**1. Introduction**

In the present scenario, the energy demand is managed with both renewable and non-renewable sources in most of the countries around the world. More quantum generation of green energy than fossil fuels is the need of the hour due to both the cost factor as well as the mitigation of global warming. At the same time, the optimum production of green energy even during interruptions is a challenging task. Hence, a study of the economic analysis on this subject is more important. The Sun is the prime source of all energies and power. Heat and light, being the primary forms of solar energy are transformed and absorbed by the environment in various ways for sustainability; this has been reviewed in [1–4]. A review on the relationships between energy transformation from

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renewable energy sources such as wind and solar with climate change mitigation has been presented in [5]. This, in turn, helps to mitigate greenhouse gas emissions and reduce global warming as has been explained in [6], along with the anticipated patterns of future energy use. A review on the scope of CO2 mitigation through various renewable energy gadgets has been presented [7]. In general, wind energy is caused by airflow and solar energy is caused by irradiation. Hence, the amount of harvestable renewable energy predicted for a particular time span might not be accurate. In addition, renewable energy sources are intermittent, fluctuating and non-continuous. Many unsatisfactory issues due to implementation of renewable electricity systems alone have been addressed in detail [8]. Voltage, frequency, and waveform are the three important affecting factors of the 'power quality' of the distribution network, due to the synchronization of the generation from renewable energy sources into the power grid [9]. It has been reported that this could also affect the scheduling scheme, in turn, hence further affecting the normal operation of the distribution network. The grid synchronization of the power is discussed in detail by [10,11]. Energy storage technologies using batteries in hybrid wind power operations is analyzed in [12,13]. The characteristics and a comparison of energy storage systems have been reviewed in [14]. The need for energy storage in active distribution networks in spite of the uncertainty of wind power distributed generation is discussed in [15].

In this context, the study of the integration of hybrid renewable energy systems with the power grid is essential. The main essence of the energy storage is nothing but the properties of its 'space transfer energy' for stable operation of the power grid and thereby to improve the power quality. In [16], an OPF control of HRES with energy storage was discussed, and the achievement of a better dynamic response due to energy storage has been proved.

In recent years, a number of nature-inspired metaheuristic evolutionary algorithms have been studied in depth and are being applied to solve different types of optimization problems. Examples of such algorithms are the Genetic Algorithm (GA) [17–19], Biogeography-based Optimization (BBO) [20], Particle Swarm Optimization (PSO) [21,22], Artificial Bee Colony (ABC) [23], Different Evolution (DE) [24], Bacterial Foraging Optimization (BFO) [25], Ant Colony Optimization (ACO) [26], Cuckoo Search (CS) [27], Honey Bee Mating Optimization (HBMO) [28], BAT Algorithm [29], etc. These different types of algorithms are often being used to solve the various engineering problems that arise in different fields. Numerous research works are available in the literature for optimum power flow managemen<sup>t</sup> using solar energy and wind energy transformation applying different techniques in different parts of the world. The steps taken by the Korea Electric Power Corporation (KEPCO) to develop a Renewable Energy Map (REM) in South Korea have been reviewed in [30]. In addition, they have explained the steps taken for optimal location identification for a renewable complex without violating any reliability standards.

An effective assessment approach to non-renewable energy sources has been developed in Taiwan; the country is dependent on the importation of energy, with more than 99% coming from foreign countries, as discussed in [31]. In [32], the many problems and challenges faced by China's wind power industry are reviewed in detail. A comprehensive assessment was presented to discuss China's wind power industry, power demand, cost and distribution of wind power. In [33] it was reported that the wind power access to power grids could have a grea<sup>t</sup> influence on the power stability and power quality of the distribution network of Henan Province in China.

In [34], the availability of the abundant solar radiation and the challenges for utilizing it for solar irrigation in Bangaladesh has been reviewed. In [35] the availability of RES and the present technologies practiced in Bangladesh have been briefed with conclusions for the implementation of systematic changes from conventional to the non-conventional (renewable) methods to obtain benefits for the whole nation.

This work focuses on an economic analysis to integrate an energy storage system (ESS) using batteries along with different hybrid renewable energy source (HRES) devices using the proposed strategy. Here, the HRES system comprises wind turbines (WT) and a photovoltaic (PV) system. The proposed strategy is the hybridization of two algorithms called Radial Basis Function Neural

network (RBFNN) explained by [36] and Oppositional Elephant Herding Optimization (OEHO) described by [37], together named the RBFNOEHO technique.

