Population growth, urbanization and economic development drive the use of resources. Securing access to essential services such as energy, water, and food, while achieving sustainable development, require that policy and planning processes follow an integrated approach. The 'Climate-, Land-, Energy- and Water-systems' (CLEWs) framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. The approach was first introduced by the International Atomic Energy Agency to conduct an integrated systems analysis of a biofuel chain. The framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. Its multi-institutional application to the case of Mauritius in 2012 initiated the deployment of the framework. A vast number of completed and ongoing applications of CLEWs span different spatial and temporal scales, discussing two or more resource interactions under different political contexts. Also, the studies vary in purpose. This shapes the methods that support CLEWs-type analyses. In this paper, we detail the main steps of the CLEWs framework in perspective to its application over the years. We summarise and compare key applications, both published in the scientific literature, as working papers and reports by international organizations. We discuss differences in terms of geographic scope, purpose, interactions represented, analytical approach and stakeholder involvement. In addition, we review other assessments, which contributed to the advancement of the CLEWs framework. The paper delivers recommendations for the future development of the framework, as well as keys to success in this type of evaluations.

Thanks to their modularity and their capacity to adapt to different contexts, hybrid microgrids are a promising solution to decrease greenhouse gas emissions worldwide. To properly assess their impact in different settings at country or cross-country level, microgrids must be designed for each particular situation, which leads to computationally intractable problems. To tackle this issue, a methodology is proposed to create surrogate models using machine learning techniques and a database of microgrids. The selected regression model is based on Gaussian Processes and allows to drastically decrease the computation time relative to the optimal deployment of the technology. The results indicate that the proposed methodology can accurately predict key optimization variables for the design of the microgrid system. The regression models are especially well suited to estimate the net present cost and the levelized cost of electricity (R-2 = 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R-2 = 0.92 and 0.86). A least-cost path towards 100% electrification coverage for the Bolivian lowlands mid-size communities is finally computed, demonstrating the usability and computational efficiency of the proposed framework.

For decades, electrification planning in the developing world has often focused on extending the national grid to increase electricity access. This article draws attention to the potential complementary role of decentralized alternatives – primarily micro-grids – to address universal electricity access targets. To this aim, we propose a methodology consisting of three steps to estimate the LCOE and to size micro-grids for large-scale geo-spatial electrification modelling. In the first step, stochastic load demand profiles are generated for a wide range of settlement archetypes using the open-source RAMP model. In the second step, stochastic optimization is carried by the open-source MicroGridsPy model for combinations of settlement size, load demand profiles and other important techno-economic parameters influencing the LCOE. In the third step, surrogate models are generated to automatically evaluate the LCOE using a multivariate regression of micro-grid optimization results as a function of influencing parameters defining each scenario instance. Our developments coupled to the OnSSET electrification tool reveal an important increase in the cost-competitiveness of micro-grids compared to previous analyses.

Increased biomass for energy production features as a key part of the transition to a competitive low-carbon EU energy system. Not all energy strategies however will lead to reduced emissions, and extensive biomass production inherently compete with, e.g., agricultural systems for key natural resources like water and land. This paper investigates the ramifications of three potential energy pathways for Europe developed by the H2020 REEEM project, ambitiously aimed at reducing CO2 emissions to 80–95% compared to 1990, using different mixes of biomass. Their environmental footprint for 2015–2050 in terms of land-use-change emissions and water consumption are confronted with near-term climate change projections. Finally, potential implications for the implementation and robustness of future European energy strategies are discussed, highlighting in particular the role of uncertainties in estimating the performance of biomass systems.

Countries around the world are preparing to give the last leap to accomplish a 100 % of rural energy access. Nonetheless, country-wide electrification planning requires the analysis of hundreds of un-electrified villages with different social, economical and geographical backgrounds. State-of-the-art planning models typically handle this computationally challenging task relying on highly-simplified technological characterizations, at the expense of a proper estimation of the cost-optimal potential of off-grid technologies, particularly micro-grids. In this paper, we propose a machine-learning method to improve such technological characterization while keeping the computational tractability of the problem under control. Firstly, field surveys from rural un-electrified villages in Bolivia are used as an input for a stochastic load generator model, creating several demand scenarios for a set of different village archetypes; secondly, renewable energy time series for representative locations of Bolivia are created using the NASA database. For each demand and renewables potential combination, a two-stage stochastic sizing model is adopted to obtain the corresponding cost-optimal micro-grid configuration. Finally, these data are used to train a Gaussian process regression with the levelized cost of energy (LCOE) as dependent variable and the daily average demand, renewable energy, and techno-economic characteristics of the components as independent variables. The results show that the trained algorithm is ultimately able to identify the LCOE of microgrids in given conditions, out of the training dataset, with satisfying accuracy and limited computational effort.

