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This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. The AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa. The book draws upon a number of background papers that were prepared by World Bank staff and consultants, under the auspices of the AICD. The main findings were synthesized in a flagship report titled Africa's infrastructure: A time for transformation, published in November 2009. Meant for policy makers, that report necessarily focused on the high-level conclusions. It attracted widespread media coverage feeding directly into discussions at the 2009 African union commission heads of state summit on infrastructure.

Information and communication technologies (ICTs) have been a remarkable success in Africa. Across the continent, the availability and quality of service have gone up and the cost has gone down. In just 10 years dating from the end of the 1990s mobile network coverage rose from 16 percent to 90 percent of the urban population; by 2009, rural coverage stood at just under 50 percent of the population. Although the performance of Africa's mobile networks over the past decade has been remarkable, the telecommunications sector in the rest of the world has also evolved rapidly. Many countries now regard broadband Internet as central to their long-term economic development strategies, and many companies realize that the use of ICT is the key to maintaining profitability. This book is about that challenge and others. Chapters two and three describe the recent history of the telecommunications market in Africa; they cover such issues as prices, access, the performance of the networks, and the regulatory reforms that have triggered much of the investment. This part of the book compares network performance across the region and tries to explain why some countries have moved so much more quickly than others in providing affordable telecommunications services. Chapter four explores the financial side of the telecommunications revolution in Africa and details how the massive investments have been financed and which companies have most influenced the sector. Chapter five deals with the future of the sector. The final chapter synthesizes the main chapters of the book and presents policy recommendations intended to drive the sector forward.

Electrification of transport offers opportunities to increase energy security, reduce carbon emissions, and improve local air quality. Plug-in electric vehicles (PEVs) are creating new connections between the transportation and electric sectors, and PEV charging will create opportunities and challenges in a system of growing complexity. Here, I use highly resolved models of residential power demand and PEV use to assess the impact of uncoordinated in-home PEV charging on residential power demand. While the increase in aggregate demand might be minimal even for high levels of PEV adoption, uncoordinated PEV charging could significantly change the shape of the aggregate residential demand, with impacts for electricity infrastructure, even at low adoption levels. Clustering effects in vehicle adoption at the local level might lead to high PEV concentrations even if overall adoption remains low, significantly increasing peak demand and requiring upgrades to the electricity distribution infrastructure. This effect is exacerbated when adopting higher in-home power charging. Electrification of transport offers many benefits for the energy transition but introduces a number of complexities around the electric system. This study undertakes modelling of residential power demand and electric vehicle use to understand the impact of uncoordinated vehicle charging on the electricity load.

As the world demand for energy continues to grow, a big question is where will all the energy come from and what will the price tag be. With such enormous sums needed, public-private partnerships (PPPs) could play a big role. But the financial crisis has raised worries about funding, and much is still not known about how best to attract PPPs. This report reviews the evidence to date with sectoral reforms and considers different approaches in varying circumstances to help outline the potential role of the private and public sector in: 1) strengthening the corporate governance of private and public utilities; 2) helping governments to establish legal, regulatory, contractual, and fiscal frameworks; and 3) improved market governance to attract private investment. Chapter one reviews the impact of the recent financial crisis on PPP investment compared with what happened in earlier financial crises. It also looks out the latest projections for additional power sector investment needed because of climate change and the possible sources of financing. Chapter two examines how PPP investment in the power sector has fared. It also gives the results of an econometric study that explores which types of incentives and variables matter most to PPPs when they are weighing entering the power sector, especially in renewables, and what influences the ongoing level of investment. The idea is to provide a powerful benchmarking tool at the sector and country levels against which governments and policy makers can evaluate progress on this issue. Chapter three examines four case studies-in China, Brazil, Peru, and Mexico-to identify, disseminate, and promote best practices on alternative ways to attract PPPs.

Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43% relative to the baseline XGBoost model. Performance validation across nine forecast days demonstrates superior accuracy, with MAPE as low as 0.35% and 0.21% on key dates. Statistical Significance tests confirm significant improvements (p < 0.05), with the highest MAPE reduction of 98.17% on critical days. Seasonal and temporal error analyses reveal stable performance, particularly in Quarter 3 and Quarter 4 (0.5%, 0.3%) and nighttime hours (<1%). Robustness tests, including 5-fold cross-validation and Various noise perturbations, confirm the model’s stability and resilience. The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting, with future optimization potential in data preprocessing, algorithm integration, and interpretability.