Reference [38] has proposed an optimal energy managemen<sup>t</sup> for a grid-connected PV-WT, MT and ESS hybrid energy system utilizing the ANFMDS approach and Homer. The technique has analyzed various load demands of the microgrid.

In the proposed study, a detailed analysis based on real data is discussed to insist on the need for an energy storage system to avoid the wastage of the production and utilization of green energy. Beneficially and economically; in particular, generation during various types of 'grid interruption' and utilization during the 'peak' hours and 'maintenance periods' of the solar and wind electric generators are studied. The research work is a comparative economic analysis of HRES without using energy storage and battery storage during grid interruption and curtailment. Curtailment has been a major concern for RES. Curtailment can be defined as a reduction in the output of a generator from what it could otherwise produce given the available resources (e.g., wind or sunlight), typically on an involuntary basis. Curtailment occurs when a transmission system operator issues an instruction to limit the energy output of a specific or a group of RES generators. There is lot of evidence for the incidence of curtailment across many states in India. Curtailment is heavily influenced by local factors, such as the status of the grid infrastructure near an RE generation site and resource variability at those sites. There is considerable variation in the quantum of curtailment across months, states, and even districts in a state, so there is a need to solve the issue during grid interruptions and curtailment using HRES combined with the optimal storage system.

In this paper, an existing Hybrid Renewable Energy Source (HRES) comprising a wind power generation and solar power generation system in a single 22 KV feeder at Vagarai located in the Palani Division in the Dindigul Circle of TANGEDCO in Tamil Nadu, India has been chosen for study. This dissertation focuses on an economic analysis to integrate the hybrid renewable energy sources (HRES) with an energy storage system (ESS) using batteries in the proposed strategy. Here, the HRES system comprises wind turbines (WT) and a photovoltaic (PV) system. The proposed strategy is the hybridization of two algorithms called Radial Basis Function Neural Network (RBFNN) and Oppositional Elephant Herding Optimization (OEHO) named the RBFNOEHO technique. The rest of the paper is organized as follows: Section 2 describes the topography of Tamil Nadu state. Section 3 contains a description of the chosen area. Section 4 describes the objective function formulation. Section 5 portrays the system configuration of HRES system. Section 6 postulates the economic analysis of battery storage system for HRES system using the proposed hybrid strategy. Section 7 defines the real data and simulation results and a discussion. Section 8 concludes the manuscript.

#### **2. Topography of the State of Tamil Nadu**

In general, in India, the Sun is visible on sunny days at 6.00 a.m. in the morning and disappears at 6.00 p.m. The solar insulation varies with seasons. However, in general, the peak of the irradiation during sunny days occurs between 7.30 a.m. and 4.00 p.m. The energy demand can be fulfilled by solar energy only during daytime hours. On the other hand, wind energy is available in the southwest monsoon months of May through September in the peninsula and Tamil Nadu. The rest of the months there will be poor generation from wind due to lull periods or shorter generation periods. The energy balance for the entire day and night and throughout the year by the hybrid system alone is very di fficult and it is not at all possible. The coordinates of the location of Thoppampatti village and Vagarai village in Palani, India are 10.5844◦ N, 77.5727◦ E. The average and maximum wind speed and wind gus<sup>t</sup> over the years in Palani, India as described in [39] are shown in Figure 1.

 average

**Figure1.**Theandmaximumwindspeed andwindgus<sup>t</sup>theinPalani,

 over  years

 India.

The average sun hours and sun days over the years in Palani, India is shown in Figure 2, as described in [39].

**Figure 2.** The average sun hours and sun days over the years in Palani, India.

### **3. Description of the Chosen Area**

An existing hybrid system with both solar and wind generation combined and formed as 22 KV feeders and fed into 110/22 KV Vagarai Substation, in the Palani Division in the Dindigul Circle of TANGEDCO in the State of Tamil Nadu of India has been chosen for our study purposes. The presentation of the real data and analysis in this section will give the real picture of the present scenario and the need for energy storage for economical and beneficial usage of the green energy to meet the entire "Demand" requirement with the limited utilization of conventional energy. The switchyard arrangemen<sup>t</sup> of the 22 KV hybrid feeders in the 110/22 KV Vagarai SS is shown in Figure 3.

**Figure 3.** Switchyard arrangemen<sup>t</sup> of the 22 KV hybrid feeder in the 110/22 KV Vagarai SS.
