1 Introduction

Electrical power utilities need to guarantee that electrical power necessity from the consumer end is fulfilled in accordance with the reliable power quality and minimum cost. Due to increasing technological research, industrial development and population, the power demand increases. With increasing electrical power demand worldwide, the non-renewable energy sources are reducing day after day. To solve the problem of increasing electrical power demand should be fulfilled by clean renewable energy sources (RES). With the use of more renewable energy sources, the power generation can be increased which is the modern research scenario at the present time. In this research, there is a more use of distributed energy resources in a specific small area which is known as a microgrid. Microgrid consists of micro-sources (distribution generator, solar and wind units, etc.), battery storage and loads.

Every utilities desire that generation cost and emission value should be as least as possible, but both objectives are contradictory so cannot be achievable at a same time. In this paper term used Combined Economic Emission Dispatch (CEED) problem. In the past, there is only objective to minimize cost while generation of power, but now a big concern about saving environment and human health from pollution to rectify problem of global warming, so some rules are imposed on private and government utilities to reduce emission of toxic gases exhalation with possible least fuel cost [1].

Various conventional linear optimization methods were used to solve the Economic Load Dispatch (ELD) problem [2]: (a) lambda-iteration method, (b) gradient method, (c) linear programing method and (c) Newton’s method. Linear programing techniques are fast and reliable, but these methods are failed to obtain the optimal solution for solving highly complex nonlinear objective function.

Interior Search Algorithm (ISA) technique guarantees to obtain global solution, and algorithm has a capability to avoid local stagnation or local optima [3]. The multi-objective power system dispatch problem can be transformed into single objective by Scalarization methods (Priori Approach) using these techniques [1]:

  • Price penalty factor technique

  • Weighted sum method (WSM)

  • Goal Attainment method

  • Lexicographic method

The CEED problem consists of either single objective or multi-objective is solved using various algorithms such as: After Scalarization technique is applied, CEED problem can be classified into two forms with and without considering valve-point effect loading of generators further classified into equation used either quadratic and cubic equation to evaluate fuel cost and emission value. CEED problem can be solved without considering valve-point effect and with price penalty factors based approach is solved with various computational techniques [2].

The CEED problem is solved using “Max–Max” price penalty factor approach by various Artificial Intelligence (AI) techniques [4] consisting of Genetic Algorithm (GA), Evolutionary Programming (EP), Particle Swarm Optimizer (PSO) and Differential Evolution (DE) is applied on IEEE-30 bus system. “Max–Max” price penalty factor is also used to solve CEED problem with Gravitational Search Algorithm (GSA) [5], Parallelized PSO (PPSO) [6], Evolutionary Programming (EP), Micro-GA (MGA) [7], Assessment of available transfer capability for practical power system with CEED problem for IEEE-30 bus system with 6 generating units and Indian Utility System 62-bus (IUS-62) with nineteen generators [8]. Analytical solution for CEED problem with IUS-62 with six generators, comparative study [9] with “Min–Max” price penalty factor using PSO and Lagrange’s Algorithm (LA), with LA [10] and PSO [11] taking “Min–Max” and “Max–Max” price penalty factors approach CEED problem is solved. Lagrange’s algorithm is used to solve CEED problem with four penalty factors [12] with quadratic equation is considered for evaluating fuel cost and emission value, six penalty factors with cubic equation [13] used for the calculation of CEED problem. Scenario-based dynamic economic emission dispatch problem is solved by Fuzzy adaptive improved PSO (FAIPSO) [14]. CEED problem with valve-point effect is solved by using “Min–Max” and “Max–Max” price penalty factors approach with LA [15], Maclaurin series-based Lagrangian method [16], Opposition-based GSA (OGSA) [17].

Various types of economic dispatch problem are solved with weighted sum method (WSM) using PSO [18]. CEED problem with WSM technique is solved using Artificial Bee Colony (ABC) algorithm with Dynamic Population size (ABCDP) [19] algorithm and opposition-based harmony search algorithm (OHS) [20]. Hybridization of PSO and GSA computational techniques with weighted sum method considers valve-point effect [21] for CEED problem solution. Neural network, Fuzzy system and Lagrange’s algorithm (LA) [22] for single- and multi-area dispatch problem investigate Emission Standards [23], Location of Greenhouse gases (GHG) emission from thermal power plant in India [24], Dispatch problem on different power system using Stochastic algorithm [25, 26], Security-constrained economic scheduling of generation considering generator constraints [27, 28], Integration of solar and coal-fired plant [29].

