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Justo, C.D.;  Tafula, J.E.;  Moura, P. Sustainable Energy Systems in Southern African Development Community. Encyclopedia. Available online: https://encyclopedia.pub/entry/32717 (accessed on 18 April 2024).
Justo CD,  Tafula JE,  Moura P. Sustainable Energy Systems in Southern African Development Community. Encyclopedia. Available at: https://encyclopedia.pub/entry/32717. Accessed April 18, 2024.
Justo, Constantino Dário, José Eduardo Tafula, Pedro Moura. "Sustainable Energy Systems in Southern African Development Community" Encyclopedia, https://encyclopedia.pub/entry/32717 (accessed April 18, 2024).
Justo, C.D.,  Tafula, J.E., & Moura, P. (2022, November 03). Sustainable Energy Systems in Southern African Development Community. In Encyclopedia. https://encyclopedia.pub/entry/32717
Justo, Constantino Dário, et al. "Sustainable Energy Systems in Southern African Development Community." Encyclopedia. Web. 03 November, 2022.
Sustainable Energy Systems in Southern African Development Community
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The Southern African Development Community (SADC) region includes all the countries located in the southernmost region of Africa, with the Democratic Republic of Congo on the northern boundary of the region. In 2017, more than 341 million people were living in SADC, representing around 33% of SSA’s total population of 1.02 billion, and with an annual growth rate of 2%.  Hydropower is the main renewable energy source in most member states, including Angola, the Democratic Republic of Congo, Lesotho, Malawi, Mozambique, Namibia, and Zambia. Therefore, to accelerate energy access for a higher share of the population, countries like South Africa, Botswana, and Zimbabwe are continuing to exploit mainly non-renewable energy sources, particularly coal. Additionally, SADC needs to face the reduction of GHG emissions to ensure sustainability, and to this end, every effort should be made to guarantee carbon neutrality and a zero-carbon economy.

renewable energy electricity access generation expansion planning low electrification sustainable energy

1. Renewable Sources in Generation Expansion Planning

The transition to a low- or zero-carbon economy is strongly related to the integration of RES into Generation Expansion Planning (GEP) [1]. GEP is a process that aims to reach an optimal generation mix, by determining the time, location, size, and type of different candidate generating facilities/sources to meet the future load demand, guaranteeing that the power system stays reliable [2][3], within a security requirement over a planning horizon of typically 10–30 years [4]. This is one of the most discussed topics within academia and among decision-makers and plays a critical role in the power sector and sustainable energy development [2][5][6].
GEP initially tackled the needs of centralized power systems, with vertically integrated state-owned electric utilities having a monopoly in the generation, transmission, distribution, and retail sectors. Traditionally, it aimed at minimizing costs, whether of installation, operation, or maintenance, becoming a mono-objective problem. However, the liberalization of electricity markets, as well as technological developments, have led to a rapid transformation of power systems and electricity markets [6]. In addition, with the increase of climate change concerns, aiming to reduce GHG emissions from the power sector, the integration of RES into power systems has been considered, due not only to the growing environmental concerns, but also due to economic reasons, given the quick reduction of the levelized cost of energy (LCOE) of some renewable technologies, like wind and solar photovoltaic power [5][7][8]. This turns the GEP into a multi-objective problem with several aspects to be evaluated, for instance, economic, technical, and environmental issues.
The literature presents several models that have been used to determine the GEP. These are identified as Mixed-Integer Nonlinear Programming (MINLP) [4][9], Mixed Integer Linear Programming (MILP) [10][11][12][13], Multi-Objective Linear Programming (MO-LP) [14][15][16] and Multi-Objective Mixed Integer Linear Programming (MO-MILP) [2][3][4][17]. Studies by [15] and [16] present MO-LP, while [18] presents MO-MILP. Both formulations were used to assess renewable energy penetration into GEP. However, the integration of RES presents challenges to be overcome, since most of these sources rely on external weather conditions (hydropower, wind, and solar photovoltaics), being designated as Intermittency Renewable Energies Sources (IRES). Such intermittency represents the main limitation for integration into the electrical grid in many countries [8][19][20][21][22], since such generation fluctuates in time and space following its driving climate variables, mostly precipitation, wind speed, solar radiation, and temperature. Therefore, IRES does not offer the same services as conventional generators and, as a consequence, high IRES penetration levels in the energy sector, if not carefully anticipated, could hinder power system management, and strongly increase the power supply costs [23]. Additionally, there are concerns about the capacity of some grids to cope with IRES [24], and some utilities and grid operators often argue that today’s power systems cannot accommodate significant variable wind and solar supplies [8], without affecting supply reliability and power quality [25].
In addition to its variability characteristics, it is also difficult to control, or easily adjust, the power generation output, mainly for wind and solar photovoltaics, making these highly non-dispatchable sources of energy and causing operational uncertainty, due to the mismatch between demand and generation [2][17][22][26]. As RES penetration has increased rapidly in recent years, power system flexibility, ramping capability, and sufficient reserves are urgently required to deal with the inherent intermittency and uncertainty of RES [1][2]. In a scenario where intermittent renewables are widely deployed, the power system must have adequate means to compensate for the effects of variability and unpredictability in the availability of wind and solar power [25]. This is necessary to plan for a long-term, sustainable, future power generation system, mainly for developing countries, since access to electricity has not yet reached the entire population [27].
Some approaches, technologies, and strategies to compensate for the effects caused by the variability and unpredictability of IRES on a large scale can be found, namely in [28][29], such as the following: operating reserve, interconnection with other grids, complementarity between renewable sources, demand-side management (DSM), energy storage (i.e., batteries, hydrogen, flywheels, pumped hydro storage, electricity-to-thermal, power-to-gas and vehicle-to-grid), supply-side flexibility, and infrastructure (i.e., super-grids, smart grids, microgrids and smoothing effects of spatial power distribution).

