Electricity demand forecasting for rural communities in developing countries: Calibrating a stochastic model for the Bolivian case

The world crusade to close the electrification gap is coming to an end in most regions of the world. In recent years the research in the area has concentrated on the development of planning methods to minimise the cost of implementation. Although successful, the lack of focus on the complex dynamics that govern electricity demand lead to over/under-sizing of technical solutions resulting in waste of resources and missed developing opportunities. In this sense, this paper aims to propose an electricity demand model for rural communities in Bolivia, based on an open-source bottom-up stochastic tool for load profile computation. The “energy sufficiency” concept is used to ensure that people’s basic needs for energy are met in all the analysed cases. Information from various sources, such as on-site surveys, databases and national reports were used to characterise the main geographical areas in Bolivia and the relative specific categories of users. Specific load curves generated with the model were used as inputs in a micro-grid sizing tool and the results were compared with an approach using a demand analysis in less detail. Main results show that the model obtained is capable of generating stochastic demand curves for single or multiple rural communities according to contextual particularities. Notably, the geographic location and the socioeconomic characteristics have a significant impact in the peak loads and the total demand. Considering small industries as an income generating activity can increase in the peak load by about 45%, consequently, there is an economic impact when investing in the solution.Tailored energy system models for energy planning in Bolivia7. Affordable and clean energ

Energy Sufficiency for Rural Communities: The Case of The Bolivian Lowlands

Access to energy has proved to have strong links to other dimensions of socio-economic develop ment. As a first step to ensure electricity coverage in developing countries’ rural communities, a minimum energy access must be settled. To do this, the theoretical concept of energy sufficiency is expanded to fit in the rural energy access logic. Ideally, un-electrified communities must move from low energy consumption states to a position where they consume enough to have a contin uous development without risking global environment goals. For that purpose, a bibliographic review is performed to define the components of an ideal rural community where people’s basic needs for energy services are met equitably. Main findings show that besides the household component, public lighting, education, health, water and production services must be considered at the moment of estimated energy demands for rural electrification. To test the implication of this, a series of plausible village configurations of the Bolivian lowlands are proposed and simulated using a bottom-up stochastic model. Not considering community services and income generating activities, carries a 45 % underestimation on peak demand. In addition, improving people’s living conditions has a considerable effect on the electricity demand of Bolivia’s rural lowland communities

The M-LED platform: advancing electricity demand assessment for communities living in energy poverty

Globally about 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa. Planning electricity access infrastructure and allocating resources efficiently requires a careful assessment of the diverse energy needs across space, time, and sectors. Because of data scarcity, most country or regional-scale electrification planning studies have however assumed a spatio-temporally homogeneous (top-down) potential electricity demand. Poorly representing the heterogeneity in the potential electricity demand across space, time, and energy sectors can lead to inappropriate energy planning, inaccurate energy system sizing, and misleading cost assessments. Here we introduce M-LED, a Multi-sectoral Latent Electricity Demand geospatial data processing platform to estimate electricity demand in communities that live in energy poverty. The platform shows how big data and bottom-up energy modelling can be leveraged together to represent the potential electricity demand with high spatio-temporal and sectoral granularity. We apply the methodology to Kenya as a country-study and devote specific attention to the implications for water-energy-agriculture-development interlinkages. A more detailed representation of the demand-side in large-scale electrification planning tools bears a potential for improving energy planning and policy

Archetypes of Rural Users in Sub-Saharan Africa for Load Demand Estimation

In the context of estimating the electricity demand of rural areas a set of archetypes of end users is proposed, in order to serve as input for off-grid energy planning models. The archetypes are proposed for residential users, schools and health centers and the set of archetypes is designed based on variation of latitude, climate zone and relative wealth of the user, with an applicability over the entire Sub-Saharan Africa (SSA). The nature of the archetypes is designed so that they can be integrated with both Energy System Optimization Models and Geospatial Electrification models. In the context of this work, they are applied to three case studies of off-grid system sizing in Nigeria, Kenya and Mozambique to highlight the flexibility of the tool

Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy

Energy system models for off-grid systems usually tend to focus solely on the provision of electricity for powering simple appliances, thus neglecting more energy-intensive and critical needs, such as water heating. The adoption of a Multi-Energy System (MES) perspective would allow us not only to provide comprehensive solutions addressing all types of energy demand, but also to exploit synergies between the electric and thermal sectors. To this end, we expand an existing open-source micro-grid optimization model with a complementary thermal model. Results show how the latter achieves optimal solutions that are otherwise restricted, allowing for a reduction in the Levelized Cost of Energy (LCOE) of 59% compared to a conventional microgrid, and an increase of reliance on renewable sources of 70%

Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities

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 (R2 ¼ 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R2 ¼ 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

Access to Electricity in Informal Settlements: Literature Review and Load Curve Estimation

Today, about one in four people in cities live in informal settlements with severe lack of basic needs such as modern, safe, and reliable energy services. This study analyses the topic of electricity access in informal settlements, apporting two main important contributions. Firstly, a systematic literature review regarding the electricity access in informal settlements is presented, followed by an integrative literature review, highlighting a gap in this study field. Secondly, a load curve estimation based on survey data from an informal settlement in Nairobi is presented. Results show a load curve with two peaks (260 kW around 9:30 and 210 kW around 21:45) and overall, an energy consumption of 2.8 MWh/day. To conclude, the research examines the extended energy deprivation suffered by the community, which results in an average increase of 143% in the electricity tariff

Modelling of a village-scale Multi-Energy System (MES) for the integrated supply of electric and thermal energy

peer reviewedEnergy system models for off-grid systems usually tend to focus solely on the provision of electricity for powering simple appliances, thus neglecting more energy-intensive and critical needs, such as water heating. The adoption of a Multi-Energy System (MES) perspective would allow not only to provide comprehensive solutions addressing all types of energy demand, but also to exploit synergies between the electric and thermal sectors. To this end, we expand an existing open-source micro-grid optimization model with a complementary thermal model and show how the latter allows to achieve optimal solutions that are otherwise restricted