Elsevier

Energy

Volume 202, 1 July 2020, 117670
Energy

Supplying not electrified islands with 100% renewable energy based micro grids: A geospatial and techno-economic analysis for the Philippines

https://doi.org/10.1016/j.energy.2020.117670Get rights and content

Highlights

  • We apply geospatial analysis and energy modelling for island electrification planning.

  • We identify 649 not electrified islands with a population of 650,000.

  • We simulate 100% renewable energy systems comprised of solar, wind and battery storage.

  • Costs for 100% reliability systems are between 0.53 and 0.61 USD/kWh.

  • Allowing supply shortages significantly decreases costs down to 0.43–0.47 USD/kWh.

Abstract

Access to clean energy is required for facilitating sustainable development in remote areas. In the Philippines many small islands are not supplied with electricity although it is aimed to achieve universal electrification by 2022. Here, renewable energy holds a large potential given the abundant resource availability and high costs for fossil fuel. However, a lack of key information for electrification planning prevents the wider deployment of renewable energy.

Therefore, this paper presents a combined approach applying geospatial analysis, cluster analysis and energy system modelling: First, we identify not electrified islands. Second, we utilize cluster analysis for pattern recognition. Third, we perform energy system simulations of 100% renewable energy systems combined of solar power, wind power and battery storage.

Thereby, we find 649 not electrified islands relevant for our analysis with a population of 650,000. These islands are grouped in four clusters according to population and renewable resource availability. For each cluster we found that cost-optimized 100% renewable energy systems are based on solar and battery capacities with supplementary wind capacities. Generation costs and system designs are most sensitive to variation in battery and capital costs. Allowing short-term supply shortages can significantly decrease costs through smaller capacitiy requirements.

Introduction

Universal electrification by 2030 was set as the 7th target under the UN’s Sustainable Development Goals (SDGs) framework [1]. Low-carbon technologies such as renewable energy (RE) are important means for achieving SDG7 [2], because environmental and social sustainability are implicit aspects of the SDG framework [3]. Rural and remote areas require electricity for socio-economic development although causal relationships are site specific and complex [4]. Additionally, providing access to electricity is of particular importance to the overall SDG framework as it positively correlates and facilitates advancing towards other SDGs [5].

Strategic energy access planning is required to advance towards SDG7 and utilize resources most efficiently [6]. Advanced software tools are needed to allow for energy access planning [6] and several examples of such tools have been developed and presented in the scientific literature [6,7]. The presented tools allow for deriving key information for energy access planning, among other the electrification type (grid connection, mini-grid, stand-alone), system design, power generation costs and dispatch of off-grid systems, distribution grid network design and additional upstream generation requirements [6]. Results of such tools allow for different depth-of detail [6]: Prefeasibility studies indicate optimal electrification solutions for areas and customers clustered in raster cells of different sizes (e.g. km2). Examples have been presented for Sub-Saharan Africa [8,9], Nigeria [10], Ethiopia [11] and Kenya [12]. Intermediate analysis tools add a further level of detail since individual villages or populated places are taken into account, grid network designs are retrieved and further technological constraints are considered. Such tools have been applied to Nigeria [13,14] and Ghana [15] among other. Finally, tools considering detailed generation networks and designs provide the maximum level of detail by including detailed village grid layouts and introducing a large variety of technological constraints for power system design. Ciller and Lumbreras (2020) [6] identify only one existing tool providing such detail but do not present peer-reviewed articles. Geospatial software has been applied as essential and integrated part of many of the aforementioned tools or was utilized for gathering data (e.g. population distribution, renewable resources). This includes several tools applied for Africa on the prefeasibility level [7,16,17]. Furthermore, geospatial analysis was used for case studies in Timor Leste [18] and Nigeria [14] on the intermediate analysis level. The presented tools and case studies focus mainly on Sub-Saharan Africa given the large need for energy access interventions there. However, thereby the tools neglect other regions in need of energy access interventions like Southeast Asia and disregard the local specific conditions e.g. the large number of islands. A combined approach of geospatial analysis and energy system modelling was applied on a global scale to study the feasibility of RE integration into island grids [19] and for a classification regarding the RE potential on islands [20] but not specifically for the strategic planning of providing energy access. In the Philippines, the case study country of this paper, similar combined approaches were utilized to quantify the potential for upgrading diesel based island systems with RE [21] and to project costs for submarine cable connection [22]. However both studies consider pre-electrified islands. Overall these findings highlight that electrification planning tools are rarely designed and applied to regions outside Africa which reflects a research gap. Furthermore, a review study formulates research needs for the improvement of electrification planning tools including adding multi-criteria optimization, including more detailed year by year planning, adding further power generation technologies, improving grid network design and addressing uncertainties of input parameters [6]. Our study contributes to the research field with a detailed assessment of the not electrified island landscape of an understudied country, the Philippines, and a simulation of 100% RE based electrification pathways. Thereby, we address the identified research gap by focusing on a region outside Africa and address some of the research needs for improving electrification planning tools outlined earlier. We present a novel and combined approach based on geospatial data and energy system modelling which is replicable to case studies with similar boundary conditions. The approach can be assigned to the intermediate level as defined by Ref. [6] as single islands are considered and system designs are simulated based on a set of technical constraints. Finally, we develop and present an integrated geospatial and energy system analysis tool which is fundamental for effective electrification planning and facilitates to derive key information for large geographic areas [11]. We base our approach on similar studies presented for landlocked countries as presented in Ref. [8,[10], [11], [12],18] and contribute with a methodological adaptation to the insular context of the Philippines.