1.1. Complementarity between Renewable Energy Sources

Although the IRES technologies are already implemented in most power systems (with different levels of penetration), traditionally, the compensation of the effects caused by intermittency and uncertainty has been mainly ensured through hydropower reservoirs in countries with high potential for hydropower [30]. Over the decades, hydropower has been the major contributor to renewable energy and the cheapest option for renewable power generation [30][31], currently representing more than 50% of all renewable electricity worldwide [32]. However, most of the locations with the highest potential around the world are being explored [33], and the construction of large hydropower plants is a slow process with relevant environmental and social impacts. Therefore, due to the increase in environmental restrictions, there has been a reduction in the construction of hydropower projects [15].
Additionally, the use of hydropower is conditioned by the geographical topology, as well as by climatic factors, since the annual rainfall cycle forces the system to store water during the wet period to be used in the dry season, to guarantee stability in the generation of energy [30][33]. Extreme climatic events, due to climate change, such as droughts, are more likely to occur in the coming years and will be responsible for reducing the flow of rivers, compromising more than half of hydropower generation, and decreasing the reliability of the energy systems based on hydropower [15][28]. This loss of output, due to the drying climate, could shift generation to fossil fuels, making it more difficult for developing countries like southern African countries to meet their climate change mitigation commitments under the Paris Agreement to the United Nations Framework Convention on Climate Change (UNFCCC) [34].
The focus on wind and photovoltaic solar power has been followed by many countries, leading to recent RES additions [35]. However, the increasing participation of IRES requires greater flexibility in the power system, which will not be achieved with the use of hydropower only [28][36]. Therefore, it is essential to take advantage of the complementarity between different options of RES and the integration of dispatchable technologies, such as biomass and geothermal. Complementarity is defined as the ability of a spatial–temporal distributed mix of generation resources to enable better electricity supply conditions, reducing operational challenges and the need to add enabling technologies, due to improved matching of outputs to demand profiles [37]. This concept is also defined in [38] as the ability of two or more energy sources to work together, complementing generation, and improving energy performance.
Complementarity is one effective way to reduce the energy storage requirements in hydropower reservoirs [8], as well as to guarantee power-system flexibility [39], increasing the balance between energy generation and demand [8], and smoothing the effect of the variable renewable resource output [37][40][41]. By evaluating the complementarity between multiple RES and determining the best combination of RES, the impact of the variability from each RES can be mitigated [42]. Since different power supply technologies have different operational characteristics that could complement each other, the deployment of renewable technologies cannot be planned in isolation from the rest of the power system, but rather needs to be looked at from the perspective of their integration into the system [31]. By combining generation systems with minimum and maximum power at different periods, the variability of power generation is reduced [43]. There is potential for energy sources to complement each other on a temporal, spatial, or spatio–temporal scale, thereby ensuring supply reliability and minimizing fluctuations or shortages in power output. This improves the efficiency and reliability of the energy supply and reduces the need for energy storage [44].
Complementarity is commonly expressed in terms of a correlation coefficient [45], Pearson’s correlation coefficient being the most widely used and accepted method to investigate and quantify the degree of correlation between pairs of RES variables [43][44][45][46][47]. The Person’s correlation coefficient measures the association strength between two specific energy generation options, with values ranging from −1 to +1 [43][47][48]. A negative value implies anti-correlation between variables, meaning that when the value of one increases the other decreases, and vice-versa. This characteristic is the most important characteristic when it comes to complementarity. On the other hand, a positive value implies a correlation between variables and a zero value implies that no association exists between the two variables. The literature presents several studies where the complementarity between RES was applied. Many of these studies are related to assessing the complementarity between solar and wind energy [17][37][38][39][41], solar and run-of-the-river energy [8], hydropower and offshore wind power [30], solar, wind and tidal [49], and wind, solar and hydropower [41][42][45][46][47]. There are also assessments of complementarity between regions, for instance in Brazil [28], Colombia [46] and Latin America [47].

