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A longitudinal study of electricity consumption growth in Kenya
Abstract
During the past 5 years, electrification in Kenya has grown by more than 30% due primarily to increases in grid penetration and solar home systems. This represents a way forward for governments, international finance institutions, and entrepreneurs to address some of the challenges of energy access. However, little is understood about how consumption has evolved among these newly-electrified customers. In this paper, we address this by conducting a longitudinal analysis for 136k utility customers across Kenya over six years of electricity bills, uncovering critical trends in spatio-temporal evolution of electricity consumption. Our analysis reveals that recently-electrified customers are reaching their steady-state consumption more quickly than previous customers, that the steady-state is increasingly less, and that typical urban and peri-urban customers tend to consume 50% more electricity than rural customers. In addition we present implications for policymakers and electricity planners considering grid extension and distributed systems for improving electrification.
... However, the strategy does not take into account the differences in consumption growth among customers. For instance, a better understanding of customers' electricity consumption behavior before connecting them can result in fewer underutilized grid connections that can be used to connect more customers [7]. Thus, the electrification strategy may miss estimate the likely near-term energy demand. ...
... The research conducted by [7] and [20] based on data-driven approaches aims at understanding diverse Kenya Power (national utility) customer behavior to improve electricity access planning. This study takes a similar approach by conducting an exploratory analysis of historical residential electricity consumption aiming to improve access to electricity planning. ...
... The reduction in consumption may be due to the energy efficiency of customers. This possible explanation is supported by the analysis conducted by [7] for 136k utility residential customers across Kenya from 2010 through 2015. In addition, [22] found that over the 2007-2012 periods, increased energy efficiency appear to be the most important contributor to decreased electricity growth rates in the United State. ...
Electrical energy plays a vital role in daily life. The developing world is making good progress in improving access to electricity toward United Nations Sustainable Development Goals number 7 through better energy planning. Understanding energy consumption growth remains a fundamental aspect of energy planning. This study aimed to investigate the dynamic of electricity consumption growth in Togo. The analysis uses a data-driven approach to determine the relationship between average electricity consumption and the electrification rate. As a result of this study, the average electricity consumption per connection decreases as the electrification rate increases. This gives utility and the government insights regarding electricity access planning. Since the average consumption is decreasing, low consumers can be aggregated on the same transformer. Also, off-grid solutions are suitable for areas located far away from the grid connection. Using these findings, the utility may reduce costs related to grid extension.
... However, the strategy does not take into account the differences in consumption growth among customers. For instance, a better understanding of customers' electricity consumption behavior before connecting them can result in fewer underutilized grid connections that can be used to connect more customers [7]. Thus, the electrification strategy may miss estimate the likely near-term energy demand. ...
... The research conducted by [7] and [20] based on data-driven approaches aims at understanding diverse Kenya Power (national utility) customer behavior to improve electricity access planning. This study takes a similar approach by conducting an exploratory analysis of historical residential electricity consumption aiming to improve access to electricity planning. ...
... The reduction in consumption may be due to the energy efficiency of customers. This possible explanation is supported by the analysis conducted by [7] for 136k utility residential customers across Kenya from 2010 through 2015. In addition, [22] found that over the 2007-2012 periods, increased energy efficiency appear to be the most important contributor to decreased electricity growth rates in the United State. ...
... To estimate electricity sales revenues, forecast demand over the system life cycle is multiplied by a volumetric (per kWh) tariff. Historical consumption data from Kenya power are used to build demand forecasting models [14]. We make the assumption that the tariff levels are the same for grid connected customers and for minigrid connected customers. ...
... The network covers both urban and rural areas. We define the rural extent of the grid network coverage using methods developed by Fobi et al [14], in which they used a k-means clustering algorithm making use of data on population density, land use classification and satellite nighttime light intensity to classify locations in Kenya as urban, peri-urban and rural. In the service area contained in our dataset, we found 180 independent rural LV networks. ...
... We use historical electricity consumption data from customers in rural Kenya as described in Fobi et al [14] to construct a demand forecasting model. Figure 4 shows the scatter plots of average monthly consumption for rural Kenya power customers connected to the grid between 2009 and 2013, and the lines of best fit. ...
A global push to achieve universal electricity access, paired with drastic reductions in the cost of decentralized electricity technologies, has led to significant research on how best to roll out access to rural communities in sub-Saharan Africa. Various geospatial electrification models have been developed to aid the decision-making process considering decentralized grid alternatives such as mini-grids and solar home systems. Despite these tools suggesting that in many cases, decentralized systems are a more cost-effective electricity access pathway, grid extension still predominates in practice. This is due, at least in part, to institutional structures in most countries that provide significant direct and indirect subsidies to grid extension projects, commonly through publicly-owned utilities. These sources of finance are generally not available to primarily privately operated off-grid energy service providers. However, the subsidy provided for grid extension projects is not well understood. In this paper, we employ utility grid extension costs and revenue data, and geospatial grid infrastructure data to estimate the size and distribution of subsidy implicitly provided to rural grid extension projects for 129 communities in Mombasa County, Kenya. We also estimate subsidies for hypothetical off-grid electricity systems in the same communities that would deliver equivalent services to the grid. We allocate the cost of shared \DIFdelbegin \DIFdel{MV }\DIFdelend \DIFaddbegin \DIFadd{medium voltage (MV) distribution }\DIFaddend infrastructure using a marginal and an average cost method for grid extension and compare these with subsidies for off-grid systems. We find that the average of average subsidy per customer across communities for grid extension is US\5,118 and US\5,330 for the two MV cost allocation methods respectively, while for the off-grid systems the corresponding average of average subsidies are US\$3,380, using a real discount rate of 1.3\% evaluated from a nominal discount rate of 8\% and inflation rate of 6.7\%. Our results show that in the communities in our case study, 40\% and 37\% of the communities would command less subsidy while served by minigrids over the grid, and the switch would save 50\% and 54\% of the total cost for average and marginal cost allocation methods respectively. We also show that by using a multi-model approach to electrification and by reallocation of implicit subsidies that have been exclusive to grid extension to other technology options utilities can cast the net wider, without an increase in budgets.
... Given the relative lack of publicly available data on electricity in SSA, we only found one study that investigated how demand evolved over time for newly connected customers. According to the longitudinal study electricity demand is declining for newly connected customers in Kenya (Fobi et al 2018). Using monthly electricity bills of 136 000 utility customers in Kenya, Fobi et al (2018) showed that the median newly connected customer tends to rapidly increase consumption until about a year of connection, after which consumption plateaus and eventually starts to decline. ...
... According to the longitudinal study electricity demand is declining for newly connected customers in Kenya (Fobi et al 2018). Using monthly electricity bills of 136 000 utility customers in Kenya, Fobi et al (2018) showed that the median newly connected customer tends to rapidly increase consumption until about a year of connection, after which consumption plateaus and eventually starts to decline. Furthermore, they found that electricity consumption among customers that were connected after 2009 tended to peak much sooner than customers that were connected before 2009. ...
... Hence, the increase in number of new rural customers in Kenya due to its national electrification plan could ultimately lead to an overall decrease in electricity consumption growth over time because of the increasing proportion of rural electricity customers with relatively less purchasing ability/income than their urban counterparts. Although neither Allee et al (2021) nor Fobi et al (2018) predicted latent demand for electricity, their results show that a latent demand prediction model would need to consider decreasing electricity consumption from new rural customers, and the LASSO model should be studied more since it has the lowest mean absolute error of all the demand prediction models in this review. ...
Universal access to electricity is an essential part of sub-Saharan Africa’s path to development. With the United Nations setting Goal 7 of its Sustainable Development Goals to be universal access to clean, reliable and affordable electricity, substantial research efforts have been made to optimize electricity supply based on projected demand in sub-Saharan African (SSA) countries. However, most of these projections of electricity demand do not explicitly account for latent demand (i.e., electricity demand that would exist if the necessary techno-economic conditions were met). Our paper reviews electricity demand estimation and consumption literature to propose a framework for quantifying latent demand. In our study, we found that of the 56 papers reviewed only 3 (5%) of them incorporated latent demand in their projection of electricity demand in SSA. Majority of the literature on electricity consumption and demand estimation in SSA use econometric models to identify determinants of electricity consumption and project future demand. Furthermore, we identified population density, urbanization, household income, electricity price, market value of crops and availability of natural resources to be significant determinants of electricity consumption in SSA. We conclude the review by proposing a methodology for more accurately projecting latent demand in sub-Saharan Africa. Incorporating latent demand in electrification models would help inform energy sector stakeholders in SSA, especially investors and policymakers, about which sectors and geographic locations hold potential for wealth creation via electricity access.