The supercritical CO 2 (S-CO 2 ) Brayton cycle is expected to replace steam cycle in the application of solar power tower system due to the attractive potential to improve efficiency and reduce costs. Since the concentrated solar power plant with thermal energy storage is usually located in drought area and used to provide a dispatchable power output, the S-CO 2 Brayton cycle has to operate under fluctuating ambient temperature and diverse power demand scenarios. In addition, the cycle design condition will directly affect the off-design performance. In this work, the combined effects of design condition, and distributions of ambient temperature and power demand on the cycle operating performance are analyzed, and the off-design performance maps are proposed for the first time. A cycle design method with feedback mechanism of operating performance under varied ambient temperature and power demand is introduced innovatively. Results show that the low design value of compressor inlet temperature is not conductive to efficient operation under low loads and sufficient output under high ambient temperatures. The average yearly efficiency is most affected by the average power demand, while the load cover factor is significantly influenced by the average ambient temperature. With multi-objective optimization, the optimal solution of designed compressor inlet temperature is close to the minimum value of 35°C in Delingha with low ambient temperature, while reaches 44.15°C in Daggett under the scenario of high ambient temperature, low average power demand, long duration and large value of peak load during the peak temperature period. If the cycle designed with compressor inlet temperature of 35°C instead of 44.15°C in Daggett under light industry power demand, the reduction of load cover factor will reach 0.027, but the average yearly efficiency can barely be improved.

In the process of promoting demand-side management, the core stakeholder groups are government departments, power grid companies, and electricity users. Due to the different positions and conflicting interests of the three parties in the game, intense and complex battles will occur. This paper investigates a tripartite evolutionary game involving government, power grid companies, and electricity users in the context of demand-side management (DSM) and analyzes the dynamic interactions between government departments, power grid companies, and electricity users within the framework of DSM using evolutionary game theory. Using evolutionary game theory, we explore how incentives and strategic interactions among these three stakeholders evolve over time, affecting the stability of DSM policies. The model addresses the asymmetry in the decision-making process and examines the dynamic equilibrium outcomes under various scenarios. The results provide insights into the optimal design of incentive mechanisms to enhance DSM adoption. The findings offer practical recommendations to improve DSM policies, fostering balanced interests between government, grid companies, and users. This research contributes to a deeper understanding of strategic interactions in DSM, revealing how adaptive behaviors can enhance energy efficiency. It also underscores the importance of carefully designed incentive mechanisms in achieving long-term stability and cooperation among key stakeholders.

In recent years, environmental and energy issues relating to global warming have become more serious, and there is a need to shift from conventional power generation, which emits an abundance of carbon dioxide, to renewable energy sources without emissions, such as solar and wind. However, solar power generation, which is one of the renewable energies, changes dynamically, depending on real time weather conditions. Thus, power supplied mainly by solar power generation is often unstable, and an appropriate on-demand energy management for demand-to-supply is required to ensure a stable power supply. Demand-to-supply management methods include inventory management analysis and on-demand inventory management analysis. The cumulative-control method has been used as one of the production management methods to visually manage inventory status in factories and warehouses, while the on-demand cumulative-control method is an extension of inventory management analysis. This study models a demand-to-supply management method for a solar power generation system by using the on-demand cumulative-control method in an actual case. First, a demand-to-supply management method is modeled by an on-demand cumulative-control method, using actual power data from a childcare facility in Tokyo. Next, the on-demand cumulative-control method is adopted to the case without batteries, and the amount of electricity to be purchased is estimated. Finally, the effectiveness of the maximum battery capacity and the amount of the initial charge are examined and discussed by sensitivity analysis.

The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.

India has 150GW of renewable energy potential, about half in the form of small hydropower, biomass, and wind and half in solar, cogeneration, and waste-to-energy. Developing renewable energy can help India increase its energy security, reduce the adverse impacts on the local environment, lower its carbon intensity, contribute to more balanced regional development, and realize its aspirations for leadership in high-technology industries. This study aims to answers critical questions on why renewable energy development is relevant in Indian context, on how much development is economically feasible, and on what needs to be done to realize the potential. The Report is based on data from nearly 180 wind, biomass, and small hydropower projects in 20 states, as well as information from the Ministry of New and Renewable Energy (MNRE) and the Central Electricity Regulatory Commission (CERC).The Report suggests that about 3GW of renewable energy ? all from small hydropower is conomically feasible, when the avoided cost of coal-based generation of Rs 3.08/kWh is considered. About 59GW of renewable energy in wind, biomass, and small hydropower is available at less than Rs 5/kWh. The entire cumulative capacity of 68GW in these three technologies can be harnessed at less than Rs 6/kWh. About 62GW?90 percent of cumulative renewable capacity in wind, biomass, and small hydropower?is economically feasible when the environmental premiums on coal are brought into consideration. Realizing the need to bridge this gap, the government has set an ambitious target of installing at least 40GW of additional capacity of renewables in the next 10 years. India has made tremendous strides in establishing overarching policy framework and institutions to bring renewable in the mainstream of energy mix, but significant financial, infrastructure and regulatory barriers to renewable energy development remain which the report sheds light on and suggests possible solutions.