1.2. Feasibility of 100% Renewable Power Systems

A methodology is presented in [30] to introduce the complementarity between RES into the GEP to ensure the demand in 2050 is met with 100% RES addressing the possibility of complementing the hydro system with offshore wind power in the Brazilian case. An objective function is proposed to optimize the solar, wind, biomass, and hydropower mix, without loss-of-load or curtailment of intermittent technology, and optimize the water flow of hydropower reservoirs, considering daily and yearly variations.
A multi-objective method for optimizing the renewable energy mix by maximizing the contribution of renewable energy to the peak load and minimizing the combined intermittence at the minimum cost is proposed in [25]. This model was applied in the Portuguese context to optimize the complementarity between renewable energy sources, instead of only using one renewable energy source. The greatest load-loss concern associated with the large-scale integration of wind, water, and solar energy (WWS), due to its variability and uncertainty, is addressed. A new grid integration model was used in [7] to find low-cost, no-load-loss, nonunique solutions to the problem of electrification of all energy sectors in the United States, considering also pumped hydropower, hydrogen, and demand response, and without considering fossil fuels. As a result, from 2050 to 2055, the social cost of a reliable 100% WWS system should be much lower than the cost associated with the use of fossil fuels.
The question of the complementarity between hydropower based on run-of-the-river generation of small rivers (with less than 1000 km2) and solar energy for a scenario of 100% renewable energy supply was considered in a region of Northern Italy in [8]. The potential value of complementarities of wind–solar resources to California’s power grid was assessed in [17]. The study was performed using one-year hourly demand data, together with the hourly-simulated output of various solar and wind technologies distributed throughout the state.
With support policy, Ukraine could reach a 100% renewable power system by 2050, according to [36], deploying solar, wind, water, biomass, and geothermal resources. A long-term planning model dedicated to the French power system was used by [23] to explore different levels of RES penetration, adding new technologies (ocean energy, energy storage, new interconnections, and demand-response). An hourly simulation of future electricity generation based on wind, solar and tidal data, to assess the feasibility of a 100% renewable energy system in Japan, was conducted in [50], to ensure that electricity demand in the year 2030 can be met.
The high-resolution energy system model REMix was applied in Brazil in [24] so as to analyze the least-cost composition and operation of a fully renewable power supply system. The results revealed that the expansion of wind and solar power was more cost-efficient than the construction of additional hydropower plants and indicated that a completely renewable power system in Brazil would not lead to a significant increase in costs, despite the additional transmission and generation capacity. For the same country, [15] presented a multi-objective model for GEP with a high share of RES, promoting the use of non-hydro RES. The results showed that, to meet the government’s goals and the peak demand, solar power was the main non-hydro renewable source to ensure expansion, since its daily curve coincided with the peak load period. To ensure 100% RES scenarios in the Brazil context, a non-linear multi-objective problem for power generation expansion planning was proposed, that maximizes complementarity and minimizes total expansion costs, with battery storage integration, complementarity between sources and regions, DSM, hydropower storage requirements and spatial technology distribution with hourly resolution [51]. The methodology was able to guarantee that three consecutive years of extreme drought in 2050 could be encountered without the need for new large reservoirs.
A multi-objective framework for finding optimal storage and a renewable generation mix for the Chilean power system for the year 2050 was developed, considering three optimization criteria for a 100% renewable power supply, specifically: minimizing investment and operational costs of the power system, environmentally friendly operation of hydropower reservoirs, and minimizing additional transmission systems [1]. A feasible transition strategy is presented to achieve 100% renewable energy considering all German energy sectors in [52]. Such a study demonstrated that it is possible to transition the German energy system to a 100% renewable energy supply within sustainable domestic renewable resources, and simultaneously keep costs at an acceptable level, similar to the energy system costs of a 2050 reference system. Seven scenarios were modeled in [53] for a 100% renewable European power system in 2050 that could operate with the same level of system adequacy as the current power system, even when relying only on domestic European sources in the most challenging weather.