... As such, the sustainability of such MG business models relies on achieving target levels of use by connected customers. However, electricity consumption data from both grid and MG connections shows a troubling pattern of persistently low electricity demand as electricity reaches deeper into rural Africa (Fobi et al., 2018;Williams et al., 2017). Utility operators and MG developers face a similar challenge: how to encourage new customers to consume, and pay for, more electricity? ...
... Third, in the specific case of MGs, the cost of unsubsidized electricity may simply be too expensive. While the marginal cost of grid electricity is a fraction of that for MG electricity (Pueyo and DeMartino, 2018), similar trends of stagnating growth in demand have been observed with grid customers (Fobi et al., 2018). This list is by no means exhaustive. ...
... These findings are relevant beyond the mini-grid sector. Grid operators in sub-Saharan Africa also struggle with low consumption in rural areas that, combined with high infrastructure costs per connection, result in significant financial losses (Fobi et al., 2018). Low levels of electricity use also suggest that public funds invested in grid extension may not be achieving significant economic development gains, at least in the short term. ...
Solar mini-grids are a key element in strategies to achieve universal access to modern energy by 2030. In many settings mini-grids offer a combination of affordability, reliability, and capacity for productive use of power, moreso than most solar home systems and some central grids. Yet the economic sustainability of mini-grids relies on achieving target usage levels, and consumption data to date suggest that they may be commercially unsustainable due to consistently low demand for power once installed—and that newly-connected recipients cannot take full advantage of access. Using a uniquely fine-grained data set spanning 29 villages in East Africa, we test whether credit constraints and the cost of electricity hinder demand growth among mini-grid-connected households. We find that households that purchased appliances under a financing program increased consumption by up to 66 percent compared to matched controls, though a sensitivity analysis suggests this estimate is rather sensitive to bias from unobservable characteristics, and the increase is not sustained. While most customers in the program do not repay loans in full, we find that on average, customers repay about 78 percent of the loan amount. When we analyze developers’ return on investment, we find that the profitability of appliance financing programs at a market cost of capital, similar to those evaluated in this study, depends substantially on the types of appliances on offer. For the tariff subsidy program, we find that lowering the cost of electricity by up to 75 percent substantially increased consumption, albeit with mixed signals for whether overall revenue could be maintained at a lower tariff.
... For instance, a case study in Sri Lanka reports a lag of 5 to 7 years between the provision of electricity access and the formation of new businesses [69]. Also, the number of connected households or businesses and the average electricity demand in a newly connected community tends to increase over time as people become aware of the benefits that electricity access can provide [70]. Lastly, as the economic conditions of newly electrified areas improve due to the economic stimulating capabilities of electrification, the use of household lightbulbs and streetlights that can influence NTL sensors can be expected to increase gradually over time. ...
... Fobi et al. [70] showed that newly connected electricity users consume less electricity than older customers. Furthermore, the difference between the average radiance of electri-fied and unelectrified pixels appears to be higher in Kenya than in Rwanda, likely due to the fact that the average annual electricity consumption in Kenya is more than double that of Rwanda [73]. ...
Remotely sensed nighttime light data have become vital for electrification mapping in data-scarce regions. However, uncertainty persists regarding the veracity of these electrification maps. This study investigates how characteristics of electrified areas influence their detectability using nighttime lights. Utilizing a dataset comprising the locations, installation date, and electricity purchase history of thousands of electric meters and transformers from utilities in Rwanda and Kenya, we present a systematic error assessment of electrification maps produced with nighttime lights. Descriptive analysis is employed to offer empirical evidence that the likelihood of successfully identifying an electrified nighttime light pixel increases as characteristics including the time since electrification, the number of meters within a pixel, and the total annual electricity purchase of meters in a pixel increase. The performance of models trained on various temporal aggregations of nighttime light data (annual, quarterly, monthly, and daily) was compared, and it was determined that aggregation at the monthly level yielded the best results. Additionally, we investigate the transferability of electrification models across locations. Our findings reveal that models trained on data from Rwanda demonstrate strong transferability to Kenya, and vice versa, as indicated by balanced accuracies differing by less than 5% when additional data from the test location are included in the training set. Also, models developed with data from the centralized grid in East Africa were found to be useful for detecting areas electrified with off-grid systems in West Africa. This research provides valuable insight into the characterization of sources of nighttime lights and their utility for mapping electrification.
... tional grid extension exclusively will prove uneconomic and enormously time-consuming, rendering full electrification by 2030 increasingly unrealistic. Extending national grids comes with exorbitant connection costs for individual households due to the geography to be bridged and the dispersion of households to be connected (Bos et al., 2018;Fobi et al., 2018;Ferrall et al., 2021). 6 Lee et al. (2016a) point out that, currently, policies to expand electricity access are not supported by evidence, which possibly is the reason for stubbornly low electrification rates in rural areas despite substantial investments in (national) grid infrastructure. ...
... Similarly, Lenz et al. (2017) estimate the median monthly energy need of rural Rwandan households at 6 kWh. On the urban-rural divide for Kenya, Fobi et al. (2018) point out that urban households consume around twice as much energy as their rural counterparts. Therefore, off-grid microgrids should be considered for their drastically lower cost 8 to consumers and utilities (Grimm et al., 2019), higher reliability in providing energy, and for 6 Power line expansion to rural areas of Sub-Saharan Africa alone may cost as much as USD 20,000 per km (Rawn and Louie, 2017). ...
Around 800 million people globally do not have access to electricity-most of those affected reside in rural areas in the Global South. The challenge of rural electrification is particularly pronounced in Africa, though pockets of South Asia are similarly afflicted. In these areas, national grid extension is often prohibitively expensive owing to geography. To allow for the reaping of documented benefits access to electricity bestows, and to retain the prospect of reaching Sustainable Development Goal 7 by 2030 (access to affordable, reliable, sustainable and modern energy for all), affordably implementable alternatives to national grid extensions require exploring. To date, policymakers around the world have, however, paid comparably little attention to off-grid solutions suited to local conditions, such as microgrids powered by sustainable sources of energy, despite potential cost-and time-savings in rural electrification. Analyzing a unique, newly constructed data set covering two years of peer-to-peer trading data from 104 solar-powered microgrids across Bangladesh yields important lessons on aspects to consider in designing and efficiently leveraging such microgrids in electrifying rural areas. Results presented underline the importance of (i) sufficient solar power generation capacity in a microgrid, with backup power supply by so-called micro-utilities constituting an important tool to enhance microgrid performance; (ii) the composition and geographic setup of microgrids are crucial, with ideally no households in a given location opting out of connecting to the microgrid and a sufficient dispersion of production capacity to meet demand close by, minimizing transmission losses; (iii) electrically run appliances are a precondition for the intensive-margin utility of a microgrid, allowing for peer-to-peer energy sellers to capitalize on their investments in solar panels and batteries. These findings underscore the importance the literature has found complementary services play. Such services range from support in financing microgrid infrastructure and appliance purchases to skills training aimed at buttressing productive-use uptake of newly-gained electricity access. Policy support in establishing microgrids and the provision of complementary services furthermore proves to be a worthwhile long-term endeavor. Even the arrival of national grid connections at numerous of the solar microgrids investigated did not diminish their utility owing to the need for backup power and these microgrids' continued capacity to provide clean energy reliably.
... Given the rapid growth of the energy sector [55], many political measures are required to achieve national political goals in the framework of energy [56]. Issues in the current energy markets highlighted policy areas that need adjustment [57], leading to the development of the NEP 2015 [58]. The goals of the new NEP includes Advancing energy resources to fulfill internal needs and streamline energy interchange [59], accelerating the expansion and development of energy substructures to restore a modern energy service mix [60], promoting the adoption of clean energy, create an atmosphere conducive to private storage in the energy sector [61], expanding energy resources [62], decorating energy efficiency and management of all subsectors [63], maximizing the interests of the Tanzanian government and people, full strategic participation and fair sharing of benefits [64], ensuring responsible management of oil resources and revenues for long-term benefits to society [58], Promoting the utilization of regionally manufactured commodities and services in the oil and gas sector, fortify institutional, legal, and regulatory structures, allocate resources to human capital to foster the sustainable expansion of the energy domain [65], and ultimately encouraging the acceptance of environmental [66], well-being, protection and controlling standards in the energy segment [66,67]. ...