Finally, the future of economic environmental emission dispatch problem is multi-objective (such as: fuel cost, emission value, CEED fuel cost, different gases exhalation) considering at a single time to find actual operating point of generators to fulfil all objectives efficiently. Multi-objective thermal power dispatch [30], considering more than one objective for CEED problem, is solved using various computational techniques such as: multi-objective DE (MODE) [31], MOGSA [32], modified non-dominated sorting genetic algorithm-II (MNSGA-II) [33], NSGA-II with valve-point effect [34], BB-MOPSO [35], hybrid multi-objective optimization algorithm based on PSO and DE (MO-DE/PSO) [36], multi-objective particle swarm optimization algorithm proposed by Coello et al. (CMOPSO) [37], multi-objective particle swarm with the sigma method (SMOPSO) [38] and time variant multi-objective particle swarm optimization (TV-MOPSO) [39].

In this paper, the analysis of islanded mode microgrid (MG) is considered. The Combined Economic Emission Dispatch (CEED) is the procedure to scheduling the generating units within their bounds together with the minimization of fuel cost and emission [40]. The CEED is an elementary problem in the microgrid, which can be optimized by meta-heuristic optimization techniques like Ant Colony Optimization (ACO) [41] technique and Cuckoo Search Algorithm (CSA) [42]. Hence, for the solution of ELD and CEED problem, Interior Search Algorithm (ISA) [43, 44] is used. Many optimization strategies have been incorporated into the basic algorithm, such as chaotic theory [53, 54], Stud [55], quantum theory [56], Lévy flights [57, 58], multi-stage optimization [59] and opposition-based learning [60]. Many other excellent meta-heuristic algorithms have been proposed, such as monarch butterfly optimization (MBO) [61, 62], earthworm optimization algorithm (EWA) [63], elephant herding optimization (EHO) [64], moth search (MS) algorithm [65].

This paper Structure is, Sect. 1: Paper introduction, Sect. 2: Microgrid structure, Sect. 3: Mathematical model of isolated mode microgrid, Sect. 4: Interior Search Algorithm, Sect. 5: Data of microgrid, Sect. 6: Results of microgrid and Sect. 7: Conclusion.

2 Microgrid structure

Microgrid is modern micro-scale power system of the centralized electricity for a small community such as villages and commercial area [45]. A microgrid is consummate specific purposes like reliability, cost reduction, emission reduction, efficiency improvement, use of renewable sources and continuous energy source [46]. Figure 1 displays a microgrid including every distributed energy sources, and all loads are coupled to the main grid. Microgrid consists of DG units like wind unit, solar unit, hydro unit, biomass unit, natural gas generator, diesel generator, combined heat and power(CHP) and battery energy storage. The microgrid also connected different types of loads like agriculture, industrial, commercial, residential, university and vehicle charging. The microgrid is connected to the micro-sources and supply produced power to the different loads through the point of common coupling (PCC) [47].

Fig. 1
figure 1

Microgrid structure

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The main advantage of a microgrid is to combine all benefits of renewable energy sources to reduce the carbon generation and power generation efficiency improvement. Microgrid has two modes of connection: first is Grid coupled mode, and second is isolated mode [48]. In the first mode, microgrid is connected to the main grid via PCC. In the isolated mode, a microgrid is not connected from the main grid.

Micro-source controllers used in microgrid control the micro-source and loads. In the isolated mode, microgrid is isolated from the utility grid and delivers power to the important loads. Rating of these critical loads is considered equal to 240 MW [40].

Figure 2 explains why there is a need of microgrid in power system. Microgrid is an answer of energy crisis in the power system [45]. Reduced transmission loss to the DERs (microgrid) connection of transmission line in different location. Power generation cost is reduced using distributed energy resources in microgrid as well as microgrid uses many renewable energy resources. Environmental emission is more reduced to be using microgrid in power system and achieve high quality and reliable energy supply to the critical loads.

