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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.

This study proposes a single‐leader‐multi follower game to model a bilevel retail market among an aggregator and multiple microgrids to determine the optimal demand scheduling of the consumer, as well as price‐power bidding strategies of microgrids in an interactive scheme. In the lower level, microgrids which include several distributed energy resources and energy storage units, compete with each other and offer the optimal energy‐price bids such that their individual profit is maximised, while energy dispatch among their energy resources is also determined. Then, in the upper‐level problem, the aggregator leads the competition taking advantages of demand‐side management including interruptible and shiftable loads to minimise its energy payment for real‐time pricing of generation units. By means of Karush–Kuhn–Tucker optimality condition, the bilevel optimisation of Stackelberg game is reduced to a single‐level mixed‐integer linear programming problem. Moreover, impact of microgrids’ integration on the retail market clearance mechanism, as well as required incentives for such integration has been discussed in a separate scenario.

This article considers various aspects of the functioning of electric power systems (EPSs) with a high proportion of available renewable energy sources (RES). In the absence of sufficient sources of basic generation in the EPS, new ways to eliminate possible consumer load jumps in the form of power reserves will be required. Based on the studies carried out in the Baltic States’ energy systems, it follows that the best way to ensure stable and safe operation of power plants in these conditions is to use energy storage devices, namely, a battery energy storage system (BESS). The BESS battery system will be able to provide reserves in a more economical way than most power plants that use organic fuels. A model for the distribution of production capabilities of an electric power producer with specified energy characteristics in market conditions is proposed. The practical implementation of the model makes it possible to obtain the initial data for creating characteristics of price proposals in the formation of a market for power reserves. The implementation of the model is illustrated for a concrete example.

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.

We propose a machine learning-based methodology which makes use of ensemble methods with the aims (i) of treating missing data in time series with irregular observation times and detecting anomalies in the observed time behavior; (ii) of defining suitable models of the system dynamics. We applied this methodology to US wholesale electricity price time series that are characterized by missing data, high and stochastic volatility, jumps and pronounced spikes. For missing data, we provide a repair approach based on the missForest algorithm, an imputation algorithm which is completely agnostic about the data distribution. To identify anomalies, i.e., turbulent movements of power prices in which jumps and spikes are observed, we took into account the no-gap reconstructed electricity price time series, and then we detected anomalous regions using the isolation forest algorithm, an anomaly detection method that isolates anomalies instead of profiling normal data points as in the most common techniques. After removing anomalies, the additional gaps will be newly filled by the missForest imputation algorithm. In this way, a complete and clean time series describing the stable dynamics of power prices can be obtained. The decoupling between the stable motion and the turbulent motion allows us to define suitable jump-diffusion models of power prices and to provide an estimation procedure that uses the full information contained in both the stable and the turbulent dynamics.

Power plants using the renewable energy resources are the plants with low marginal costs, and that is why they are given the priority in electricity supply. Therefore, they have a negative impact on spot markets, reducing the market price of electricity, known as merit-order effect. However, the subsidization made through feed-in tariff (FIT) scheme puts a burden on the retail electricity costs. This paper tries to explain the net cost impact of FIT portfolio which consists of wind, solar, hydropower, geothermal, and biofuel sources used in electricity supply in Turkey. Turkish electricity market 2014-2020 period hourly data is analyzed using multiple linear regression model. The results show that merit-order effect is lower than the FIT cost, so increases the total retail cost during the studied period. Moreover, it is important to assess the foreign currency-based scheme at the end of its life cycle and see whether lessons learnt are applied for the new local currency scheme. Additionally, the effect of renewable sources on the volatility of electricity prices are examined using financial time series methods with a focus on COVID-19 pandemic. The conclusion is renewables increase uncertainty, but COVID-19 has no impact.

The large-scale integration of renewable energy sources requires flexibility from power markets in the sense that the latter should quickly counterbalance the renewable supply variation driven by weather conditions. Most power markets cannot (yet) provide this flexibility effectively as they suffer from inelastic demand and insufficient flexible storage capacity or flexible conventional suppliers. Research accordingly shows that the volume of renewable energy in the supply system affects the mean and volatility of power prices. We extend this view and show that the level of wind and solar energy supply affects the tails of the electricity price distributions as well and that it does so asymmetrically. The higher the supply from wind and solar energy sources, the fatter the left tail of the price distribution and the thinner the right tail. This implies that one cannot rely on symmetric price distributions for risk management and for valuation of (flexible) power assets. The evidence in this paper suggests that we have to rethink the methods of subsidizing variable renewable supply such that they take into consideration also the flexibility needs of power markets.

Learning the dynamics of power prices in a given market is important for a number of players (e.g., producers, consumers, and policy makers) at both macro- and microeconomic levels. This paper analyzes the recent behavior of spot prices in eight Western European countries. The sample period coincides with the COVID-19 pandemic for the most part: it starts in April 2020 and runs until May 2023; it includes the start of the Russia–Ukraine war. We introduce a new model for the hourly spot price of electricity. The deterministic component includes yearly, weekly, and daily seasonalities; the stochastic component accounts for volatility, mean reversion, and discrete jumps. We estimate the model with publicly available hourly data. Regarding the development of the internal market for electricity, we find that core mainland power markets now move closer in step with one another than before, but the integration process of the Iberian Peninsula seems to have kicked into reverse. As for the dynamics of power prices, in the last part of the sample period the speed of reversion falls everywhere, and price volatility increases noticeably; the expected number of jumps per hour decreases, but their average size turns to positive and they become more volatile.

Power exchanges in Europe have been operating since the 1990s, while in the region of Southeast Europe (SEE) they are only a few years old. Volatility is considered to be one of the indicators that define the level of market development. In this paper, the authors examine 15 power markets in Europe with the novelty of special attention dedicated to the SEE markets, in terms of their volatility. The aim of this paper is to investigate volatility on SEE markets, and their correlation with developed European markets. Power price volatility is measured by price velocity concepts, the daily velocity based on daily/monthly/quarterly/yearly and overall average power prices. The outcomes reveal that power price volatility is higher in new SEE markets than in more mature European markets. The least volatile market is the Greek pool, followed by Switzerland and Austria. The Bulgarian market is the most volatile, followed by that of Romania, and these markets are nearly twice as volatile as the ones previously mentioned. A correlation matrix is carried out and confirms positive correlation between all markets in terms of their average prices. However, a correlation matrix of measured volatilities depicts a negative correlation, in some cases, between SEE and Central European (CE) markets.

The wind power price policy has promoted the rapid development of the wind power industry in China. However, China’s wind power industry is facing high-quality development problems such as wind curtailment and blind investment. Exploring the relationship between the change in wind power price policy and China’s high-quality development of wind power is of great significance for the energy system to achieve carbon neutrality. This paper constructs an SBM-GML global covariance model, calculates the total factor productivity of wind power in 30 provinces in China from 2015 to 2019 and conducts index decomposition, and selects provincial panel data from 2015 to 2019 to empirically test the impact of the wind power price policy on the total factor productivity of wind power in China. The results show that the wind power price policy can significantly improve the total factor productivity of wind power, and the results of the endogenous test and robustness test show the effectiveness of the model. The wind power price policy is helpful to promote the technological progress of wind power, affect the reduction of the price difference between wind power benchmark price and coal power benchmark price, and then promote the improvement of wind power total factor productivity so as to meet the policy requirements of wind power full grid parity. In addition, the impact of wind power price policy on the total factor productivity of wind power has obvious regional heterogeneity. Future price policy formulation should clarify industry development expectations, consider regional differences, and establish a sound, market-oriented electricity pricing mechanism.