... Issues in the current energy markets highlighted policy areas that need adjustment [57], leading to the development of the NEP 2015 [58]. The goals of the new NEP includes Advancing energy resources to fulfill internal needs and streamline energy interchange [59], accelerating the expansion and development of energy substructures to restore a modern energy service mix [60], promoting the adoption of clean energy, create an atmosphere conducive to private storage in the energy sector [61], expanding energy resources [62], decorating energy efficiency and management of all subsectors [63], maximizing the interests of the Tanzanian government and people, full strategic participation and fair sharing of benefits [64], ensuring responsible management of oil resources and revenues for long-term benefits to society [58], Promoting the utilization of regionally manufactured commodities and services in the oil and gas sector, fortify institutional, legal, and regulatory structures, allocate resources to human capital to foster the sustainable expansion of the energy domain [65], and ultimately encouraging the acceptance of environmental [66], well-being, protection and controlling standards in the energy segment [66,67]. The policy document covers diverse domains or segments, including power (production, transfer, distribution, linkage, power transaction, and countryside electrification), oil and gas (upriver, midway, and downriver operations), sustainable power, energy preservation, and comprehensive issues (subsidies, institutional, legal, regulatory, monitoring, and assessment frameworks) [68] ...
The central objectives of this study are to locate existing research on renewable energy, examine the energy policy of Tanzania, assess bibliometric factors, determine the direction of the current research, and comprehend unexplored research topics. This exploration focuses on a bibliometric-based study using computer-assisted software tools known as VOS viewer and RStudio in analyzing the Scopus data retrieved package for the key phrase of "Renewable energy" in the article titles published from 2002 to 2022. A total of 661 publications (which is only 0.45% and 6.3% of the global and continental publications from Africa respectively) were analysed after refining using different bibliometric criteria like study site, type of document, publication stage, language used in the document, and publication time interval. The results shows that Energy fuels, engineering, technology, environmental sciences, ecology, and business economics are the most frequently studied fields. Also, from a total of 661 publications, only 32 documents were published from Tanzania for 20 years from 2022 which is less than 2 publication/year. This study concludes that there is a notable lack of research output from Tanzania in this critical field. This gap underscores the need for greater investment in renewable energy research and development within the country, as well as targeted efforts to build research capacity and foster collaboration among academia, government, and industry stakeholders.
... Voltage drops (Fobi et al., 2018 ;Yin et al., 2020) are more or less recurrent in some of Bujumbura's peri-urban localities, such as Ngagara, Tenga, Rubirizi, Kanyosha, Gatunguru, etc. Unfortunately, this is the result of the actions of some of their customers because some of the equipment in use today does not meet internationally recognized standards, and has been purchased from private stores. ...
... Unfortunately, this is the result of the actions of some of their customers because some of the equipment in use today does not meet internationally recognized standards, and has been purchased from private stores. Indeed, the distance between an electrical transformer and the point of energy consumption, household for example (Fobi et al., 2018;Wu et al., 2021) should not exceed 1000m. Beyond this distance, not only will there be frequent voltage drops, but the current carried will have lost much of its quality. ...
Bujumbura has been the capital city of Burundi for a long time and has changed into an economic city since 2015. This means the majority of economic activities are located in Bujumbura and most of them require electricity. This article aims to analyze the public policy layout towards electricity distribution among urban people in Bujummbura. We proceeded by documentary technique. Results reveal that the importance of electricity is undoubtedly as long as development cannot be done without electricity. However, Bujum-bura is marked as a dark city where many quarters are not lit even tonight. Entrepreneur-ship is in most parts of the city due to the lack of electricity where the population is accustomed to anytime shortage and cuts. This is the consequence that due to the population increasing time after time, REGIDESO is becoming impossible to cover the population's electricity needs. In the former time, electricity equipment in Bujumbura was to be furnished to governmental authority residential quarters. Peripheral ones were not in number. As long as REGIDESO, a public sector enterprise with the exclusive power of producing and selling electricity becomes impossible to cover the needs of the urban inhabitants of Bujumbura, an alternative energy solution is to be applied. This will use solar energy instead of hydroelectricity or combine both opening the gate to private societies or individuals to invest in the electricity domain.
... In their longitudinal study of Kenyan residential customers' electricity consumption, [36] aimed at gaining a better understanding of electric demand evolution over time, thus helping model developers in what was identified in [16] as the most neglected component of energy planning models. Their findings showed an overall increase in consumption over time; however, great discrepancies in trends were pointed out between urban and rural households (with the first having higher increases), highlighting the need to include the urban/rural characterization in future studies. ...
... Measurement_age, suggesting that the likelihood of owning an appliance grows over time once electricity is provided. Analogous conclusions were derived in [26,[34][35][36]. ...
To grant a reliable and affordable electricity provision to non-electrified communities, proper system sizing, based on accurate demand estimation is crucial. However, the absence of historical data, coupled with scarce, scattered and often unreliable pre-electrification surveys, makes this process particularly prone to errors. Acquiring data, especially with high quality and detail, is often difficult, time consuming and expensive. Even though, in a few site-specific cases the limited data collected have allowed researchers to develop methodologies to generate synthetic demand profiles based on variegated site-specific socio-economic information and appliance adoption patterns, among other parameters. However, given the lack of comprehensive datasets of such information, the use of synthetic methodologies has been circumscribed to limited regional and socio-economic scopes. In this study, by means of a data-driven approach, we propose a machine learning methodology to estimate the appliance adoption in rural areas of developing countries, supported by a review of the drivers of load demand and appliance adoption identified in literature and an extensive data collection to populate the data-driven technique. These data are subsequently harmonized into a database, which consists of 60 drivers for a total of 16252 users in Sub-Saharan Africa. The database, released in open access, has been used to calibrate logistic regression models to estimate the ownership of 8 key appliances, whose accuracy is about 71.7% when most features are used. The output can assist future players in the development of short, yet reliable surveys, ensuring an accurate demand estimation while also limiting time and operational costs.
... Overall, most of the methodologies relied on socioeconomic information at granular level, which are not easily available [26], [29], [74]. Moreover, when input parameters are lacking or of poor quality, the entire energy modelling is compromised, regardless of the quality of the demand assessment tool [21]. ...
... Several reviews focusing on social dynamics and longitudinal socio-econometric studies highlighted that the major classes of information for demand assessment can be grouped into the following major categories [26], [29], [41], [74]: 1) Socio-economic data, such as population information, job type, income, culture 2) Dwelling factors, such as the type and quality of the dwelling 3) Appliance, such as ownership, usage pattern 4) Geographical information, such as location, proximity to major point of interest 5) Supply data, such as type of connection, tariffs 6) Alternative energy sources 7) Past demand Kuster et al. [41] reviewed beyond 100 forecasting models for electricity demand, also highlighting the issue of the many data required for bottom-up approaches devoted to long-term prediction of electric demand. The paper also classified papers based on socio-economic, weather, building and occupancy, and past demand information. ...
Energy Access is a pivotal need for socio-economic growth. Proven to be a key enabler of development and progress, access to electricity has been prioritized by governments using grid extension actions and off-grid solutions, namely microgrids and home systems technologies, fed by renewable sources. However, achieving universal access to energy is still highly challenging, given the lack of resources and the large population currently unserved. The lack of adequate socio-economic data at granular scale and of a good understanding of demand uptake led by economic growth is a barrier for efficient energy planning. Access to cojoint demand and socio-economic data at local level is crucial, yet hard to obtain: often such data are unavailable or very difficult to collect, and current data platforms often lack the ability to conjointly store variegated socio-economic and time series data. For these reasons, in this paper, we present a comprehensive methodology that, based on an extensive literature review, draws guidelines for developing data-sharing platforms in energy access, develops a proposed architecture to support the data collection of conjoint socio-economic and time-series data, and proposes a prototype of the final application. The methodology leverages on a novel extensive literature review to identify the major determinants of demand uptake and the corresponding consuming entities: villages, households and appliances. The proposed architecture is able to capture numeric, categorical and time series information for all consuming entities, based on state-of-the-art NoSQL databases. Finally, a prototype implementation with a web-based interface developed with Angular and Spring is proposed and discussed.
... To give a sense of how much capacity is required to operate the EApp from DES in a rural context, Fig. 2 presents estimates using a simple calculation of the DES capacity to power commonly used EApp in a household (left) and the total electricity capacity required to power EApp for a duration of time (right). The electricity demand in urban areas is greater than the rural areas (Fobi et al., 2018). Fig. 2 (left) uses three benchmark capacities based on rural electrification policies; 25Wh represents the average capacity of subsidized SHS in Nepal using the AEPC dataset. ...