Fig. 2
figure 2

Why need microgrid in the power system

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3 Mathematical model of isolated mode microgrid

3.1 Generator fuel cost function

The main objective of the Economic Load Dispatch (ELD) problem solution is to examine the generation levels of every on-line unit which decreases the total generation fuel cost and reduces the emission level of the system, together with satisfying a system constraint [47]. The objective of ELD is to reduce the generation fuel cost together with satisfying the power demand of a modern power system during a given duration of time considering the power system operating constraints. The ELD problem fuel cost function of Generators quadratic equation is [49]:

$${\text{Min}}(F_{C} ) = \sum\limits_{i = 1}^{NG} {u_{i} P_{i}^{2} + v_{i} P_{i} + w_{i} }$$
(1)

where F C  = Total fuel cost, NG Number of generators, P i  = Active power generation of ith generator, u i  = Cost coefficient of ith generator in [$/MW2h], v i  = Cost coefficient of ith generator in [$/MWh], w i  = Cost coefficient of ith generator in [$/h].

The various pollutants like carbon dioxide, sulphur dioxide and nitrogen oxide are released as a result of the operation of the diesel generator, gas generator, CHP [1, 2]. Reduction of these pollutants is compulsory for every generating unit. To achieve this goal, new criteria are included in the formulation of the Emission Dispatch problem as follows.

$$E_{T} = \sum\limits_{i = 1}^{n} {\left( {x_{i} P_{i}^{2} + y_{i} P_{i} + z_{i} } \right)}$$
(2)

where E T  = Total Emission Value, x i  = Emission coefficient of ith generator in [kg/MW2h], y i  = Emission coefficient of ith generator in [kg/MWh], z i  = Emission coefficient of ith generator in [kg/h].

Price Penalty Factor (PPF) hi is used to convert multi-objective CEED problem into a single-objective optimization problem [1].

$$F_{T} = \sum\limits_{i = 1}^{n} {\left[ {\left( {u_{i} P_{i}^{2} + v_{i} P_{i} + w_{i} } \right) + h_{i} \left( {x_{i} P_{i}^{2} + y_{i} P_{i} + z_{i} } \right)} \right]}$$
(3)

where F T  = Total CEED Cost, h i  = Price Penalty Factor (PPF).

The function of PPF is to transfer the physical sense of emission measure from the mass of the emission to the fuel cost for the emission. The variance among these penalty factors is in the fuel cost mass for emission in the last optimal fuel cost for generation and emission. The PPF for multi-objective ELD problem is formulated taking the ratio of fuel cost to emission value of the corresponding generators as follows [13, 15].

Min–Max price penalty factor is formulated as:

$$h_{i} = \frac{{\left( {u_{i} P_{i\rm {min} }^{2} + v_{i} P_{i\rm {min} } + w_{i} } \right)}}{{\left( {x_{i} P_{i\rm{max} }^{2} + y_{i} P_{i\rm {max} }^{{}} + z_{i} } \right)}}\quad (\$/{\text{h)}}$$
(4)

3.2 Solar generation prediction

The cost function is [48, 49]:

$$F(P_{\text{Solar}} ) = aI^{p} P_{\text{Solar}} + G^{E} P_{\text{Solar}}$$
(5)
$$a = \frac{r}{{[1 - [(1 + r)^{ - N} ]}}$$
(6)

where P Solar = Solar generation in [kW], r = Interest scale = 0.09, a = Annuitization coefficient, N = Investment duration = 20 years, I p = Ratio of Investment cost to unit establish power = 5000$/kW, G E = Operational cost and maintenance cost = 0.016$/kW.

The cost function for solar energy can be calculated as:

$$F(P_{\text{Solar}} ) = 5 4 7. 7 4 8 3*P_{\text{Solar}}$$
(7)

The 24 h’ data of solar generation are shown in Table 1. In this case, we have considered the solar generation data [50] of a location in the east coast of USA, as shown in Table 1.

Table 1 Solar generation
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3.3 Wind generation prediction

The cost function is [51]:

$$F(P_{\text{Wind}} ) = aI^{p} P_{\text{Wind}} + G^{E} P_{\text{Wind}}$$
(8)
$$a = \frac{r}{{[1 - [(1 + r)^{ - N} ]}}$$
(9)

where P Wind = Wind generation in [kW], r = Interest scale = 0.09, a = Annuitization coefficient, N = Investment duration = 20 years, I p = Ratio of Investment cost to unit establish power = 1400$/kW, G E = Operational cost and maintenance cost = 0.016$/kW.