1.3. Power Management Strategies for Systems with Renewable Energy Sources Penetration

The penetration of RES is an increasingly growing topic, but due to the intermittent behavior of the main renewable sources, namely hydro, solar, and wind power, whether off-grid or on-grid, power systems need to face special requirements. Therefore, in cases of high penetration of these sources, management strategies must be adopted in order to deal with the deficits and surpluses of generated power. In the literature, some strategies have been pointed out to minimize the impacts of RES penetration into power systems.
For instance, an energy management strategy in a hybrid wind/solar/fuel cell plant is proposed, using a self-made simulation tool developed in MATLAB/Simulink, in [54]. The objective was to improve some very simplified models that are used by HOMER to avoid unfair results and erroneous conclusions. In other work, a two-stage energy management strategy was developed for networked microgrids under the presence of highly renewable resources [55]. The proposed method, according to the presented results, could identify optimal scaling results, reduce operational costs of risk aversion, and mitigate the impact of uncertainties. A day-ahead optimal energy management strategy for the economic operation of industrial microgrids with high-penetration renewables under both isolated and grid-connected operation modes was also proposed in [56]. Such an approach is based on a regrouping particle swarm optimization, formulated over a day-ahead scheduling horizon with one hour time steps, and considering forecasted renewable energy generations and electrical load demands.
The advantages and disadvantages of three main strategies were also assessed [57], which included storing the surplus generation by electrical storage, converting it to hydrogen, or injecting it into the main grid. A new Real-Time Hierarchical Congestion Management technique was also proposed that reschedules generators in two stages, based on the Available Congestion Clearing Time of the transmission lines in the presence of renewable energy sources [58]. Chaotic Darwinian Particle Swarm Optimization was also used for determining the optimal schedules of demand response loads and rescheduling conventional generators to mitigate congestion. The solar and wind energy sources were modeled using Rayleigh and Beta probability density functions.
A novel demand response integrated day-ahead energy management framework subjected to remote off-grid power systems is presented in [59]. The results demonstrated that for an isolated remote community in Northern Canada, for both summer and winter seasons, it was possible to achieve the intended objectives of day-ahead energy management. A different work, [60], proposed a management system for a future household equipped with controllable electric loads and an electric vehicle equipped with a PV–Wind–Battery hybrid renewable system connected to the national grid. The proposed management system was based on a linear programming model with non-linear constraints solved with MATLAB toolboxes. The simulation was based on a database of meteorological conditions resulting from TRNSYS and processed to achieve a frequency of one hour.
Uncertain behaviors are also considered in several studies. For instance, a novel reinforcement learning approach to the management of a microgrid incorporating the uncertain behavior of electric vehicles and renewable energy sources was developed in [61]. In order to predict the output power of the renewable energy sources of wind units and solar panels, the proposed approach used a Q-learning technique, which enhanced the prediction of conventional models, such as neural networks. A new management methodology to find the optimum operation of a grid-connected microgrid, which was modeled as an optimization approach and aimed to minimize total cost was presented in [62]. The uncertainties in renewable energy-based distributed generation units, including wind turbines and PV panels, were also considered herein. The simulation results demonstrated that the proposed methodology could supply all required electricity and simultaneously optimize power transactions between the microgrid and the main grid.

2. Power Systems Planning in the Sub-Saharan Context

2.1. Generation Expansion Planning

In the literature, several studies have already addressed the issue of GEP. Some have models to optimize energy generation, using energy storage, both through batteries and hydro reservoirs. Other studies consider a power system 100% based on RES, through resource optimization, and applying complementarity between the RES and regions. However, such models are focused on countries with a high electrification rate and are not suitable for most developing countries, where the electrification rate remains very low and there is limited electrical infrastructure. For most developing countries, these models need to be adapted, as access to electricity is still a challenge, mainly in rural communities, being directly related to poverty levels. The adoption of long-term GEP in developing countries changes the problem in many fundamental ways compared to traditional formulations.
The following three crucial aspects that characterize the problems of long-term energy planning in developing countries have not been addressed in the actual GEP literature [63]: (1) the presence of substantial planned suppressed demand due to insufficient initial power infrastructure, as evidenced by electrification rates below 100%; (2) the challenge of dealing with highly unequal access to electricity on a sub-national level; (3) the importance of integrating on-grid and off-grid electrification options into an expansion planning optimization model. Off-grid solutions have been found to be cheap electrification alternatives in many developing countries and, thus, need to be part of an integrated planning approach [52].