... Thirdly, while we recognize having grid electricity doesn't automatically enable its use and economic returns (Fobi et al., 2018;Lee et al., 2020a;Taneja, 2019), the promotion of low-capacity DES might impact the clean cooking transition in off-grid areas of Nepal. EApp is only one part of the derived demand from electricity. ...
The adoption and use of electronic appliances generally contribute to higher productivity and thus are key to ameliorate living standards of rural households. A wide range of decentralized electricity systems has been used to power remote parts of Nepal. How these systems facilitate the adoption of electronic appliances, is a question under explored in the existing energy policy literature. This study bridges this gap by comparing the electronic appliance adoption among households that use micro-hydropower and solar home systems against traditional lighting solutions and grid electricity in Nepal. Applying the two-stage least-square method to data from Nepal's population and household census of 2011, we found solar home systems do not increase the use of high-wattage electronic appliances such as televisions and fridges when compared to kerosene, but micro-hydro plants and grid electricity do. This finding indicates that low-capacity electricity sources like solar home systems appear to be limited to facilitate the adoption of high-wattage electronic appliances. It is recommended that energy access policies should look beyond providing basic access to electricity for lighting and prioritize the provisioning of electricity sources that support the use of high-wattage electricity appliance
... Kenya has structured its energy policy in a way that favors high penetration of RE in order to meet its energy demand while reducing greenhouse gas emissions (Oluoch et al., 2021). Although the country's energy policy has recently endeavored to increase RE share in order to face the growing energy demand (Fobi et al., 2018;Moner-Girona et al., 2019), its focus on technoeconomic aspects may lead to non-sustainability. The public involvement is neglected in most developing countries when planning new energy projects (Oluoch et al., 2020). ...
... Many previous studies were conducted in Kenya to evaluate Kenyan energy planning scenarios. However, most of them have analyzed the technical aspect such as dynamic power consumption (Fobi et al., 2018) and demand forecasting (Lahmeyer International, 2016;Mbae and Nwulu, 2020;Otieno et al., 2018), the techno-environmental aspect such as low carbon capacity expansion (Carvallo et al., 2017;Kehbila et al., 2021), the technoeconomic electricity expansion aspect (Moksnes et al., 2020;Moner-Girona et al., 2019) and economic, techno-environmental electricity expansion aspect (Musonye et al., 2021). ...
Kenya expects a high growth in energy demand due to its high demographic and economic growth as well as increasing industrialization. In that regard, the government of Kenya has already shown interest to expand its power supply which includes coal-fired power plants. However, many previous studies conducted to evaluate the Kenyan energy planning scenarios were limited to technical aspect such as dynamic power consumption and demand forecasting; techno-environmental aspect such as low carbon capacity expansion; techno-economic electricity expansion aspect and economic, techno-environmental electricity expansion aspect. The concern of evaluating all the potential Kenyan power options against sustainability dimensions as a whole was not addressed since selecting power technology options has become a multidimensional problem. Therefore, this study aimed at prioritizing Kenyan power technology options using sustainable dimensions: Economic, Social, Environmental and Technical. This research applied Multi-criteria decision making (MCDM) method which is an interesting tool able to bring together several variables to handle a decision making problem. Hence, energy options were evaluated against the four sustainable dimensions (Economic, Social, Environmental and Technical) combining 17 energy indicators and a hybrid AHP–TOPSIS technique was used for that purpose. Results showed that Solar PV and Wind are the most promising technologies in Kenya. Although CSP has not been privileged by Kenyan policymakers, it ranks among the first-three promising technologies, except for economic scenario raking this option the last. Five different analyzed scenarios (Economic privileged, Technical privileged, Environmental privileged, Social privileged, Equal importance) showed the robustness of Solar PV in the all sustainable dimensions. This study has provided a critical policy contribution to the Kenyan government and energy projects investors by solving the dilemma of technologies prioritization in capacity expansion.
... Energy providers, constrained by limited investment budgets, face a perpetual trade-off between expanding electricity access and cost recovery. When consumption levels are low, as can occur in low-income settings, utilities struggle to recover the cost of servicing a grid connection, and the government subsidies [12] for initial capital are poorly utilized. Alternatives to grid extension such as Solar Home Systems (SHS) can support smaller loads without the large wire investments, while in some cases clustered homes (with clusters far from each other) can make mini-grids viable [13]. ...
... Previously [12], we conducted a longitudinal study of 100k+ randomly sampled electrified households, observing that median customers in Kenya typically reach a consistent level of electricity consumption roughly 12 months after receiving an electricity connection. Given this observation, we define the average monthly consumption of a household after 12 months of a connection as the expected stable electricity consumption. ...
In low-income settings, the most critical piece of information for electric utilities is the anticipated consumption of a customer. Electricity consumption assessment is difficult to do in settings where a significant fraction of households do not yet have an electricity connection. In such settings the absolute levels of anticipated consumption can range from 5-100 kWh/month, leading to high variability amongst these customers. Precious resources are at stake if a significant fraction of low consumers are connected over those with higher consumption. This is the first study of it's kind in low-income settings that attempts to predict a building's consumption and not that of an aggregate administrative area. We train a Convolutional Neural Network (CNN) over pre-electrification daytime satellite imagery with a sample of utility bills from 20,000 geo-referenced electricity customers in Kenya (0.01% of Kenya's residential customers). This is made possible with a two-stage approach that uses a novel building segmentation approach to leverage much larger volumes of no-cost satellite imagery to make the most of scarce and expensive customer data. Our method shows that competitive accuracies can be achieved at the building level, addressing the challenge of consumption variability. This work shows that the building's characteristics and it's surrounding context are both important in predicting consumption levels. We also evaluate the addition of lower resolution geospatial datasets into the training process, including nighttime lights and census-derived data. The results are already helping inform site selection and distribution-level planning, through granular predictions at the level of individual structures in Kenya and there is no reason this cannot be extended to other countries.
... Another tool developed in a partnership of five universities is the Electricity Growth and Use in Developing Economies (e-GUIDE), which focuses on macro scale consumption and infrastructure. e-GUIDE integrates satellite imagery and longitudinal consumption data to predict the demand on a national scale [12]. ...
... Data collection at the healthcare facility level in a low-resource context is logistically challenging hence a limiting factor in statistical inference and model accuracy and performance due to small sample sizes and a lack of representative samples with respect to possible variations in the population. Most of the energy access models, including the state-of-the-art planning tools as well as ad hoc studies either integrate satellite imagery with governmental or macroscale data from governments, and/or multilateral organizations (e.g., [11,12]), or use categorical evaluations (e.g., [4,18]). The methodology developed in the DREAM tool enables researchers to collect HCF level data that capture the realities of each HCF in a relatively large sample size through the application of cloud-based data collection. ...
This study presents a multi-platform analysis for accelerating the deployment of distributed renewable energy (DRE) systems for the electrification of healthcare facilities (HCFs) in low-income regions. While existing tools capture national and regional scale planning for DRE deployment in HCFs, there are limited tools for facility level energy needs and no existing data-driven approach for systematic decision-making and resource allocation across a portfolio of HCFs. We address this gap by utilizing decentralized data collection, and multi-criteria decision-making to evaluate each HCF against a set of weighted decision criteria. We applied the approach presented in this research in a case study across 56 HCF in Uganda. Results present current and future energy needs for each individual clinic and the prioritization of HCFs for allocation of resources for DRE deployment. Additionally, results provide insight for best practices for reliability of services that are specific to each HCF. For example, failures in the existing solar photovoltaic (PV) systems are approximately up to 60% due to a lack of proper operation and management (O&M) strategy, and 40% is attributable to improper system design and installation. Thus, this study enables decision-makers to better understand the electrification needs of different HCFs, prioritize DRE deployment, financial investments, cost-effective procurement, and long-term O&M.
... However, of even more concern is the fact that even among those with access to electricity, a vast distribution across access quality tiers exists (Falchetta et al. 2020). For example, in some countries, where rapid growth in electricity access has been reported (e.g., Kenya), the estimated final use among newly electrified households remains very limited and is growing very slowly (Fobi et al. 2018). Such low levels of use suggest that people have not moved beyond subsistence use for lighting and phone charging to levels of demand that provide a means of livelihood through productive uses, enhanced employment, education, and income earning opportunities. ...
... • Monitoring, tracking, estimation, and reporting of multiple access dimensions and latent demand. Recent efforts have used satellite datasets and mobile phones to estimate road quality (Cadamuro et al. 2019), electricity supply outages (Correa et al. 2018), and latent demand for electricity (Fobi et al. 2018, Falchetta et al. 2020). ...