The cost function for wind energy can be calculated as:

$$F(P_{\text{Wind}} ) = 1 5 3. 3 8 1 0*P_{\text{Wind}}$$
(10)

The 24 h’ data of wind generation are shown in Table 2. In this case, we have considered the wind generation data [50] of a location in the east coast of USA, as shown in Table 2.

Table 2 Wind generation
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3.4 Total cost of economic dispatch (ELD) and combined economic emission dispatch (CEED) in microgrid

3.4.1 Total cost of economic load dispatch (ELD)

$${\text{Min}}(F_{C} ) = \sum\limits_{i = 1}^{NG} {u_{i} P_{i}^{2} + v_{i} P_{i} + w_{i} } { + 153} . 3 8 1 0*P_{\text{Wind}} + 5 4 7. 7 4 8 3*P_{\text{Solar}}$$
(11)

3.4.2 Total cost of combined economic emission dispatch (CEED)

$$F_{T} = \sum\limits_{i = 1}^{n} {\left[ {\left( {u_{i} P_{i}^{2} + v_{i} P_{i} + w_{i} } \right) + h_{i} \left( {x_{i} P_{i}^{2} + y_{i} P_{i} + z_{i} } \right)} \right]} +\,153.3810*P_{\text{Wind}} + 547.7483*P_{\text{Solar}}$$
(12)

3.5 Constraint function

  1. (a)

    Isolated type of MG:

No trading of energy from the main grid [52].

  1. (b)

    Power Balance constraint:

$$P_{\text{Load}} = P_{1} + P_{2} + P_{3} + P_{4} + P_{5}$$
(13)
  1. (c)

    Power Generation constraint:

Each generator output bounded by minimum and maximum boundaries [52].

$$P_{i}^{\hbox{min} } \le P_{i} \le P_{i}^{\hbox{max} }$$
(14)

P max i  = Max. output power of ith generator, P min i  = Min. output power of ith generator.

4 Interior search algorithm

Interior Search Algorithm (ISA) technique guarantees to obtain global solution, and algorithm has a capability to avoid local stagnation or local optima [3]. ISA is a combined optimization analysis divine to the creative work or art relevant to interior or internal designing [3] consisting of two stages: first one is composition stage where a number of solutions are shifted towards to get optimum fitness. The second stage is reflector or mirror inspection method where the mirror is placed in the middle of every solution and best solution to yield a fancy view to design, satisfying all control variables to constrained design problem.

  1. 1.

    However, the position of acquired solution should be in the limitation of maximum bound and minimum bounds, later estimate their fitness amount [3].

  2. 2.

    To evaluate the best value of the solution, the fittest solution has maximum objective function whenever aim of the optimization problem is minimization and vice versa is always true. The solution has universally best in jth run (iteration).

  3. 3.

    Remaining solutions are collected in two categories mirror and composition elements with respect to a control parameter α. Elements are categorized based on the value of random number (all used in this paper) ranging [0, 1].

    Whether rand 1() is less than or equal to α, it moves to mirror category else moves towards composition category. For avoiding problems, α must be carefully tuned.

  4. 4.

    Being Composition category elements, every element or solution is, however, transformed as described below in the limited uncertain search space.

    $$x_{i}^{j} = lb^{j} + (ub^{j} - lb^{j} )*r_{2}$$
    (15)

    where x j i represents ith solution in jth run, ub j and lb j upper and lower ranges in jth run, whereas its maximum and minimum values for all elements exist in (j − 1)th run and \({\text{rand}}_{ 2} ()\) ranging [0, 1].

  5. 5.

    For ith solution in jth run, spot of mirror is described [43]:

    $$x_{m,i}^{j} = r_{2} x_{i}^{j - 1} + (1 - r_{3} )*x_{gb}^{j}$$
    (16)

    where rand 3() ranging [0, 1]. Imaginary position of solutions is dependent on the spot where mirror is situated defined as:

    $$x_{i}^{j} = 2x_{m,i}^{j} - x_{i}^{j - 1}$$
    (17)
  6. 6.

    It is auspicious for universally best to little movement in its position using uncertain walk defined:

    $$x_{gb}^{j} = x_{gb}^{j - 1} + r_{n} *\lambda$$
    (18)

    where r n a vector of distributed random numbers having the same dimension of x, λ = (0.01*(ub − lb)) scale vector, dependable on search space size.

