.60	4452.50	0.00288
12	46.96	185.74	4804.12	0.00324	46.96	194.05	4852.64	0.00324
13	34.80	168.87	3935.32	0.00272	34.80	177.17	3975.07	0.00272
14	54.06	194.22	5000.14	0.00355	54.06	202.53	5050.65	0.00355
15	58.64	199.32	5261.84	0.00375	58.64	207.63	5314.99	0.00375
16	34.80	168.87	3909.07	0.00272	34.80	177.17	3948.55	0.00272
17	46.96	185.74	4730.48	0.00324	46.96	194.05	4778.26	0.00324
18	29.64	160.48	3696.39	0.00235	29.64	168.78	3733.73	0.00235
19	98.38	237.00	7170.05	0.00536	98.38	245.30	7242.47	0.00536
20	70.35	219.44	6499.76	0.00432	70.35	227.75	6565.42	0.00432
21	61.83	202.73	5820.51	0.00390	61.83	211.03	5879.31	0.00390
22	46.96	185.74	5044.57	0.00324	46.96	194.05	5095.52	0.00324
23	24.95	152.10	3545.19	0.00241	24.95	160.40	3581.00	0.00241
24	24.95	152.10	3534.30	0.00241	24.95	160.40	3570.00	0.00241

Fig. 12

Comparison of power loss between case 4 and case 5.

Fig. 13

Comparison of generation cost between case 4 and case 5.

Regardless of the type of ES, i.e., WES or PVES, the best placements for the ES are nearly same.

By sharing load with the grid, the use of renewable energy cuts emissions significantly.

Because the generation cost of WES is lower than that of PVES, the overall system generation cost and real power loss drop as the number of WES increases.

When compared to PVES, WES has a higher per-day efficiency.

When it comes to lifetime, PVES are superior over WES.

5.2.4 Hybrid PV-DE and WES-DE integrated with microgrid

The efficiency of the RES is regarded a major issue when integrated into the linked microgrid, as evidenced by the results of the previous cases. PVES generates no electricity for roughly 12 hours a day and has a 25 percent efficiency for the remaining 8 hours of the day. PVES, on the other hand, would be far more efficient than WES during the summer months. In terms of WES, the installation necessitates a minimum appropriate wind velocity at the locations at all times of the day.

Overall, the RES’ delivery performance is subpar, despite the fact that they are installed in the best possible sites. When it comes to global warming and the depletion of traditional energy sources, however, renewable energy sources are the best option. As a result of these factors, the best way to improve performance while maintaining the benefits of RES has been determined. Many research projects involving hybrid DGs have recently been completed. The hybrid DG is made up of controllable DGs and renewable energy sources, and it ensures a consistent power supply at the Point of Common Coupling (PCC). When RES power generation is insufficient to meet demand, controlled DGs are used to provide the necessary supply. The performance of the RES can be improved in this way with the help of controllable DGs. The choice of the appropriate controlled DG is determined by the RES’s installed capacity. To test the performance improvement, the RES is replaced by hybrid DGs in this scenario. With the replacement, the hybrid DG ensures 100 kW of power generation throughout the day, regardless of the RES’ power supply performance. The studied two possible combinations of the preceding example are investigated with hybrid DGs for a better comparison of the solutions. The proposed method makes use of the identical boundary conditions as the previous example. The fitness function (32) is created using the equations (3–9) and all of the operational restrictions stated in equations (10–28) are included in the solution process along with the objectives.

The implementation is done for a 24-hour variable load pattern, and the results are shown in Table 6, which shows the power loss, total generating cost, total emission cost, and minimum bus voltage deviation of the best configuration. Under variable load situations, the compromised solution among the examined objectives is ensured. The collected results of all of the situations are compared in order to verify the performance boost of hybrid DGs. The incorporation of hybrid DGs into the grid, most critically, increases the penetration of DGs. The microgrid’s performance is improved due to the increased penetration of DGs. Figure 12 depicts the power loss of the final configuration achieved by the various examples over the course of a day.

The replacement of RES by hybrid DGs provides the best option with the least amount of power loss at all times of the day. Furthermore, even at the best setups, all of the remaining situations are useless due to the RES’s low delivery performance. In Fig. 13, the overall producing cost of the scenarios is compared. When hybrid DGs are linked with an interconnected microgrid, the total generating cost is significantly reduced. In particular, the hybrid WES has a lower generating cost than the hybrid PVES, which can be reversed for plants with higher capacity. Additionally, because hybrid DGs emit dangerous gases, which is unavoidable when addressing global warming issues, this work analyzes the optimization of the total emission cost of the microgrids. The relative performance benefits of WES and PVES are assessed using the predicted daily load curve for the next day and the forecasted daily power generation curve of WES and PVES. The annual anticipated power generation of WES and PVES, which varies seasonally, can be used to analyze this study.

6 Conclusion and future work

In this research aspect to integrate hybrid DGs performance level and reconfiguration microgrid has been improved. The detailed analysis was carried out with the three key objective functions of total generation cost minimize, total emission cost minimize, and voltage deviation minimize in this manuscript. The fuzzy is integrated with TLA to deal with multi-objective issues. It is used to optimize and reconfigurable location of DGs. At present the investigation on IEEE 33 buses configured in four categories such as microgrid incorporate with WES, microgrid incorporate with PVES, microgrid incorporate with WES / PVES, and microgrid incorporate with hybrid DGs.