... However, of even more concern is the fact that even among those with access to electricity, a vast distribution across access quality tiers exists (Falchetta et al. 2020). For example, in some countries, where rapid growth in electricity access has been reported (e.g., Kenya), the estimated final use among newly electrified households remains very limited and is growing very slowly (Fobi et al. 2018). Such low levels of use suggest that people have not moved beyond subsistence use for lighting and phone charging to levels of demand that provide a means of livelihood through productive uses, enhanced employment, education, and income earning opportunities. ...
... • Monitoring, tracking, estimation, and reporting of multiple access dimensions and latent demand. Recent efforts have used satellite datasets and mobile phones to estimate road quality (Cadamuro et al. 2019), electricity supply outages (Correa et al. 2018), and latent demand for electricity (Fobi et al. 2018, Falchetta et al. 2020). ...
... However, of even more concern is the fact that even among those with access to electricity, a vast distribution across access quality tiers exists (Falchetta et al. 2020). For example, in some countries, where rapid growth in electricity access has been reported (e.g., Kenya), the estimated final use among newly electrified households remains very limited and is growing very slowly (Fobi et al. 2018). Such low levels of use suggest that people have not moved beyond subsistence use for lighting and phone charging to levels of demand that provide a means of livelihood through productive uses, enhanced employment, education, and income earning opportunities. ...
... • Monitoring, tracking, estimation, and reporting of multiple access dimensions and latent demand. Recent efforts have used satellite datasets and mobile phones to estimate road quality (Cadamuro et al. 2019), electricity supply outages (Correa et al. 2018), and latent demand for electricity (Fobi et al. 2018, Falchetta et al. 2020). ...
Innovations for Sustainability: Pathways to an efficient and sufficient post-pandemic
future, assesses all the positive potential benefits innovation brings to sustainable development for all, while also highlighting the potential negative impacts and challenges going forward. The report outlines strategies to harness innovation for sustainability by focusing on efficiency and sufficiency in providing services to people, with a particular focus on consumption and production. It concludes with the related governance challenges and policy implications.
Innovation has been the foundation of human and societal development since the dawn of civilization. It has resulted in enormous benefits for human wellbeing while at the same time is has brought the world to a critical crossroads where further unconstrained development risks societal and environmental collapse. The current rate and direction of innovation is insufficient to achieve the United Nation’s (UN) ambitious goals for an inclusive sustainable future for all, in part because of a relatively narrow focus on technology innovation without also addressing societal, institutional, and cultural innovation. We need to rebalance so that all dimensions of innovation and invention are promoted simultaneously, including addressing inequities. We also need to develop more proactive efforts to promote diffusion and learning and to address barriers, constraints, and unintended consequences of innovations.
We live in interesting times. They are times of great dangers and uncertainty for humanity and the planet, but times of unprecedented opportunities for directing development toward a just, resilient, and sustainable future. The current coronavirus disease 2019 (COVID-19) pandemic is disrupting the status quo, providing an opportunity to create sustainable societies with higher levels of wellbeing for all and mitigating environmental impacts at all scales. Properly directed, the stimulus packages underway to restart economies can ignite and leverage effects toward sustainability. The risk is that they may promote the resurrection of the ‘old normal,’ going back to business-as-usual, rather than a transformation toward sustainability.
This report, which focuses on innovation, is the third by The World in 2050 (TWI2050) initiative that was established by the International Institute for Applied Systems Analysis (IIASA) and other partners to provide scientific foundations for the UN’s 2030 Agenda for Sustainable Development. This report is based on the voluntary and collaborative effort of more than 60 authors and contributors from about 20 institutions globally, who met virtually to develop science-based strategies and pathways toward achieving the Sustainable Development Goals (SDGs). Presentations of the TWI2050 approach and work have been made at many international conferences such as the United Nations Science, Technology and Innovation Forums and the United Nations High-level Political Forums.
... When funding and policy do arrive, microfinance-based plans are frequently plagued by political instability and socio-economic complications specific to some developing economies [66]. The challenge is highlighted by a recent development in Kenya, which has been the recipient of large efforts to extend the grid to their rural customers, seeing a 30% increase in the number of electrified households from 2013 to 2018 [67]. A key assumption behind the electrification initiative was the well-documented correlation that economies in low-income countries increased proportionately due to electrification rates [68]. ...
Purpose of Review
This paper reviews practical challenges for microgrid electrification projects in low- and middle-income economies, proposing a Social-Technical-Economic-Political (STEP) framework. With our STEP framework, we review recent Artificial Intelligence (AI) methods capable of accelerating microgrid adoption in developing economies.
Recent Findings
Many authors have employed novel AI methods in microgrid applications including to support energy management systems, fault detection, generation sizing, and load forecasting. Despite these research initiatives, limited works have investigated the specific challenges for developing economies. That is, high-income countries often have high-quality power, reliable wireless communication infrastructure, and greater access to equipment and technical skills. Accordingly, there are numerous opportunities for the adaptation of AI methods to meet the constraints of developing economies.
Summary
In this paper, we provide a comprehensive review of the electrification challenges in developing economies alongside an assessment of novel AI approaches for microgrid applications. We also identify emerging opportunities for AI research in the context of developing economies and our proposed STEP framework.
... While future electricity demand is often estimated by extrapolating from the past, there are many examples of unexpectedly high demand [3]. In Kenya, low investment in electricity infrastructure has led to ongoing rolling blackouts [4] even while consumer demand was less than projected [5]. In the United States and Europe, heat waves are driving power consumption to historic levels. ...
A classic multi-period stochastic energy system expansion planning (ESEP) model aims to address demand uncertainty by requiring immediate demand satisfaction for all scenarios. However, this approach may result in an expensive system that deviates from the planner’s long-term goals, especially when facing unexpectedly high demand scenarios. To address this issue, we propose a chance-constrained stochastic multi-stage ESEP model that allows for a portion of demand to remain unmet in specific periods while still ensuring complete demand satisfaction during most of the planning horizon, including the final period. This approach provides more time flexibility to build infrastructure and assess needs, ultimately reducing costs and allowing for a broader view of infrastructure planning options. To solve the chance-constrained stochastic model, we introduce a binary-search-based progressive hedging algorithm heuristic, which is particularly useful for large-scale models. We demonstrate the effectiveness and benefits of implementing the chance-constrained model through a case study of Rwanda using real-world data.
... Furthermore, grid electricity is usually available in cities, yet, it is not accessible to all particularly the urban poor (i.e., slums) who mostly live at neglected localities deprived of basic infrastructure (Karekezi et al., 2008). In rural areas, homesteads are often dispersed and consequently, not connected to grid electricity because of high transmission and distribution costs associated with grid extension (Fobi et al., 2018). The latter is particularly evident in Eastern and Southern Africa where the majority of the rural population resides in dispersed homesteads (Karekezi & Kithyoma, 2002). ...
Clean, diversified and sustainable household energy sources for cooking is essential in order to maintain worthy health for women and children and also improving the energy security of people in the developing countries. Yet, the understanding of household energy dynamics and information remains unclear. This necessitates investigation of transition pathways towards diversification, sustainable and modern household energies. The main objective of this research was to model household energy utilization, changing behaviours and diversification using Structural Equation Modelling (SEM). The specific objectives included: determinants of household energy utilization and changing behaviours; the effects of renewable energy and accessibility on energy utilization, changing behaviour and household diversification of energy sources and finally modeling of the effects of moderators and mediators on the household energy sources diversification. The research was carried out in the counties of Bungoma and Uasin Gishu. Random sampling technique was used to select 640 households from a target household of 663,739 and data was collected using a structured questionnaire. The data was analyzed using AMOS version 23 to achieve the first three objectives. Bootstrapping method was utilized to validate mediation and moderation models. The results showed that firewood is still the most common energy resource used for cooking in both rural and peri urban areas as evidenced by responses of 87.5% and 72.4%, respectively. The use of LPG (26 to 42%), charcoal (39.4% to 53.8%) and kerosene (14.3% to 17.3%) for cooking was found to increase as one moves from rural to peri-urban and vice versa for agricultural residues (12.3% to 5.3%). Biogas uptake still represents a small fraction (11.4 to 14.6%) of the energy mix at local level. The use of solar for lighting showed reduction as one move from rural to peri urban (44.8% to 39.6%) and vice versa for kerosene and electricity. SEM analysis found that factors such as education level, income, residential status, peri urbanization, house size, house composition, age and gender of the household head influence the changing behaviours and diversification among households both for cooking and lighting. Biogas users realized time saving of 1hour 36 minutes on average per household daily with financial saving of KES 2,557 per month as compared to firewood users. In addition, biogas indicated negative association with the use of conventional household energy sources for cooking fuels. Consequently, accessibility increased household fuel utilization and diversification. Interestingly, LPG (Path coefficient () = 0.461, critical ratio (C.R) = 15.204) followed by biogas ( = 0.333, C.R = 11.738) revealed to be the most important contributor to household diversification. The mediating effects of peri urbanization improved the household utilization of charcoal ( = 0.01, C.R = 6.72) kerosene ( = 0.04), LPG ( = 0.01), and conversely for firewood ( = - 0.013, C.R = 8.72) and agricultural residues ( = - 0.01). With income as an independent variable and education as a moderator; number of cars ( = 0.21), peri urbanization ( = 0.01), household size ( = 0.0397), residential status ( = - 0.0396), and gender ( = - 0.104) revealed mediating effects on the household energy diversification. According to bootstrapping reliability test, the limit for Bollen-Stine bootstrap is < 0. 12. In conclusion, household attributes have direct, moderating and mediating effects on the household energy utilization, changing behaviour and diversification. This study showed that household energy changing behaviour and diversification in Kenya are affected by moderating and mediating factors such as peri urbanization, cars among others. This study puts forward the need for policymakers and energy planners in Kenya and other developing countries to improve accessibility (supply and distance) of sustainable fuels and create awareness about the harmful effect of using dirty fuel at early stage through education curriculum, seminars and workshops.