Access to clean and reliable electricity services is a prerequisite for sustainable development and an important factor to promote, among other things, economic activities, poverty reduction, health, education, and gender equality [64][65]. Electricity plays an important role in modern society, and the lack of this resource has become a major barrier to the development of most SSA countries. In order to deal with the problem of the low electrification rate in SSA countries, [63] presents a long-term, spatially explicit MO-MILP energy planning model to expand the long-term GEP suitable for developing countries with limited initial electricity infrastructure. The model was applied to the case of the Ugandan national power system and the achieved results showed that, in contrast to the government’s focus on grid extension, which would imply sub-national electrification inequality, widespread electrification equality could be cost-optimal if off-grid options were considered. Therefore, although the study considered the integration of decentralized solutions for rural electrification, the large-scale integration of renewable into power systems and strategies, such as complementarity between renewable energy sources, was not addressed.
The Open Source Spatial Electrification Tool (OnSSET) was applied to two ECOWAS countries, Burkina Faso, and Côte d’Ivoire [66], to determine the optimal combination of grid-connected and off-grid systems to serve rural and urban demand by 2030, using high-resolution data by geospatial information systems (GIS), highlighting the fundamental role of off-grid solar photovoltaics and wind technologies in bridging the electricity access gap, particularly in Burkina Faso. Least-cost electrification strategies were provided on a country-by-country basis for SSA using a GIS, i.e., datasets derived from satellite imagery and from a plethora of existing maps to fill data gaps coupled with OnSSET in [64]. In both studies, the electrification options included grid expansion, micro-grids, and stand-alone systems in rural, suburban, and urban contexts across the economy, becoming a powerful tool to design effective electrification strategies in developing countries. The results showed that, in the case of low electricity demand (rural areas), there is a strong penetration of stand-alone technologies, while in areas with higher demand the cheap electrification options shift from stand-alone systems to micro- and mini-grids, as well as to grid extensions.
Likewise, with the objective to find the least-cost electricity solutions for SSA, the area was subdivided into 16 sub-regions, hourly resolved, and based on 100% RES in [65]. A detailed least-cost optimization model was developed to estimate the additional cost of power generation and provide grid access for households that currently do not have electricity and to inform decision-makers and other stakeholders of the true costs of providing electricity in SSA in [67]. A geo-referenced LCOE model was used in [68] to reach 100% electricity access in Burkina Faso to provide power to more people quickly by mini-grids, instead of the national policy for electrification through grid extension and the government subsidizing fossil fuel electricity production.
The indicative power plant investment plan was assessed in [69] regarding forty-seven African countries, based on the power transaction potential. Using OSeMOSYS (a cost optimization tool for long-term energy planning) a cost-effective system configuration was developed. A comparison of the scenario before 2040 with the establishment of the TEMBA (Electrical Model Bank of Africa) business relationship shows that an improved grid could change the power generation mix in Africa and reduce the cost of power generation. The possibility of using hybrid systems was investigated in Nigeria [70], considering PV/Diesel with or without batteries. The results showed that the LCOE value for PV/Diesel was lower than for a diesel-only system. The same approach was applied in Burkina Faso, [71], to ensure electricity access to rural areas. Renewable off-grid systems, mainly using solar photovoltaic generation (solar systems for private households and small grids), could provide technically feasible and economical solutions to the energy problem, mainly when the cost of expanding the grid is not profitable [65].
In order to study the long-term planning of power generation capacity in developing countries, a new multi-period stochastic optimization model was proposed and used in Ghana [72]. The results showed that for most rural areas, single-grid and micro-grid are the cheapest options and could be used as a temporary solution until the extension of the power grid. A multi-step integer linear programming method was proposed for South-Eastern Nigeria in [73], to design a renewable energy network based on hybrid solar and wind power plants that would take advantage of energy resource availability and demographical conditions.
It can be concluded from the literature that, to improve electricity access over SSA, it is essential to consider off-grid solutions based on RES. The traditional method of electrifying SSA by expanding the grid has not contributed to the eradication of energy poverty in rural areas. To do so, the selection of technologies for rural electrification must consider socio-economic feasibility, geographical potential, and compatibility of renewable technology options. In addition, spatial analysis and feasibility studies help determine the most cost-effective electrification options for remote areas. Renewable energy, especially solar photovoltaic energy, can be used to improve services, due to its decentralization, playing an important role in promoting rural electrification [65].