  7. 7.

    Evaluate fitness amount of new position of elements and for its virtual images. Whether its fitness value is enhanced, then position should be updated for next design. For minimization optimization problem, updating are as follows [44]:

    $$x_{i}^{j} = \left\{ {_{{x_{i}^{j - 1} \ldots \ldots Else}}^{{x_{i}^{j} \ldots f\left( {x_{i}^{j} } \right) < f\left( {x_{i}^{j - 1} } \right)}} } \right.$$
    (19)
  8. 8.

    If termination condition not fulfilled, again evaluate from the second step.

  9. A.

    Parameter tuning

A curious component in algorithm is α. For unconstrained benchmark test function, it is almost fixed 0.25, but the requirement is to increase its value ranging [0, 1] randomly as the increment in a maximum number of runs selected for a particular problem. It requires shifting search emphasized from exploration stage to exploitation optimum solution towards termination of maximum iteration.

  1. B.

    Constraint manipulation

Evolutionary edge (boundary) constraint manipulation:

$$f\left( {z_{i} \to x_{i} } \right) = \left\{ {_{{r_{5} *ub_{i} + (1 - r_{5} )x_{gb,i} \ldots if \ldots z_{i} < ub_{i} }}^{{r_{4} *lb_{i} + (1 - r_{4} )x_{gb,i} \ldots if \ldots z_{i} < lb_{i} }} } \right.$$
(20)

where \(r_{{_{4} }}\) and r 5 = random numbers between [0, 1]. x gb,i  = Component of the global best solution.

  1. C.

    Nonlinear constraint manipulation

Nonlinear Constraint manipulations have following rules:

  1. I.

    Both solutions are possible, then consider one with best objective functional value.

  2. II.

    Both solutions are impossible, then consider one with less violation of constraints.

Evaluation of constraint violation:

$$V(x) = \sum\limits_{k = 1}^{nc} {\frac{{g_{k} (x)}}{{g_{\hbox{max} k} }}}$$
(21)

where nc = No. of constraints, g k (x) = k th constraint consisting problem, g maxk  = The maximum violation in kth constraint yet.

Control Parameter of ISA, CSA and ACO

Control parameter of ISA, CSA and ACO is Population Size: 40, Maximum Iteration (N): 500, Number of Variable (d): 3, Random Number (r): [0, 1].

Pseudo-code of Algorithm [ 44 ]

5 Data of microgrid

The minimum limit and maximum limit of the output power of all micro-sources are shown in Table 3.

Table 3 Generation of power min–max limits [40]
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Table 4 shows fuel cost coefficient of three generators.

Table 4 Fuel cost coefficient of three generators [48]
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Table 5 shows emission coefficient of three generators.

Table 5 Emission coefficient of three generators [48]
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Table 6 shows the system power demand for 24 h of a day.

Table 6 24-h load demand [50]
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6 Results of microgrid

6.1 All sources included

6.1.1 Without emission (ED)

Table 7 shows results of cost and generation of 24 h for the case when all sources included. This table also shows comparative study of generation cost obtained from CSA and ISA with respect to prior solved techniques RGM and ACO. Statistically aggregated 24-hour generation cost for ED case in comparative study clearly proves that lowest cost is obtained with ISA compared to other techniques.

Table 7 All sources included
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6.1.2 With emission (CEED)

Table 8 shows results of cost and generation of 24 h for the case when all sources included. This table also shows comparative study of generation cost obtained from CSA and ISA with respect to prior solved techniques RGM and ACO. Statistically aggregated 24-hour generation cost for CEED case in comparative study clearly proves that lowest cost is obtained with ISA compared to other techniques.

Table 8 All sources included
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Figure 3 shows the comparison of cost saving of ED and CEED using ISA with different algorithms like RGM, ACO and CSA. Aggregated cost saving for all sources included ISA with respect to GM, ACO and CSA is 20.70, 13.21 and 0.03%, respectively.

Fig. 3
figure 3

Comparison of ISA versus other techniques

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6.2 All sources without wind energy

6.2.1 Without emission (ED)

Table 9 shows results of cost and generation of 24 h for the case when all sources included without wind energy. This table also shows comparative study of generation cost obtained from CSA and ISA with respect to prior solved techniques RGM and ACO. Statistically aggregated 24-hour generation cost for ED case in comparative study clearly proves that lowest cost is obtained with ISA compared to other techniques.