... Tracking of energy poverty and access has generally been carried out through household surveys administered by national governments and international organizations. Satellite-based NTL data can serve as a proxy for electricity access to support electrification planning, complementing traditional survey methods (see example in Figure 4) (Min et al., 2013;Burlig and Preonas, 2016;Dugoua et al., 2017;Fobi et al., 2018;Avtar et al., 2019). These data are often combined with data on population density and other socioeconomic indicators (Stokes and Seto, 2019;Zhao et al., 2019;Falchetta et al., 2020b). ...
Transitioning to a sustainable energy system poses a massive challenge to communities, nations, and the global economy in the next decade and beyond. A growing portfolio of satellite data products is available to support this transition. Satellite data complement other information sources to provide a more complete picture of the global energy system, often with continuous spatial coverage over targeted areas or even the entire Earth. We find that satellite data are already being applied to a wide range of energy issues with varying information needs, from planning and operation of renewable energy projects, to tracking changing patterns in energy access and use, to monitoring environmental impacts and verifying the effectiveness of emissions reduction efforts. While satellite data could play a larger role throughout the policy and planning lifecycle, there are technical, social, and structural barriers to their increased use. We conclude with a discussion of opportunities for satellite data applications to energy and recommendations for research to maximize the value of satellite data for sustainable energy transitions.
... However, recent attempts to increase access to electricity connections in low-income countries have created a set of new and complex challenges for utilities and consumers that collectively reduce service quality (Golumbeanu and Barnes, 2013), and challenge sustainable development (McRae, 2015;Sievert and Steinbuks, 2020;Lukuyu et al., 2021). Among the concerns is low revenue generation for the utility or other providers, both from minimal electricity use by those newly connected and poorly-managed bill collection (Lukuyu et al., 2021;Fobi et al., 2018). Another major challenge is electricity theft, which impedes revenue collection and infrastructure maintenance (Blimpo and Cosgrove-Davies, 2019), though differentiating pilferage from other types of non-technical losses (NTLs) is difficult. ...
In low-income countries such as Ethiopia, pre-paid metering is often argued to alleviate several challenges with traditional electricity billing systems, including high non-payment rate, pilferage and fraud, administrative and enforcement costs for utilities, and inflexibility and incongruence of bills with poorer consumers' irregular income. Despite increasing adoption of this technology, few studies examine its causal impacts on household behaviour. This paper examines the impacts of pre-paid metering on electricity consumption, ownership of appliances, level of satisfaction, and cooking behaviour in Addis Ababa, the capital of Ethiopia. We employ propensity score matching and instrumental variable techniques to control for the non-random selection into pre-paid metering. Results indicate that pre-paid customers have significantly lower electricity consumption compared to those with traditional meters, and express greater satisfaction with utility service. This technology also has a positive, but modest and statistically insignificant impact on total appliance ownership, and a positive and significant impact on ownership of energy-efficient lights. Impacts are heterogeneous across customers, however: those who are more educated, who have higher income, and who do not share meters tend to reduce electricity use more. The results suggest that pre-paid meters have had positive impacts on households and the utility in Addis Ababa.
... In this study, a longitudinal data approach in a spatial heterogeneity model is applied. Compared to previous studies, our methodology enables us to disentangle any variations in the features of the target sample, which is the energy consuming household characteristics within a given region or across regions (Fobi et al., 2018;Upham, 2009). In exploring patterns of changes and dynamics of energy consumer behaviour, the methodology has the potential to identify trends and relationships within the data collected, while providing meaningful insights into cause-and-effect relationships of the variables (Geng and Cui, 2020). ...
Understanding the dynamic behaviour of Sub-Saharan African households as they move along the energy ladder is essential for the energy transition in developing countries. This study applies Fixed and Random effect panel data models to analyse the drivers of rural and urban households' energy transition in Nigeria from 2010 to 2018. The estimation results from the panel models with robust standard errors show that rural households tend to increase their expenses on fuel sources that potentially substitute the energy source whose prices have increased. However, there is no significant relationship between the price and expenditure on different fuels in urban households. Irrespective of spatiality, we find that aside from income – education, household size, and internet access are essential drivers of household fuel choices. More importantly, we find evidence of reverse energy transition. We argue that this reverse energy transition limits the shift to cleaner fuels and increases the economic vulnerabilities of rural households. Our analysis also reveals that Nigerians’ preference for fuels is shifting to be price inelastic. We make a strong case for policies and interventions that raise household income, empower women, reduce the cost of living, and improve clean and affordable energy access to encourage energy transition.
... To realize the full impacts of electrification, increasing access to electricity connections must be accompanied by affordable, reliable, and sustainable electricity consumption and financially sustainable power systems [19,57]. The unfortunate reality is that in most countries in sub-Saharan Africa, increasing electricity access rates have not been accompanied by the same level of consumption growth [22]. Consequently, people often still rely on traditional fuels, especially for cooking, and utilities and mini-grid companies are struggling for financial viability. ...
... The findings are supported by Gates and Yin [41], Zaman et al. [42] and Yang et al. [21] who reported that electricity consumption is affected positively by urbanization. Fobi et al. [43] also found that rural customers use 50% less electricity than urban customers in Kenya. Note that Morocco and Kenya are part of the same group of income "Lower middle-income economies", according to the classification made by the WDI [2]. ...
In a comprehensive LMDI-STIRPAT-ARDL framework, this research investigates the residential electricity consumption (REC)-income nexus in Morocco for the period 1990 to 2018. The logarithmic mean Divisia index (LMDI) results show that economic activity and electricity intensity are the leading drivers of Morocco’s REC, followed by population and residential structure. And then, the LMDI analysis was combined with stochastic impacts by regression on population, affluence, and technology (STIRPAT) analysis and the bounds testing approach to search for a long-run equilibrium relationship. The empirical results show that REC, economic growth, urbanization, and electricity intensity are cointegrated. The results further show that there exists a U-shaped relationship between per capita gross domestic product (GDP) and REC: an increase in per capita GDP reduces REC initially; but, after reaching a turning point (the GDPPC level of 17,145.22 Dh), further increases in per capita GDP increase REC. Regarding urbanization, the results reveal that it has no significant impact on Morocco’s REC. The stability parameters of the short and long-term coefficients of residential electricity demand function are tested. The results of these tests showed a stable pattern. Finally, based on the findings mentioned above, policy implications for guiding the country's development and electricity planning under energy and environmental constraints are given.
... The findings are supported by Gates and Yin [41], Zaman et al. [42] and Yang et al. [21] who reported that electricity consumption is affected positively by urbanization. Fobi et al. [43] also found that rural customers use 50% less electricity than urban customers in Kenya. Note that Morocco and Kenya are part of the same group of income "Lower middle-income economies", according to the classification made by the WDI [2]. ...
... The information collected from the documents listed in Table 3 allows to run the simulations in RAMP using the data in Table 4 and obtain a set of six different electricity load profiles, namely two evolution scenarios per each user class, defined according to the income levels. As often observed in contexts of recent access to electricity, the demand increases in the years following the first access, eventually reaching a stable behaviour (Fobi, Deshpande, Ondiek, Modi, & Taneja, 2018). The load profiles obtained in RAMP refer to this final configuration of the demand, as they are employed to evaluate the limits imposed by the installation of SHS not only on current needs but also on potential demand growth. ...
