**1. Introduction**

Due to the continuous increase in power demand and rapid depletion of fossil fuels, researchers all over the world have no other option but to look for alternative energy sources by utilizing small-scale distributed power generation (DG) and energy storage systems (ESS) [1]. However, due to the inherent intermittency and volatility of renewable energy sources (RESs), its large-scale integration into the power system will increase the regulation

**Citation:** Abou El-Ela, A.A.; El-Sehiemy, R.A.; Allam, S.M.; Shaheen, A.M.; Nagem, N.A.; Sharaf, A.M. Renewable Energy Micro-Grid Interfacing: Economic and Environmental Issues. *Electronics* **2022**, *11*, 815. https://doi.org/ 10.3390/electronics11050815

Academic Editor: José Matas

Received: 6 February 2022 Accepted: 3 March 2022 Published: 5 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

burden and affect the security operation of the main grid. The microgrid is defined as smallscale controllable electrical distribution systems, which have the important advantage of operating either islanded or interconnected to the main grid. Microgrids (MGs) are usually composed of Distributed Energy Resources (DERs), ESSs, and controllable loads [2]. The DERs are based on conventional resources, such as diesel generators and RESs such as photovoltaics (PV) and wind turbines (WT) [3–6]. There are two modes of operation for MGs. In a grid-connected operation, the MGs draw/supply power from/to the grid based on load and generation conditions with regard to the market prices. On the other hand, it will be disconnected from the grid to provide electricity to associated critical loads in the event of faults [7].

In the first mode of operation, the MGs should be operated economically and reliably, where a supervisory control and data acquisition (SCADA) system is activated to monitor, control, and dispatch all DERs to guarantee the economic and secure operation of the MGs. For this targeted operation of the MGs, various conventional and artificial intelligent programming techniques were applied for dispatching the DERs [8]. In [9–12], the operation of distribution systems was optimally controlled via DERs commitment, Capacitor Banks (CBs) switching, SVC, and reconfiguration using the jellyfish search algorithm and manta ray foraging optimization algorithm, respectively. In both studies, the wasted energy of power losses was minimized considering the daily load variations, but the uncertainties of the DERs were not taken into account. In [13], the optimal operation and energy management method for a hybrid MG including photovoltaics, wind turbines, a pump as a turbine system, and a diesel generator was introduced, with a study on day-ahead scheduling. The optimal energy management minimizes the fuel cost of diesel generators, the daily operating cost, as well as the balance between the generation and load for both warm and cold days using an imperialist competitive algorithm. In [14], a hybrid ant lion optimization with a bat algorithm was utilized for the power management of the MG considering a droop controller strategy. The main target of this droop controller was stabilized by the MG by minimizing the errors of real and reactive power under a power shortage and power maximum. In [15], an optimal energy scheduling mechanism was presented in multi-MGs in order to minimize the total operational costs of their committed DERs. This study provided different DERs types and their associated uncertainties in a multi-MG system, but the linked lines and their losses were completely ignored. In [16], a Tabu search algorithm was applied for the design of the MG system components with minimum investment, operation, and emission costs. This study utilized the Monte Carlo simulation to deal with the uncertainties due to load forecasting and the random outages of the units. However, the uncertainties due to the intermittent sources of WTs and PVs were not taken into consideration where their outputs were directly evaluated from the daily wind velocity and solar irradiance, respectively. In [17], particle swarm optimization (PSO) was dedicated to minimize the MG operational costs considering the variations in loadings, DGs, and requirements of stable grid operational constraints.

Despite that, only the fuel costs of the committed DGs were handled where the quadratic cost models were utilized for the fuel cells and the micro turbines. In [18], a manta ray foraging optimizer was developed to optimally solve the economic dispatch problem with wind power inclusion considering the valve point effects of the generators, while the wind power effects were ignored in [19,20]. However, the environmental impacts of this operation were not considered. In [21], an artificial ecosystem-based optimizer was applied considering the demand side management for minimizing the techno-economic evaluations of hybrid energy systems. In [22], an optimal operational strategy was presented for MGs including hydrogen storage to integrate RESs and decrease the emissions. In [23], in off-grid MG simulation and tests, an adaptable energy management strategy relying on a mixed energy systems has been provided to preserve the stable operating condition of the off-grid MG and lengthen the lifetime of batteries.

In [24], a harmony search algorithm was combined with differential evolution for the optimal operation of MGs. Added to that, an optimal operation of MGs was considered with WTs, PVs, battery energy storage (BES) systems, electric vehicles (EVs), and demand response for minimizing the total operating costs [25]. In this study, Sparse Nonlinear OPTimizer (SNOPT) solver was utilized using Generalized Algebraic Modeling Systems (GAMS) software. In both studies [24,25], the outputs of WTs and PVs were directly evaluated from the hourly wind velocity and solar irradiance, respectively. Therefore, the uncertainties due to the intermittent sources require an effective handling procedure. In [26,27] a moth flame optimization algorithm was employed for the optimal operation of a hybrid energy system including WTs, PVs, gas turbines, and energy storage.

In [28], an optimal MG operation strategy was presented to minimize the fuel costs and the produced emissions. In this study, the DER uncertainties were considered via the probability distributions and confidence. However, the application was limited to a very small number of sources where linear programming and quadratic programming (QP) were utilized as solvers. In [29], RES was allocated and optimized in a distribution system using Mixed Integer-Linear Programming (MILP) and was solved by FICO® XPRESS optimization software. In [30], the Energy Storage (ES) and RES were integrated using MILPmethod.

Recently, a new effective optimization algorithm of Equilibrium optimizer (EO) was presented [31]. EO provides strong exploratory and exploitative search mechanisms to adjust solutions at random, assisting in local minima avoidance in the optimization process, which is a common drawback of many optimization algorithms [31]. EO was efficiently used for solving the optimal power flow (OPF) problem in the AC power systems [32] and hybrid AC/DC power grids [33]. In [34,35], EO was utilized for handling the economic and the combined economic environmental dispatch problems, respectively, considering the power constraints, effects of the valve point, transmission losses, and ramp rate limits. In [36], an adaptive EO was developed for an optimal allocation procedure of biomass DGs to enhance the performance of the distribution systems and to reduce the related environmental emissions. In [37], EO was used to deal with the energy management optimization (EMO) in the MG considering the variations of WTs, PVs, and load demand for cost minimization and voltage magnitude improvements. In [38], an improved EO integrated for with optimal allocation of multiple PV units with batteries has been described. In [39], EO was utilized for the EMO considering the energy storage devices and the emissions from the associated DERs. In [40], EO was employed for estimating the undefined parameters for the lithium-ion batteries. In [41], EO was employed for identifying the prediction of oil breakdown voltage considering the barrier impact.

In this paper, an optimal operating strategy based on EO is developed for the technoeconomic and environmental optimization scheme for MGs with multiple RESs. For this target, two objective functions are represented for minimizing the generation costs and minimizing the emissions of environmental pollution caused by them. The proposed algorithm is tested on two systems in order to verify its effectiveness and efficiency. The two systems are IEEE 33-bus and a practical large-scale 141-bus system of AES-Venezuela in the metropolitan area of Caracas. Added to that, EO is compared with other recent algorithms of DE and RAO algorithms. The main contributions of this paper can be summarized as follows:


Four additional rest sections of the current paper are organized thusly: Section 2 shows the mathematical formulation of the MG operation. The EO algorithm is developed in Section 3. Section 4 reveals the simulation results on the two small and large scale tested distribution systems. Section 5 concludes the paper findings.