In addition to that, a 24-hour load dispatch energy system is under operating, an hour-to-hour shifting daily pattern was investigated. The simulation results show that the proposed method achieves the best outcome, and the optimized structure is clearly enhanced to the present structure. In additionally taken as various combinations of DGs in order to determine the optimum solutions. Most crucially, the value of hybrid DGs over stand-alone DGs was realized. This work can be improved by taking into account the microgrid’s non-linear and unbalanced load situations. Also, a hybrid DG framework used to block-chain technology can be elevated through maximizing customer benefits. The impact of other controllable DGs, as well as renewable energy sources can also be investigated. DGs are used to examine the solution to the microgrid under abnormal situations.

References

Zhao

, Wang

, Zhao

, Lin

, Zhou

, Wang

, A review of active management for distribution networks: current status and future development trends, Electric Power Components and Systems 42 (2014), 280–293.

Niknam

, Kavousi-Fard

, Optimal energy management of smart renewable micro-grids in the reconfigurable systems using adaptive harmony search algorithm, International Journal of Bio-Inspired Computation 8 (2016), 184–194.

Gazijahani

F.S.

, Salehi

, Stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids, IET Renewable Power Generation 11 (2017), 1749–1759.

Wang

, Chen

, Wang

, Kim

, Begovic

M.M.

, Robust optimization based optimal DG placement in microgrids, IEEE Transactions on Smart Grid 5 (2014), 2173–2182.

Khorram-Nia

, Bahmani-Firouzi

, Simab

, Optimal switching in reconfigurable microgrids considering electric vehicles and renewable energy sources, Journal of Renewable and Sustainable Energy 10 (2018), 0459051–18.

, Wang

, Qu

, A hierarchical framework for generation scheduling of microgrids, IEEE Transactions on Power Delivery 29 (2014), 2448–2457.

El-salam

, Mirna

E.B.

, Eteiba

, A new hybrid technique for minimizing power losses in a distribution system by optimal sizing and siting of distributed generators with network reconfiguration, Energies 11 (2018), 3351.

Badran

, Mekhilef

, Mokhlis

, Dahalan

, Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies, Renewable and Sustainable Energy Reviews 73 (2017), 854–867.

Jabbari-Sabet

, Moghaddas-Tafreshi

S.-M.

, Mirhoseini

S.-S.

, Microgrid operation and management using probabilistic reconfiguration and unit commitment, International Journal of Electrical Power & Energy Systems 75 (2016), 328–336.

10.

Sedighizadeh

, Shaghaghi-shahr

, Esmaili

, Reza

, Aghamohammadi, Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties, Journal of Energy Storage 21 (2019), 58–71.

11.

Huang

Y.C.

, Enhanced-genetic-algorithm-based fuzzy multi-objective approach to distribution network reconfiguration, IEE Proceedings-Generation, Transmission and Distribution 149 (2002), 615–620.

12.

, Wang

, Gooi

H.B.

, Ye

, Wu

, Multi-objective optimal dispatch of microgrid under uncertainties via interval optimization, IEEE Transactions on Smart Grid 10 (2019), 2046–2058.

13.

, Xu

, Zhou

, Lee

W.-J.

, Zhao

, Stochastic optimal operation of microgrid based on chaotic binary particle swarm optimization, IEEE Transactions on Smart Grid 7 (2016), 66–73.

14.

Niknam

, Azizipanah-Abarghooee

, Narimani

M.R.

, An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation, Applied Energy 99 (2012), 455–470.

15.

Gazijahani

F.S.

, Salehi

, Robust design of microgrids with reconfigurable topology under severe uncertainty, IEEE Transactions on Sustainable Energy 9 (2018), 559–569.

16.

Kavousi-Fard

, Khodaei

, Efficient integration of plug-in electric vehicles via reconfigurable microgrids, Energy 111 (2016), 653–663.

17.

Dall’Anese

, Giannakis

G.B.

, Risk-constrained microgrid reconfiguration using group sparsity, IEEE Transactions on Sustainable Energy 5 (2014), 1415–1425.

18.

Nikmehr

, Najafi-Ravadanegh

, Khodaei

, Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty, Applied Energy 198 (2017), 267–279.

19.

Golshannavaz

, Afsharnia

, Siano

, A comprehensive stochastic energy management system in reconfigurable microgrids, International Journal of Energy Research 40 (2016), 1518–1531.

20.

Hemmati

, Mohammadi-Ivatloo

, Ghasemzadeh

, Reihani

, Risk-based optimal scheduling of reconfigurable smart renewable energy based microgrids, International Journal of Electrical Power & Energy Systems 101 (2018), 415–428.

21.

Bornapour

, Hooshmand

R.-A.

, Khodabakhshian

, Parastegari

, Optimal stochastic scheduling of CHP-PEMFC, WT, PV units and hydrogen storage in reconfigurable micro grids considering reliability enhancement, Energy Conversion and Management 150 (2017), 725–741.