In the context of last-mile electrification of rural communities, stand-alone solutions like Solar Home Systems are often considered an equivalent option for access to electricity as other technologies, such as minigrids and connection to the main grid. In this work, the authors question this paradigm and propose an alternative approach that takes into account the limited service, and in turn, limited development possibilities, that Solar Home Systems grant to their users. A monetary penalty is applied to this technology based on the missed services. The conceived approach is applied to a case study in the district of Cochabamba, Bolivia, thanks to the cooperation with a local NGO. Results show how the inclusion of shadow costs influences the optimal electrification strategy in terms of users connected to the main grid by up to 33%, highlighting how standard approaches tend to overlook key aspects of access to electricity linked to the possibility of development.
... In this study, 1 kWh/day per household is used as "the basic service package" in all scenarios. This amount coincides with Tier 3 of the MTF scale and meets the current demand of a large part of recently electrified rural households in Kenya [63], where the rural area could be compared to the one in Nyagatare District. ...
In 2019, there were 759 million people globally without access to electricity and 2.6 billion people lacked access to clean cooking. Cooking with electricity could contribute to achieving universal access to energy by 2030. This paper uses geospatially-based techniques—a computer model named REM, for Reference Electrification Model—to show the impact of integrating electric cooking into electrification planning. Three household scenarios were analyzed: one for providing basic electricity access with no electric cooking; another for cooking with electricity; and the third for cooking half of the meals with electricity and half with another fuel, with a clean stacking process. Results of the application of REM to the three scenarios were obtained for the Nyagatare District, Rwanda. The case study showed that electric cooking substantially changes the mix of technologies and the total cost of the least-cost electrification plan. It also showed that electric cooking can be cost competitive compared to LPG and charcoal in grid-connected households and can reduce greenhouse emissions. Stacking with energy-efficient electric appliances provides most of the benefits of full electric cooking at a lower cost and is a pathway worthy of further consideration.
... Furthermore, grid electricity is usually available in cities, yet, it is not accessible to all particularly the urban poor (i.e., slums) who mostly live at neglected localities deprived of basic infrastructure [18]. In rural areas, homesteads are often dispersed and consequently, not connected to grid electricity because of high transmission and distribution costs associated with grid extension [19]. The latter is particularly evident in Eastern and Southern Africa where the majority of the rural population resides in dispersed homesteads [20]. ...
Household energy utilization trends have been argued to be affected by the rate of urbanization. Therefore, due to lack of information there is need to understand the effects of peri urbanization. The main objective of this research was to investigate household energy utilization trends and the effects of peri urbanization on household energy utilization and changing behaviour. The research was carried out in the counties of Bungoma and Uasin-Gishu of Kenya. Random sampling technique was used to select 560 households from a target household of 663,739 and data was collected using a structured questionnaire. The results showed that firewood is still the most common energy resource used for cooking in both rural and peri urban areas as evidenced by responses of 87.5% and 72.4%, respectively. The use of LPG (26 to 42%), charcoal (39.4% to 53.8%) and kerosene (14.3% to 17.3%) for cooking were found to increase as one move from rural to peri-urban and vice versa for agricultural residues (12.3% to 5.3%). Biogas uptake still represents a small fraction (11.4 to 14.6%) of the energy mix at local level. The use of solar for lighting showed reduction as one move from rural to peri urban (44.8% to 39.6%) and vice versa for kerosene (68.4% to 72%) and electricity (55.5% to 58.2%). In conclusion, this study showed that household energy utilization and changing behaviour in Kenya are affected by peri urbanization among others. This study offers understandings in enhancing household energy policy making in Kenya.
... Ahlborg and Hammar [3] find government policies and priorities to be the key driver for electrification, while limited funds and technical capabilities are a barrier. In Kenya, Fobi et al. [13] show that as electrification proceeds to increasingly rural populations, the demand of newly electrified households is lower than previously electrified households, hindering the economic capacity for further electrification. In contrast, Wolfram et al. [45] show that in Brazil, the combination of aggressive electrification policies and financial support for low-income households contributed to successful centralized grid electrification. ...
Sub-Saharan Africa faces unique barriers to electricity development due to the large proportion of the population that is un-electrified and the prevalence of rural populations. Typically, power system expansion planning models assume all potential consumers can be immediately electrified. This assumption is unrealistic in sub-Saharan Africa, where electrification will likely be a gradual process over a number of years. Furthermore, since a large proportion of the population in sub-Saharan Africa is located in rural regions, the prioritization of these regions may impact how the grid develops. In this research, we develop a multi-period optimization model for power generation and transmission system expansion planning in sub-Saharan Africa. In contrast to existing models, which assume full electrification, we consider a variety of electrification policies and analyze the impact of varying the electrification rate and policy on the cost and resources selected for power system expansion. We test our model on a case study of Rwanda. We find that varying the year in which full electrification is reached has a larger impact on cost and generation capacity than varying the electrification policy does, although, when urban and rural regions are considered equitably, more rooftop solar is built. Varying the electrification policies has a larger impact on transmission expansion than on generation expansion and this impact is amplified when starting from zero initial system capacity rather than the original Rwanda system. Additionally, a sensitivity analysis shows that tightening the bounds on CO2eq emissions has a large impact on the generation portfolio and cost.
... Our one-year dataset also fails to distinguish between sites' progression along the load growth curve. A recent longitudinal study of newlyelectrified Kenyan customers found that loads tended to grow for roughly two years before steadying (Fobi et al., 2018). The same study found that rural customers tended to flatten their load growth sooner after connection than urban customers. ...
Mini-grids are the lowest-cost solutions for electrifying many homes and businesses in rural communities with low energy access. Estimates of the electricity demand of unelectrified customers are a crucial input to selecting mini-grid sites, projecting revenue, and sizing system components to provide adequate capacity while minimizing capital costs. Typical customer survey-based demand estimates for these communities — where there are no historical data — are not reliable, typically overpredicting demand. Here, we test a data-driven approach to demand prediction using survey and smart meter data from 1378 Tanzanian mini-grid customers. We found that models incorporating customer survey data into their predictions consistently out-performed a baseline model that did not. Our best-performing model, the LASSO, predicted daily electricity demand with a median absolute error of 66% and 37% for individual connections and mini-grid sites, respectively. Quantitative measures of variable importance show that most survey data are not useful for estimating demand. These results suggest that surveys should prioritize thorough inventories of prospective customers' currently-owned appliances instead of detailed demographic information or self-reported habits and plans. Pairing shortened questionnaires with smart meter data from preexisting mini-grids can improve estimates of initial customer electricity demand significantly compared to standard field practices.
... The technicians receive their hands-on training at a 10 kW solar-hybrid demonstration system at Strathmore University (Strathmore University, 2019). Secondly, in collaboration with government institutions, practical handbooks for site section, licensing, system sizing, and financing of off-grid solar mini-grids were developed (Fobi et al., 2018;Herbert and Phimister, 2019). ProSolar, a solar company based in Kenya, assisted Marsabit and Turkana Counties in developing energy sector plans, which serve as a benchmark in strategizing, mapping, and monitoring the county's distribution and use of energy. ...
With the world's lowest electrification rate, Africa is repositioning to offer its citizens a brighter future. Global renewable energy agencies and international financing to expedite rural electrification fueled by off-grid solar systems are attracting worldwide attention. Currently, 770 million people lack access to electricity on the continent, and more than 60% live in poor rural areas where the national power grid is non-existent. The challenge herein is how to supply electricity to rural population, living on $1.5 a day, at a reasonable power tariff. Although there are opportunities for off-grid solar energy to keep growing in sub-Saharan countries, it is impossible to ignore particular challenges in these countries. This paper focuses on three sub-Saharan counties: Kenya, Ethiopia, and Rwanda. Rwanda, Kenya, and Ethiopia foster off-grid solar systems as the primary solution through rural electrification programs. This paper provides a comparative analysis of the electrification experiences of these countries in terms of sources of funding, the challenges and opportunities they have been experiencing as well as an analysis of policy implications. The results show that off-grid solar systems improve health, ICT, and micro-enterprises in rural areas. However, governments should generate more robust developmental schemes that provide income to rural people that pushes them above the poverty line and enables them to afford off-grid solar products.
... Remotely sensed nighttime light (NTL) has long been considered as a direct measure of socio-economic development, and has served as primary data for a wide range of human-based research 1,2 , such as population modelling 3,4 , GDP estimation 5,6 , urban expansion mapping 7,8 , and EPC estimation 9,10 . Many studies have demonstrated a strong correlation between NTL and EPC at multiple levels, and consequently regression models for such estimation have been built 1,9,[11][12][13][14][15] . However, the relationship between NTL and EPC varies across areas, owing to the local socioeconomic diversity 16 . ...