Bolivia plans significant investments in conventional and renewable energy projects before 2025. Deployment of large hydro-power, wind and solar projects are foreseen in the investment agenda. However, and despite the large renewable potential in the country non-conventional renewable technologies are not yet expected to be a main source in the supply chain. The aim of this article is to evaluate the flexibility of the Bolivian power generation system in terms of energy balancing, electricity generation costs and power plants scheduling in a scenario that considers large solar and wind energy technology deployment. This is done using an open source unit commitment and optimal dispatch model (Dispa-SET) developed by the Joint Research Center of the European Commission. National data for existing infrastructure, committed and planned energy projects are used to assess the case of Bolivia. The base scenario consider all techno-economic data of the Bolivian power system up to 2016. A harmonized dataset is gathered and released as open data to allow other researchers to run and re-use the model. This model is then used to simulate scenarios with different levels of solar and wind energy deployment. Results from the analysis show that an energy mix with participation of solar and wind technology with values lower than 30% is technically feasible and indicates that further grid reinforcements are required.

This paper considers hypothetical options for the transformation of the Bolivian power generation system to one that emits less carbon dioxide. Specifically, it evaluates the influence of the weighted average cost of capital (WACC) on marginal abatement cost curves (MACC) when applying carbon taxation to the power sector. The study is illustrated with a bottom-up least-cost optimization model. Projections of key parameters influence the shape of MACCs and the underlying technology configurations. These are reported. Results from our study (and the set of assumptions on which they are based) are country-specific. Nonetheless, the methodology can be replicated to other case studies to provide insights into the role carbon taxes and lowering finance costs might play in reducing emissions.

In the last decades, energy modelling has supported energy planning by offering insights into the dynamics between energy access, resource use, and sustainable development. Especially in recent years, there has been an attempt to strengthen the science-policy interface and increase the involvement of society in energy planning processes. This has, both in the EU and worldwide, led to the development of open-source and transparent energy modelling practices. This paper describes the role of an open-source energy modelling tool in the energy planning process and highlights its importance for society. Specifically, it describes the existence and characteristics of the relationship between developing an open-source, freely available tool and its application, dissemination and use for policy making. Using the example of the Open Source energy Modelling System (OSeMOSYS), this work focuses on practices that were established within the community and that made the framework's development and application both relevant and scientifically grounded.

Bolivia has plenty of energy resources that can supply not only its own electricity demand but has also the potential to export surplus production to its neighbors in South America. This study presents a comparative analysis of the electricity export potential of Bolivia, considering modeling results carried out by the Bolivian government and those from OSeMOSYS SAMBA - South America Model Base. Four scenarios were modelled from different conceptions of strategic large hydropower combinations. The scenarios comparison highlights the cross-border potential trade between Bolivia and neighboring countries, mainly Brazil. Using a Cooperative Games approach, through the calculation of the Shapley value, the bargaining power of Bolivia was identified, reaching its higher value in the scenario where El Bala and Cachuela Esperanza dams are present. The cooperative games approach provides a better understanding of electricity trade opportunities to support policy makers in international negotiations, thus considerably reducing incentives to non-cooperative actions.

Increasing energy access in emerging economies has played an important role to maintain or achieve desirable social and economic development targets. As a consequence, the growing energy requirements need policy instruments to ensure energy supply for future generations. The literature reports many studies with different approaches to model and test policy measures in the energy sector, however few energy-related studies for Bolivia are available. This paper addresses this knowledge gap, representing the first national level energy demand model and projections for Bolivia. The model use demographic, economic, technology and policy trends with a pragmatic model structure that combines bottom-up and top-down modelling. The scenario analysis has a particular focus on alternatives for energy savings, energy mix diversification and air quality. Three scenarios were analysed: Energy Savings, Fuel Substitution and the aggregate effects in a Combined scenario. The reference scenario results show the overall energy consumption grows 134% in 2035 compared to 2012 with an annual average growth of 3.8%. The final energy demand in the energy savings scenario is 8.5% lower than the Reference scenario, 1.5% lower in the fuel substitution scenario and 9.4% lower in the combined Scenario. The aggregate impact of both energy savings and fuel substitution measures leads to potential avoided emissions of 25.84 million Tons of CO2 equivalent in the model horizon 2012-2035.