22.

Maulik

, Das

, Optimal operation of a droop-controlled DCMG with generation and load uncertainties, IET Generation, Transmission & Distribution 12 (2018), 2905–2917.

23.

Kanwar

, Gupta

, Niazi

K.R.

, Swarnkar

, Optimal distributed resource planning for microgrids under uncertain environment, IET Renewable Power Generation 12 (2018), 244–251.

24.

Nafisi

, Farahani

, Abyaneh

H.A.

, Abedi

, Optimal daily scheduling of reconfiguration based on minimisation of the cost of energy losses and switching operations in microgrids, IET Generation, Transmission & Distribution 9 (2015), 513–522.

25.

Gazijahani

F.S.

, Ravadanegh

S.N.

, Salehi

, Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies, ISA Transactions 73 (2018), 100–111.

26.

Chen

, Duan

, Cai

, Liu

, Hu

, Smart energy management system for optimal microgrid economic operation, IET Renewable Power Generation 5 (2011), 258–267.

27.

Mohammadi

, Soleymani

, Mozafari

, Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices, International Journal of Electrical Power & Energy Systems 54 (2014), 525–535.

28.

Jithendranath

, Das

, Scenario-based multi-objective optimisation with loadability in islanded microgrids considering load and renewable generation uncertainties, IET Renewable Power Generation 13 (2019), 785–800.

29.

Gabbar

H.A.

, Zidan

, Optimal scheduling of interconnected micro energy grids with multiple fuel options, Sustainable Energy, Grids and Networks 7 (2016), 80–89.

30.

Chen

, Zhang

, Li

, Zhang

, Liu

, Zhao

, Zhang

, Optimal sizing for grid-tied microgrids with consideration of joint optimization of planning and operation, IEEE Transactions on Sustainable Energy 9 (2018), 237–248.

31.

McKenna

, Olsen

T.L.

Performance and Economics of a Wind-Diesel Hybrid Energy System: Naval Air Landing Field, San Clemente Island, California, DOE Strategic Environmental Development Research Program, National Renewable Energy Laboratory, 1999.

32.

Oviroh

P.O.

, Jen

T.-C.

, The energy cost analysis of hybrid systems and Diesel Generators in powering selected base transceiver station locations in Nigeria, Energies 11 (2018), 687.

33.

Mariaraja

, Manigandan

, Thiruvenkadam

, An expert system for distribution system reconfiguration through fuzzy logic and flower pollination algorithm, Measurement and Control 51 (2018), 371–382.

34.

Adel

, El-Ela

, El-Sehiemy

R.A.

, Abbas

A.S.

, Optimal placement and sizing of distributed generation and capacitor banks in distribution systems using water cycle algorithm, IEEE Syst J 12(4) (2018), 3629–3636.

35.

, Wang

, Gooi

H.B.

, Ye

, Wu

, Multi-Objective Optimal Dispatch of Microgrid Under Uncertainties via Interval Optimization, IEEE Transactions on Smart Grid 10(2) (2019), 2046–2058 10.1109/TSG.2017.2787790.

36.

Rao

R.V.

, Savsani

V.J.

, Vakharia

D.P.

, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems, Computer-Aided Design 43 (2011), 303–315.

37.

Albert

J.R.

, Stonier

A.A.

, Design and development of symmetrical super-lift DC–AC converter using firefly algorithm for solar-photovoltaic applications, IET Circuits Devices Syst 14(3) (2020), 261–269. https://doi.org/10.1049/iet-cds.2018.5292

38.

Albert

J.R.

, Vanaja

D.S.

Solar Energy Assessment in Various Regions of Indian Sub-continent, Solar Cells & Theory, Materials and Recent Advances, IntechOpen (December 10th 2020). 10.5772/intechopen.95118

39.

Kaliannan

, Albert

J.R.

, Muhamadha Begam

and Madhumathi

, Power Quality Improvement in Modular Multilevel Inverter Using for Different Multicarrier PWM, European Journal of Electrical Engineering and Computer Science 5(02) (2021), 19–27. DOI: https://dx-doi-org.web.bisu.edu.cn/10.24018/ejece.2021.5.2.315

40.

Dhivya

, Renoald

A.J.

, Fuzzy Grammar Based Hybrid Split-Capacitors and Split Inductors Applied In Positive Output Luo-Converters, International Journal of Scientific Research in Science, Engineering and Technology (3) (2017), 327–332. http://ijsrset.com/IJSRSET173174

41.

Renoald

A.J.

, Hemalatha

, Punitha

, Sasikala

, Solar Roadways-The Future Rebuilding Infrastructure and Economy, International Journal of Electrical and Electronics Research 4(2) (2016), 14–19. https://researchpublish.com/journal-details/IJEER

42.

Santhiya

, Devimuppudathi

, Santhosh

, Kumar and A.J. Renold, Real Time Speed Control of Three Phase Induction Motor by Using Lab View with Fuzzy Logic, Journal on Science Engineering and Technology 5(2) (2018), 21–27. http://jset.sasapublications.com/wp-content/uploads/2018/04/1841019.pdf