1. Introduction

Due to the technological and industrial worldwide progress and the growing industry and society need of power generation for the development and increment of life quality, it is of unquestionable importance to increase sustainable access to electrical energy. In developing countries, there are still many locations without power supply.

Power generation through fossil generators offers a continuous and reliable source of energy making it a very popular option for electrification in off-grid areas. This alternative presents an initial investment cost relatively low compared to other sources of power generation. However, fossil power generators are sized to meet peak demand and have a low performance when the load is quite below to its rated

capacity. Additionally, operating and maintenance costs are high; the cost of energy (COE) is subject to changes according the national and international fuel markets. In addition, logistical challenges associated with fuel supply in remote areas can cause a significant increase in generation costs [1]. A solution for these disadvantages is the implementation of HRES which includes fossil and other energy sources. For warm and high-average daily radiation levels, photovoltaic solar energy with battery backup represents an attractive complementary source to diesel generation systems. This solution allows the reduction of generation costs and increased system reliability [2, 3].

Hybrid systems have shown lower generation costs and greater reliability than dependent systems of a single source of energy [1, 2–6]. Each element of the system has to be properly sized to achieve a techno-economic profitability. Therefore, the penetration of renewable energy sources in the energy market depends mainly on the applied sizing methodology to optimize its design [7].

The optimization of these systems could be complex, since many variables are naturally stochastic and linked to the selected location. Examples of these variables are temperature, solar resource, and load profile of the location [8]. Moreover, the optimization technique depends on the selected objective function, which can be oriented in seeking financial gain, increasing system reliability, and reducing the environmental impact [9].

there is insufficient power from the PV source and the battery bank. Only the minimum DG unit required operates in every time step; (3) all DG units must operate over the minimum load ratio (δmin) defined otherwise the DG unit must be turned off; (4) all DG units have the same nominal power capacity and operate at the equilibrium point at the same load ratio; (5) when diesel units are operating, the PV generation prioritizes the charge of the battery bank over the load; (6) only AC loads are considered; and (7) a maximum number of DG units are considered. The proposed methodology is composed of the following steps: (1) a dispatch strategy algorithm, (2) calculation of economic indicators, (3) calculation of reliability indicators, (4) calculation of fiscal incentives, and (5) a PSO optimization process given an objective function which optimizes the number of components of the installation and a calculation of economic and reliability indicators for the best solution. The following subsections detail the steps of the methodology. Figure 2 shows the schematic of the proposed methodology and the optimization process.

Methodology for Sizing Hybrid Battery-Backed Power Generation Systems in Off-Grid Areas

Schematic diagram of a hybrid solar/battery/diesel generation system.

DOI: http://dx.doi.org/10.5772/intechopen.88830

Figure 3 shows the dispatch strategy flowchart used on the diesel-PV-battery

1. Obtain or generate inputs of the system: load profile (PL), irradiance (G), and temperature (T) for the location in a year. Load profile can be obtained through a survey considering the uncertainties on the input data (38) and (39), and also load profile can also be obtained using measurement of the electrical demand. High-quality solar resource and meteorological data can be obtained by two approaches: high-accuracy instruments installed at a meteorological station and complex solar meteorological models which are validated

2. Introduce the following technical information of each element of the system

2.1. According to the available location and its restrictions, introduce the following technical information: NDG, max (maximum number of DG units), wDG (rated power of the available diesel generator), δmin (minimum load ratio [%]), f <sup>0</sup> (fuel Curve intercept coefficient [l=kW]), f <sup>1</sup> (fuel curve slope coefficient [l=kW]), Npv (number of PV modules), Ppvstc (rated power of the

model for a year which algorithm is described in detail below.

using high-quality ground instruments.

2.1 Dispatch strategy algorithm

Figure 1.

and initialize variables.

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Then, it is necessary to develop a methodology for optimizing the design of HRES that allows the integration of photovoltaic and diesel generation systems, with or without energy storage, allowing to reduce energy costs and maintaining a high reliability in energy supply in off-grid areas. The methodology requires a set of input information linked to the project site, as meteorological and load profile data, and also technical and economic information of the main equipment of the HRES. Then, an optimization process is necessary to determine the best combination of diesel power, PV power, and battery bank capacity. Economic and reliability parameters that support the solution obtained is expected to be presented with the solution.

In the last decade, several optimization techniques have been used to obtain an optimal solution of the sizing of HRES [7, 10–13]. The results among different approaches may vary depending on the characteristics of the model which permits to simulate the behavior of different elements of the system and also the economic and reliability model used as base on the optimization process.

The main objective of this work is to develop an optimization methodology for sizing HRES in off-grid areas of developing countries. In contrast to other works, each step of the methodology is described in detail. Also, special condition will be considered on the development of the economic and reliable model to adjust it to the reality of Colombia, for example, the national and international physical distribution cost or the incentive proposed by the Act 1715 for electrification using nonconventional energy sources in Colombia.