2.2. Renewable Energies in SADC Power Systems

Over SADC member states, studies have been conducted to enhance the exploitation of RES and their integration into the power system.

2.3. SADC Member States’ Programs and Renewables Targets

The Paris Agreement, introduced in 2015, at the United Nations Framework Convention on Climate Change (UNFCCC), aims to keep global warming below 2 °C, limiting it to 1.5 °C compared to pre-industrial levels. To be part of this international agreement, each country prepared a National Determined Contributions (NDC) document. In these documents, countries communicated the actions they would take to reduce GHG emissions. Such national contributions naturally imply the reduction of electricity generation by fossil fuels, and the increase of generation through RES.
The SADC member states assumed and ratified this agreement, having in mind that programs with different horizons were elaborated, but whose transversal objective was the gradual increase of penetration of renewables into the national energy mix. The main aim is to reduce dependence on fossil fuels, and ensure greater sustainability, as well as explore different sources of electricity generation, mainly to supply rural areas, which are essentially characterized by low electricity consumption and are located far from the main urban centers. Thus, the proposal of the SADC member states involves the integration of renewables in the national energy mix and the implementation of off-grid systems powered by renewable sources, such as solar PV, wind, and mini-hydro.

References

  1. Haas, J.; Nowak, W.; Palma-Behnke, R. Multi-objective planning of energy storage technologies for a fully renewable system: Implications for the main stakeholders in Chile. Energy Policy 2019, 126, 494–506.
  2. Li, Q.; Wang, J.; Zhang, Y.; Fan, Y.; Bao, G.; Wang, X. Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources. Sustainability 2020, 12, 1083.
  3. Pourmoosavi, M.A.; Amraee, T.; Firuzabad, M.F. Expansion planning of generation technologies in electric energy systems under water use constraints with renewable resources. Sustain. Energy Technol. Assess. 2020, 43, 100828.
  4. Hemmati, R.; Saboori, H.; Jirdehi, M.A. Multistage generation expansion planning incorporating large scale energy storage systems and environmental pollution. Renew. Energy 2016, 97, 636–645.
  5. Dagoumas, A.S.; Koltsaklis, N.E. Review of models for integrating renewable energy in the generation expansion planning. Appl. Energy 2019, 242, 1573–1587.
  6. Koltsaklis, N.E.; Dagoumas, A.S. State-of-the-art generation expansion planning: A review. Appl. Energy 2018, 230, 563–589.
  7. Jacobson, M.Z.; Delucchi, M.A.; Cameron, M.A.; Frew, B.A. Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes. Proc. Natl. Acad. Sci. USA 2015, 112, 15060–15065.
  8. François, B.; Borga, M.; Creutin, J.D.; Hingray, B.; Raynaud, D.; Sauterleute, J.F. Complementarity between solar and hydro power: Sensitivity study to climate characteristics in Northern-Italy. Renew. Energy 2016, 86, 543–553.
  9. Zhang, N.; Hu, Z.; Shen, B.; He, G.; Zheng, Y. An integrated source-grid-load planning model at the macro level: Case study for China’s power sector. Energy 2017, 126, 231–246.
  10. Pozo, D.; Sauma, E.E.; Contreras, J. A Three-Level Static MILP Model for Generation and Transmission Expansion Planning. IEEE Trans. Power Syst. 2013, 28, 202–210.
  11. Guerra, O.J.; Tejada, D.A.; Reklaitis, G.V. An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems. Appl. Energy 2016, 170, 1–21.
  12. Georgiou, P.N. A bottom-up optimization model for the long-term energy planning of the Greek power supply sector integrating mainland and insular electric systems. Comput. Oper. Res. 2016, 66, 292–312.
  13. Sharan, I.; Balasubramanian, R. Integrated generation and transmission expansion planning including power and fuel transportation constraints. Energy Policy 2012, 43, 275–284.
  14. Ren, H.; Zhou, W.; Nakagami, K.; Gao, W.; Wu, Q. Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects. Appl. Energy 2010, 87, 3642–3651.
  15. Luz, T.; Moura, P.; de Almeida, A. Multi-objective power generation expansion planning with high penetration of renewables. Renew. Sustain. Energy Rev. 2018, 81, 2637–2643.
  16. Zhang, Q.; Mclellan, B.C.; Tezuka, T.; Ishihara, K.N. An integrated model for long-term power generation planning toward future smart electricity systems. Appl. Energy 2013, 112, 1424–1437.
  17. Solomon, A.A.; Kammen, D.M.; Callaway, D. Investigating the impact of wind-solar complementarities on energy storage requirement and the corresponding supply reliability criteria. Appl. Energy 2016, 168, 130–145.
  18. Muis, Z.A.; Hashim, H.; Manan, Z.A.; Taha, F.M.; Douglas, P.L. Optimal planning of renewable energy-integrated electricity generation schemes with CO2 reduction target. Renew. Energy 2010, 35, 2562–2570.
  19. Antunes, C.H.; Martins, A.G.; Brito, I.S. A multiple objective mixed integer linear programming model for power generation expansion planning. Energy 2004, 29, 613–627.
  20. Koltsaklis, N.E.; Dagoumas, A.S.; Kopanos, G.M.; Pistikopoulos, E.N.; Georgiadis, M.C. A spatial multi-period long-term energy planning model: A case study of the Greek power system. Appl. Energy 2014, 115, 456–482.