In the Philippines universal electrification by 2022 was announced as target in the Philippine Energy Plan published by the Department of Energy [23]. Nevertheless, household electrification was at 89.6% as reported for 2016 with more than 2.36 million households lacking access to electricity [24]. More recent statistics for 2019 state the electrification rate of the Philippines at >95% reflecting a population of 5.2 million without access to electricity [25]. Reaching out to the remaining 5% and the “last mile” is challenging, given the heterogeneity of the country which is comprised of more than 7,600 islands [26]. Additionally, key information for energy access planning e.g. population statistics and resource availability is missing for many of the remote areas and small islands.

Currently islands which are supplied with electricity but not connected to the two centralized electricity systems are mostly supplied with diesel generators [21]. This leads to long power cuts due to the high costs of diesel power generation [27] and is therefore not a feasible solution for the electrification of the entire archipelago [28]. Furthermore, the Philippine economy, as a net importer of crude oil products, is sensitive to global market developments and a rising oil price negatively affect the national economy [29]. As a consequence renewable energy sources need to be utilized to supply an ever increasing demand and to comply with climate change mitigation objectives [30]. This is especially relevant since the Philippines are the most vulnerable country [31], in a region largely affected by climate change [32]. Therefore, providing sustainable energy access to remote and marginalized communities is crucial for improving living conditions [33] and strengthening resilience to climate change [34]. In conclusion sustainable electricity access planning should consider only RE technologies as supply source.

In order to reach the last mile electrification with renewable energy systems, an effective island electrification plan needs to be derived. This study presents a combined approach based on geospatial analysis and energy system modelling to reduce data paucity and the uncertainty regarding the number and location of not electrified islands and the renewable energy potential. The approach enables to identify populated islands without electricity access, to derive information for energy modelling and to simulate 100% RE systems. Thereby, we address the following research questions:

  • A)

    Where are not electrified populated islands located?

  • B)

    What are specific population and renewable resource characteristics of the not electrified islands and how can the islands be grouped for energy access planning?

  • C)

    What are the techno-economically optimal supply options for certain island groups considering an 100% renewable combination of solar power, wind power and battery storage?

We introduce the research approach and methods in chapter 2. In chapter 3 we present the main findings of our consecutive approach separately for each step starting with geospatial analysis, cluster analysis and energy system modelling analysis. We discuss our approach and results in chapter 4 and conclude the paper with conclusions and policy recommendations in chapter 5.

Section snippets

Material and methods

This study applies a three-step approach for addressing the research questions as outlined in the introduction section. First, we conduct a geospatial analysis to identify not electrified islands. Second, we apply explorative cluster analysis to classify islands and to identify representative case study islands per cluster group. Third, we utilize open source energy system modelling to assess the potential for 100% RE systems for the case study islands.

Geospatial analysis

We identify 171 islands with connection to the electricity grid or power plants based on the geospatial approach outlined in subsection 2.1.1 and exclude these islands from further investigation. For the remaining more than 17,600 polygons reflecting land masses we assess the population as described in subsection 2.1.2. Thereby we identify 1,920 islands as populated (population > 0) with an overall population of more than 734 thousand. This population reflects approx. 14% of the not electrified

Discussion

Our results for generation costs, supply shortage levels and share of excess electricity are in line with findings of other researchers: Lozano et al. [71] find LCOE of 0.39 USD/kWh for 100% RE systems with excess electricity of 39.3% and a shortage level of 91.4%. Katsaprakakis and Voumvoulakis [103] compute power generation costs of 0.29 EUR/kWh for a 100% RE scenario on the Greek island Sifnos considering pumped-hydro storage as cost-efficient electricity storage option. Another case study

Conclusion

Finally, we can state that the research questions outlined in the introduction chapter have been addressed: We find 1,920 not electrified and populated islands of which we select 649 with a population larger 50 and complete resource datasets for further analysis. PAM cluster analysis indicates an optimal split of four island groups. Three cluster groups comprise the majority of islands (88%) and are characterized by small populations of around 500. These cluster groups differ in resource

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

I am acknowledging the funding of my PhD research through the Reiner Lemoine Foundation. The author thank the Reiner Lemoine-Foundation for co-financing this research work. Additionally, the author also thanks Philipp Blechinger, Martha Hoffmann, Karoline Gerbatsch and Setu Pelz for methodological support and proof-reading.

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