Nearly 840 million people still lack access to electricity, while over a billion more have an unreliable electricity connection. In this article, the three different electrification pathways-grid extension, centralized microgrids, and standalone solar-based solutions, such as pico-solar and solar home systems (SHS)-are critically examined while understanding their relative merits and demerits. Grid extension can provide broad scale access at low levelized costs but requires a certain electricity demand threshold and population density to justify investments. To a lesser extent, centralized (off-grid) microgrids also require a minimum demand threshold and knowledge of the electricity demand. Solar-based solutions are the main focus in terms of off-grid electrification in this article, given the equatorial/tropical latitudes of the un(der-)electrified regions. In recent times, decentralized solar-based off-grid solutions, such as pico-solar and SHS, have shown the highest adoption rates and promising impetus with respect to basic lighting and electricity for powering small appliances. However, the burning question is-from lighting a million to empowering a billion-can solar home systems get us there?The two main roadblocks for SHS are discussed, and the requirements from the ideal electrification pathway are introduced. A bottom-up, interconnected SHS-based electrification pathway is proposed as the missing link among the present electrification pathways.

With almost 1.1 billion people lacking access to electricity, solar-based off-grid products like Solar Home Systems (SHS) have become a promising solution to provide basic electricity needs in un(der)-electrified regions. Therefore, optimal system sizing is a vital task as both oversizing and undersizing a system can be detrimental to system cost and power availability, respectively. This paper presents an optimal SHS sizing methodology that minimizes the loss of load probability (LLP), excess energy dump, and battery size while maximizing the battery lifetime. A genetic algorithm-based multi-objective optimization approach is utilized to evaluate the optimal SHS sizes. The potential for SHS to cater to every tier of the Multi-tier framework (MTF) for measuring household electricity access is examined. The optimal system sizes for standalone SHS are found for different LLP thresholds. Results show that beyond tier 2, the present day SHS sizing needs to be expanded significantly to meet the load demand. Additionally, it is deemed untenable to meet the electricity needs of the higher tiers of MTF purely through standalone SHS without compromising one or more of the system metrics. A way forward is proposed to take the SHS concept all the way up the energy ladder such that load demand can also be satisfied at tier 4 and 5 levels.

Off-grid solar home systems (SHSs) currently constitute a major source of providing basic electricity needs in un(der)-electrified regions of the world, with around 73 million households having benefited from off-grid solar solutions by 2017. However, in and of itself, state-of-the-art SHSs can only provide electricity access with adequate power supply availability up to tier 2, and to some extent, tier 3 levels of the Multi-tier Framework (MTF) for measuring household electricity access. When considering system metrics of loss of load probability (LLP) and battery size, meeting the electricity needs of tiers 4 and 5 is untenable through SHSs alone. Alternatively, a bottom-up microgrid composed of interconnected SHSs is proposed. Such an approach can enable the so-called climb up the rural electrification ladder. The impact of the microgrid size on the system metrics like LLP and energy deficit is evaluated. Finally, it is found that the interconnected SHS-based microgrid can provide more than 40% and 30% gains in battery sizing for the same LLP level as compared to the standalone SHSs sizes for tiers 4 and 5 of the MTF, respectively, thus quantifying the definite gains of an SHS-based microgrid over standalone SHSs. This study paves the way for visualizing SHS-based rural DC microgrids that can not only enable electricity access to the higher tiers of the MTF with lower battery storage needs but also make use of existing SHS infrastructure, thus enabling a technologically easy climb up the rural electrification ladder.

Solar Home Systems (SHS) have recently gained prominence as the most promising solution for increasing energy access in remote, off-grid communities. However, the higher than standard testing conditions (STC) temperatures have a significant impact on the SHS components like PhotoVoltaic (PV) module and battery. A modeling methodology is described in this study for quantifying the temperature impact on SHS. For a particular location with high irradiation and temperatures and a given load profile, an SHS model was simulated, and the temperature-impact was analyzed on the performance and lifetime of the SHS components. Different PV module temperature estimation models were applied, and the corresponding dynamic PV outputs were compared. The nominal operating cell temperature (NOCT) model was found inadequate for estimating PV module temperatures under high irradiance conditions. The PV yield was found to be affected by almost 10% due to thermally induced losses. When different levels of temperature variations were considered, the battery lifetime was seen to be up to 33% less than that at 25 ° C. The modeling methodology presented in this paper can be used to include the thermal losses in SHS for rural electrification, which can further help accordingly in system sizing.

The rapid increase in the adoption of Solar Home Systems (SHS) in recent times hopes to ameliorate the global problem of energy poverty. The battery is a vital but usually the most expensive part of an SHS; owing to the least lifetime among other SHS components, it is also the first to fail. Estimating battery lifetime is a critical task for SHS design. However, it is also a complex task due to the reliance on experimental data or modelling cell level electrochemical phenomena for specific battery technologies and application use-case. Another challenge is that the existing electrochemical models are not application-specific to Solar Home Systems. This paper presents a practical, non-empirical battery lifetime estimation methodology specific to the application and the available candidate battery choices. An application-specific SHS simulation is carried out, and the battery activity is analyzed. A practical dynamic battery lifetime estimation method is introduced, which captures the fading capacity of the battery dynamically through every micro-cycle. This method was compared with an overall non-empirical battery lifetime estimation method, and the dynamic lifetime estimation method was found to be more conservative but practical. Cyclic ageing of the battery was thus quantified and the relative lifetimes of 4 battery technologies are compared, viz. Lead-acid gel, Flooded lead-acid, Nickel-Cadmium (NiCd), and Lithium Iron Phosphate (LiFePO₄) battery. For the same SHS use-case, State-of-Health (SOH) estimations from an empirical model for LiFePO₄ is compared with those obtained from the described methodology, and the results are found to be within 2.8%. The relevance of this work in an SHS application is demonstrated through a delicate balance between battery sizing and lifetime. Based on the intended application and battery manufacturer's data, the practical methodology described in this paper can potentially help SHS designers in estimating battery lifetimes and therefore making optimal SHS design choices.

The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead-acid (VRLA) battery and the LiFePO₄ (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge (SOC) and open circuit voltage (V_OC) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design.

The coupling of solar panels and energy storage is inevitable and especially pertinent in places with no access to the electricity grid. This combination must be modular, providing the opportunity to scale up the system if energy demand increases, but also easy to install and user-friendly. These requirements validate the PV-Battery Integrated Module (PBIM) as a potential solution for stand-Alone applications. In this paper, we assess the performance of directly integrating a battery system at the back of a PV panel in comparison to a typical solar home system (SHS) with all the components in a separated manner. The study is carried out using data from a community in the countryside of Stung Treng (Cambodia). First, the optimum battery size and PV panel rating were determined using the loss of load probability metric. Second, the extra PV power losses in the case of (PBIM) were calculated, finding that it is 2.16% less efficient than a normal SHS due to the poorer heat dissipation induced by integrating the converter and batteries at the back of the PV panel. Third, the battery capacity faded by 1% after a year of simulation. Although when compared to a typical SHS PBIM results in slightly higher system losses, the losses are moderated and their impact is minimum when considering the expected benefits derived from using PBIM in SHS. Therefore in this paper, we validate the feasibility of PBIM as a solution for standalone systems in developing countries.