  21. Unsihuay-Vila, C.; Marangon-Lima, J.W.; de Souza, A.C.Z.; Perez-Arriaga, I.J. Multistage expansion planning of generation and interconnections with sustainable energy development criteria: A multiobjective model. Int. J. Electr. Power Energy Syst. 2011, 33, 258–270.
  22. Gebretsadik, Y.; Fant, C.; Strzepek, K.; Arndt, C. Optimized reservoir operation model of regional wind and hydro power integration case study: Zambezi basin and South Africa. Appl. Energy 2016, 161, 574–582.
  23. Krakowski, V.; Assoumou, E.; Mazauric, V.; Maïzi, N. Reprint of Feasible path toward 40–100% renewable energy shares for power supply in France by 2050: A prospective analysis. Appl. Energy 2016, 184, 1529–1550.
  24. Gils, H.C.; Simon, S.; Soria, R. 100% Renewable energy supply for Brazil-The role of sector coupling and regional development. Energies 2017, 10, 1859.
  25. Moura, P.S.; de Almeida, A.T. Multi-objective optimization of a mixed renewable system with demand-side management. Renew. Sustain. Energy Rev. 2010, 14, 1461–1468.
  26. Caldera, U.; Breyer, C. Impact of Battery and Water Storage on the Transition to an Integrated 100% Renewable Energy Power System for Saudi Arabia. Energy Procedia 2017, 135, 126–142.
  27. Khan, I. Power generation expansion plan and sustainability in a developing country: A multi-criteria decision analysis. J. Clean. Prod. 2019, 220, 707–720.
  28. da Luz, T.; Moura, P. Power generation expansion planning with complementarity between renewable sources and regions for 100% renewable energy systems. Int. Trans. Electr. Energy Syst. 2019, 29, e2817.
  29. Lund, P.D.; Lindgren, J.; Mikkola, J.; Salpakari, J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew. Sustain. Energy Rev. 2015, 45, 785–807.
  30. Silva, A.R.; Pimenta, F.M.; Assireu, A.T.; Spyrides, M.H.C. Complementarity of Brazils hydro and offshore wind power. Renew. Sustain. Energy Rev. 2016, 56, 413–427.
  31. IRENA. African Power Pool: Planning and Prospects Energy; IRENA: Abu Dhabi, United Arab Emirates, 2013.
  32. REN21. Renewables 2020 Global Status Report; REN21 Secretariat: Paris, France, 2020.
  33. Johnson, B.; Denholm, P.; Kroposki, B.; Hodge, B. Achieving a 100% Renewable Grid: Operating Electric Power Systems with Extremely High Levels of Variable Renewable Energy. IEEE Power Energy Mag. 2017, 15, 61–73.
  34. Spalding-Fecher, R.; Joyce, B.; Winkler, H. Climate change and hydropower in the Southern African Power Pool and Zambezi River Basin: System-wide impacts and policy implications. Energy Policy 2017, 103, 84–97.
  35. REN21. Renewables 2022: Global Status Report; REN21: Paris, France, 2022.
  36. Child, M.; Breyer, C.; Bogdanov, D.; Fell, H.J. The role of storage technologies for the transition to a 100% renewable energy system in Ukraine. Energy Procedia 2017, 135, 410–423.
  37. Solomon, A.A.; Child, M.; Caldera, U.; Breyer, C. Exploiting wind-solar resource complementarity to reduce energy storage need. AIMS Energy 2020, 8, 749–770.
  38. Gallardo, R.P.; Ríos, A.M.; Ramírez, J.S. Analysis of the solar and wind energetic complementarity in Mexico. J. Clean. Prod. 2020, 268, 122323.
  39. Jurasz, J.; Mikulik, J.; Dąbek, P.B.; Guezgouz, M.; Kaźmierczak, B. Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis. Energies 2021, 14, 1118.
  40. Weschenfelder, F.; Leite, G.D.N.P.; da Costa, A.C.A.; de Castro Vilela, O.; Ribeiro, C.M.; Ochoa, A.A.V.; Araujo, A.M. A review on the complementarity between grid-connected solar and wind power systems. J. Clean. Prod. 2020, 257, 120617.
  41. Sun, W.; Harrison, G.P. Wind-solar complementarity and effective use of distribution network capacity. Appl. Energy 2019, 247, 89–101.
  42. de Oliveira Costa Souza Rosa, C.; da Silva Christo, E.; Costa, K.A.; dos Santos, L. Assessing complementarity and optimising the combination of intermittent renewable energy sources using ground measurements. J. Clean. Prod. 2020, 258, 120946.
  43. Kougias, I.; Szabó, S.; Monforti-Ferrario, F.; Huld, T.; Bódis, K. A methodology for optimization of the complementarity between small-hydropower plants and solar PV systems. Renew. Energy 2016, 87, 1023–1030.
  44. Miglietta, M.M.; Huld, T.; Monforti-Ferrario, F. Local Complementarity of Wind and Solar Energy Resources over Europe: An Assessment Study from a Meteorological Perspective. J. Appl. Meteorol. Climatol. 2016, 56, 217–234.
  45. Canales, F.A.; Jurasz, J.; Beluco, A.; Kies, A. Assessing temporal complementarity between three variable energy sources through correlation and compromise programming. Energy 2020, 192, 116637.
  46. Henao, F.; Viteri, J.P.; Rodríguez, Y.; Gómez, J.; Dyner, I. Annual and interannual complementarities of renewable energy sources in Colombia. Renew. Sustain. Energy Rev. 2020, 134, 110318.
  47. Viviescas, C.; Lima, L.; Diuana, F.A.; Vasquez, E.; Ludovique, C.; Silva, G.N.; Huback, V.; Magalar, L.; Szklo, A.; Lucena, A.F.; et al. Contribution of Variable Renewable Energy to increase energy security in Latin America: Complementarity and climate change impacts on wind and solar resources. Renew. Sustain. Energy Rev. 2019, 113, 109232.