Spatially explicit information on electric power consumption (EPC) is crucial for effective electricity allocation and utilization. Many studies have estimated fine-scale spatial EPC based on remotely sensed nighttime light (NTL). However, the spatial non-stationary relationship between EPC and NTL at prefectural level tends to be overlooked in existing literature. In this study, a classification regression method to estimate the gridded EPC in China based on imaging NTL via a Visible Infrared Imaging Radiometer Suite (VIIRS) was described. In addition, owing to some inherent omissions in the VIIRS NTL data, the study has employed the cubic Hermite interpolation to produce a more appropriate NTL dataset for estimation. The proposed method was compared with ordinary least squares (OLS) and geographically weighted regression (GWR) approaches. The results showed that our proposed method outperformed OLS and GWR in relative error (RE) and mean absolute percentage error (MAPE). The desirable results benefited mainly from a reasonable classification scheme that fully considered the spatial non-stationary relationship between EPC and NTL. Thus, the analysis suggested that the proposed classification regression method would enhance the accuracy of the gridded EPC estimation and provide a valuable reference predictive model for electricity consumption.
... One study finds that the average residential consumer in 2017 consumed 30 per cent of the electricity that the average residential consumer consumed in 2009. The researchers also note that urban households consume 50 per cent more electricity than rural households [4]. Based on data obtained from a private company operating mini-grids in Kenya, Osiolo et al. [1] report that the average monthly electricity consumption of rural consumers is 5kWh, significantly lower than Nairobi, where it is 200kWh. ...
In the last ten years, electrification in Kenya has proceeded at an astonishing rate. Notwithstanding this feat, household electricity consumption, particularly in rural areas, remains significantly low. Thus, stimulating demand and sustainable consumption are the next critical challenges policymakers face. In this paper, we present a case study of an electrification project that targets workers’ housing inside a commercial tea estate. We use Energy Cultures framework to analyse what motivates and constrains household electricity consumption in rural Kenya. Our findings show that although people give significant consideration to cost, money is not the only determinant. Electricity is desired to the extent that it enables families to carry out socially desirable activities, while service is measured against expectations and aspirations. Although access to the grid influences households’ perceptions of wellbeing, their status as migrant workers has a constraining effect on how they consume electricity. Empowered by technology, households are also increasingly taking charge in shaping their own distinct energy cultures. However, for the most part, this involves finding new ways to reproduce and sustain a way of life that is consistent with their belief systems. Seeing households as embodiments of lifestyles whose energy culture is shaped by their on-going interactions with their physical and social environments, the paper argues for an integrated approach to policy and programmatic interventions.
Purpose of Review
Even with increasing electricity access rates, electricity consumption remains low in Sub-Saharan Africa. To remedy this, policies and strategies to stimulate electricity demand are increasingly critical to ensure socio-economic development in the region. This review summarizes approaches to stimulating electricity demand in different sectors of the economy and highlights key policy considerations to make the electricity sector commercially viable and sustainable in Sub-Saharan Africa.
Recent Findings
Recent demand stimulation strategies include facilitating the growth of existing electricity consumption among residential and commercial customers through appliance financing, and tariff subsidy programs, replacing existing non-electric productive energy uses such as irrigation and agro-processing in the agricultural sector, promoting the adoption of electric vehicles and electric cooking technologies, and establishing new electricity-based enterprises in different sectors of the economy.
Summary
While current demand stimulation strategies have promising socio-economic benefits, their commercial viability and sustainability hinge on key policy considerations that address fundamental gaps, calling for further research in this area.
While many tools and methodologies for assessing social impact exist and are used in the social science and global development fields, there is a lack of standard methods for considering the broader social impact of products in the engineering community. Some reasons these methods are not as widely used in the engineering community include designers not being aware of the methods, or methods not being widely applicable. The purpose of this research is to help designers and researchers find relevant design tools and methods for implementing social impact considerations. This is done through the classification of 374 papers in the Engineering for Global Development (EGD) literature along several dimensions including method purpose, industry sector, social impacts considered, sustainable development goals, paper setting, and data inputs required. This paper describes how designers and researchers can use this set of classified papers to locate relevant design tools and methods to improve social impact considerations in their work.
Electrifying 600 million people in sub-Saharan Africa will require substantial investments. Integrated electrification models inform key policy decisions and electricity access investments in many countries. While current electrification models apply sophisticated geospatial methods, they often make simplistic assumptions about financing conditions. Here we establish cost of capital values, reflecting country and electrification mode (that is, grid extension, minigrids and stand-alone systems), and specific risks faced by investors and integrate them into an open source electrification model. We find that the cost of capital for off-grid electrification is much higher than currently assumed, up to 32.2%. Accounting for finance shifts approximately 240 million people from minigrids to stand-alone systems in our main scenario, suggesting a more cost-effective electrification mode mix than previously suggested. In turn, electrification models based on uniform cost of capital assumptions increase the per kWh cost of electricity by 20%, on average. Upscaling and mainstreaming off-grid finance can lower electrification cost substantially.
This study investigates the evolution of electricity consumption across residential and industrial sectors in Taiwan. By applying the dynamic factor model, we classify the fluctuations in electricity consumption into aggregate, regional, and individual factors, which sheds light on the latent dynamics of the electricity market. The empirical result shows that around 73% of residential electricity consumption variations are explained by the aggregate and regional factors. Moreover, the aggregate factor contributes 79% to the industrial electricity consumption variation. Interestingly, this study finds a distinct pattern of latent aggregate factor development across the two sectors. Finally, this study finds that temperature change and business cycle indicator help to predict changes in common elements in the variations in residential and industrial electricity consumption based on the linear and non-linear Granger causality test. This finding suggests that the aggregate factor index, together with temperature change and business cycle indicators would be great indicators for monitoring electricity consumption in Taiwan.
In Kenya, between 2010 and 2015, the number of Small and Medium Enterprises (SMEs) connected to the grid increased by over 60%. Despite this substantial increase, little is known about the behavioural patterns or conditions that contribute to increased electricity consumption among these SMEs. This study addresses the problem through a longitudinal analysis of monthly electricity bills for over 179,000 grid connected SMEs in Kenya. We then leverage multiple publicly available geospatial datasets to estimate how complementary infrastructural variables (such as access to roads, markets, financial services, and macro/micro-economic conditions) correlate with sustained electricity consumption growth by SMEs. Results from our longitudinal analysis indicate that newly electrified SMEs in urban areas have higher median consumption than their older counterparts while in rural areas, more newly connected SMEs appear to have lower median consumption. We find the effects of complementary infrastructure on SME electricity consumption to be more pronounced in rural areas than urban areas. For example, SMEs located in rural neighborhoods with close proximity to roads, markets or financial service providers are associated with a 10% to 16% increase in electricity consumption while in urban areas, we only observe about a 2% increment in electricity consumption for SMEs within close proximity to roads. All other infrastructural variables are either statistically insignificant or negatively correlated with urban SME electricity consumption.
African electricity access shortfalls have prompted multiple energy planning models to suggest rapid deployment of low-carbon pathways. Instead of being data driven, these scenario-based models often rely on assumptions and political targets. Recently in Nature Energy, Alova et al. propose a solution for integrating local factors into energy planning models.
Until now, electricity system planners in emerging economies have primarily relied on simple assumptions and deductive methods for demand forecasting. Optimization models for both large- and small-scale electrification planning show high sensitivity to this input. Better forecasts could help to right-size infrastructure for initial consumption as well as for expected growth. Since governments always face a litany of competing needs, better understanding electricity demand could allow strained budgets to stretch further
This article explores the gaps in our understanding of how to maximize economic development from increased access and use of modern energy in low-income countries. We argue that despite the obvious value energy has for development, its potential can be enhanced by a better understanding of energy use at an individual and community level. Drawing on qualitative data collected from 75 in-depth interviews with energy users in Kenya, and utilising social practice theory, we find that while people are enthusiastic about energy access, there is a complicated path to traverse before its benefits can be fully exploited and sustainable changes occur. Respondents in our sample had limited ideas for how they could use increased access to improve existing businesses or start new ones, which often reflected their lived experiences rather than innovative applications to facilitate economic activity. Where people could see uses for energy for their businesses there were many barriers to achieving those opportunities, highlighting a complex array of factors beyond simple access and use of energy that drives people’s business decisions. We conclude that to realise the benefits promised by modern energy use, we need more nuanced understandings of energy, and the interlinked role it plays in society.
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