Table 9 All sources without wind energy
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6.2.2 With emission (CEED)

Table 10 shows results of cost and generation of 24 h for the case when all sources without including wind energy. This table also shows comparative study of generation cost obtained from CSA and ISA with respect to prior solved techniques RGM and ACO. Statistically aggregated 24-hour generation cost for CEED case in comparative study clearly proves that lowest cost is obtained with ISA compared to other techniques.

Table 10 All sources without wind energy
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Figure 4 shows the comparison of cost saving of ED and CEED using ISA with different algorithms like RGM, ACO and CSA. Aggregated cost saving for all sources without including wind energy of ISA with respect to RGM, ACO and CSA is 18.52, 13.25 and 0.03%, respectively.

Fig. 4
figure 4

Comparison of ISA versus other techniques

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6.3 All sources without solar and wind energy

6.3.1 Without emission (ED)

Table 11 shows results of cost and generation of 24 h for the case when all sources without including solar and wind energy. This table also shows comparative study of generation cost obtained from CSA and ISA with respect to prior solved techniques RGM and ACO. Statistically aggregated 24-hour generation cost for ELD case in comparative study clearly proves that lowest cost is obtained with ISA compared to other techniques.

Table 11 All sources without solar and wind energy
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6.3.2 With emission (CEED)

Table 12 shows results of cost and generation of 24 h for the case when all sources without including solar and wind energy. This table also shows comparative study of generation cost obtained from CSA and ISA with respect to prior solved techniques RGM and ACO. Statistically aggregated 24-hour generation cost for CEED case in comparative study clearly proves that lowest cost is obtained with ISA compared to other techniques.

Table 12 All sources without solar and wind energy
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Figure 5 shows the comparison of cost saving of ED and CEED using ISA with different algorithms like RGM, ACO and CSA. Aggregated 24-hour cost saving for all sources without including solar and wind energy of ISA with respect to RGM, ACO and CSA is 15.8, 11.78 and 0.04%, respectively.

Fig. 5
figure 5

Comparison of ISA versus other techniques

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6.4 All sources without solar energy

6.4.1 Without emission (ED)

Table 13 shows results of cost and generation of 24 h for the case when all sources without including solar energy. This table also shows comparative study of generation cost obtained from CSA and ISA. Statistically aggregated 24-hour generation cost for ED case in comparative study clearly proves that lowest cost is obtained with ISA compared to CSA.

Table 13 All sources without solar energy
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6.4.2 With emission (CEED)

Table 14 shows results of cost and generation of 24 h for the case when all sources without including solar energy. This table also shows comparative study of generation cost obtained from CSA and ISA. Statistically aggregated 24-hour generation cost for CEED case in comparative study clearly proves that lowest cost is obtained with ISA compared to CSA.

Table 14 All sources without solar energy
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Figure 6 shows aggregated 24-hour cost saving for all sources without including solar energy of ISA with respect to CSA is 0.02%, respectively.

Fig. 6
figure 6

Comparison of ISA versus cuckoo search algorithm

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As shown in Fig. 7, total CEED cost using interior search algorithm for four different cases like all sources included, all sources except solar and wind, all sources except wind energy and all sources exc ept solar energy. Figure 7 shows that all sources included scenarios cost to be minimum compared to other scenarios.

Fig. 7
figure 7

Total CEED cost using interior search algorithm

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7 Conclusion

The key objective of this work is to solve the Economic Load Dispatch (ELD) and Combined Economic Emission Dispatch (CEED) problem to obtain optimal system cost in isolated microgrid mode. The minimization of total ELD cost and total CEED cost is obtained with four different scenarios like all sources included, all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In the above scenarios, the result of ELD and CEED cost is calculated with Interior Search Algorithm (ISA) and compared with Reduced Gradient Method (RGM), Ant Colony Optimization (ACO) technique and Cuckoo Search Algorithm (CSA) considering two different cases with and without emission. The results obtained to ISA give comparatively better cost reduction as compared with RGM, ACO and CSA which shows the effectiveness of the given algorithm. The future work includes the grid-connected mode CEED problem optimization and also in the microgrid optimization of energy, achieves maximum reliability and efficiency.