  48. Jurasz, J.; Canales, F.A.; Kies, A.; Guezgouz, M.; Beluco, A. A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions. Sol. Energy 2020, 195, 703–724.
  49. Neto, P.B.L.; Saavedra, O.R.; Oliveira, D.Q. The effect of complementarity between solar, wind and tidal energy in isolated hybrid microgrids. Renew. Energy 2020, 147, 339–355.
  50. Esteban, M.; Portugal-Pereira, J.; Mclellan, B.C.; Bricker, J.; Farzaneh, H.; Djalilova, N.; Ishihara, K.N.; Takagi, H.; Roeber, V. 100% renewable energy system in Japan: Smoothening and ancillary services. Appl. Energy 2018, 224, 698–707.
  51. Luz, T.; Moura, P. 100% Renewable energy planning with complementarity and flexibility based on a multi-objective assessment. Appl. Energy 2019, 255, 113819.
  52. Mahapatra, S.; Dasappa, S. Rural electrification: Optimising the choice between decentralised renewable energy sources and grid extension. Energy Sustain. Dev. 2012, 16, 146–154.
  53. Zappa, W.; Junginger, M.; van den Broek, M. Is a 100% renewable European power system feasible by 2050? Appl. Energy 2019, 233–234, 1027–1050.
  54. Cozzolino, R.; Tribioli, L.; Bella, G. Power management of a hybrid renewable system for artificial islands: A case study. Energy 2016, 106, 774–789.
  55. Wang, D.; Qiu, J.; Reedman, L.; Meng, K.; Lai, L.L. Two-stage energy management for networked microgrids with high renewable penetration. Appl. Energy 2018, 226, 39–48.
  56. Li, H.; Eseye, A.T.; Zhang, J.; Zheng, D. Optimal energy management for industrial microgrids with high-penetration renewables. Prot. Control Mod. Power Syst. 2017, 2, 12.
  57. Tabar, V.S.; Abbasi, V. Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem. Energy 2019, 189, 116264.
  58. Namilakonda, S.; Guduri, Y. Chaotic darwinian particle swarm optimization for real-time hierarchical congestion management of power system integrated with renewable energy sources. Int. J. Electr. Power Energy Syst. 2021, 128, 106632.
  59. Kaluthanthrige, R.; Rajapakse, A.D. Demand response integrated day-ahead energy management strategy for remote off-grid hybrid renewable energy systems. Int. J. Electr. Power Energy Syst. 2021, 129, 106731.
  60. Chakir, A.; Abid, M.; Tabaa, M.; Hachimi, H. Demand-side management strategy in a smart home using electric vehicle and hybrid renewable energy system. Energy Rep. 2022, 8, 383–393.
  61. Wei, L.; Yi, C.; Yun, J. Energy drive and management of smart grids with high penetration of renewable sources of wind unit and solar panel. Int. J. Electr. Power Energy Syst. 2021, 129, 106846.
  62. Ali Dashtaki, A.; Mehdi Hakimi, S.; Hasankhani, A.; Derakhshani, G.; Abdi, B. Optimal management algorithm of microgrid connected to the distribution network considering renewable energy system uncertainties. Int. J. Electr. Power Energy Syst. 2023, 145, 108633.
  63. Trotter, P.A.; Cooper, N.J.; Wilson, P.R. A multi-criteria, long-term energy planning optimisation model with integrated on-grid and off-grid electrification—The case of Uganda. Appl. Energy 2019, 243, 288–312.
  64. Mentis, D.; Howells, M.; Rogner, H.; Korkovelos, A.; Arderne, C.; Zepeda, E.; Siyal, S.; Taliotis, C.; Bazilian, M.; De Roo, A.; et al. Lighting the World: The first application of an open source, spatial electrification tool (OnSSET) on Sub-Saharan Africa. Environ. Res. Lett. 2017, 12, 085003.
  65. Barasa, M.; Bogdanov, D.; Oyewo, A.S.; Breyer, C. A cost optimal resolution for Sub-Saharan Africa powered by 100% renewables in 2030. Renew. Sustain. Energy Rev. 2018, 92, 440–457.
  66. Bissiri, M.; Moura, P.; Figueiredo, N.C.; Pereira da Silva, P. A geospatial approach towards defining cost-optimal electrification pathways in West Africa. Energy 2020, 200, 117471.
  67. Valickova, P.; Elms, N. The costs of providing access to electricity in selected countries in Sub-Saharan Africa and policy implications. Energy Policy 2021, 148, 111935.
  68. Moner-Girona, M.; Bódis, K.; Huld, T.; Kougias, I.; Szabó, S. Universal access to electricity in Burkina Faso: Scaling-up renewable energy technologies. Environ. Res. Lett. 2016, 11, 084010.
  69. Taliotis, C.; Shivakumar, A.; Ramos, E.; Howells, M.; Mentis, D.; Sridharan, V.; Broad, O.; Mofor, L. An indicative analysis of investment opportunities in the African electricity supply sector—Using TEMBA (The Electricity Model Base for Africa). Energy Sustain. Dev. 2016, 31, 50–66.
  70. Adaramola, M.S.; Paul, S.S.; Oyewola, O.M. Assessment of decentralized hybrid PV solar-diesel power system for applications in Northern part of Nigeria. Energy Sustain. Dev. 2014, 19, 72–82.
  71. Ouedraogo, B.I.; Kouame, S.; Azoumah, Y.; Yamegueu, D. Incentives for rural off grid electrification in Burkina Faso using LCOE. Renew. Energy 2015, 78, 573–582.
  72. Afful-Dadzie, A.; Afful-Dadzie, E.; Awudu, I.; Banuro, J.K. Power generation capacity planning under budget constraint in developing countries. Appl. Energy 2017, 188, 71–82.
  73. Ikejemba, E.C.X.; Schuur, P. Locating solar and wind parks in South-Eastern Nigeria for maximum population coverage: A multi-step approach. Renew. Energy 2016, 89, 449–462.
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