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Large and growing subsidies to residential consumers in Mexico have become a major policy concern. This report explains the growth of subsidies, the current distribution of subsidies across income classes, and uses utility and household survey data to simulate how alternative subsidy mechanisms could improve distributional and fiscal performance. The goal is to help inform discussion in Mexico about how to reduce subsidies and redirect them toward the poor. The findings also offer lessons for other countries that are planning tariff reforms in their electricity sectors.

With an increasing deployment of plug-in electric vehicles, evaluating and mitigating the impacts of additional electrical loads created by these vehicles on power distribution grids become more important. This paper explores the use of prices of electricity at public charging stations as an instrument, in couple of road pricing, to better manage both power distribution and urban transportation networks. More specifically, a multi-class combined distribution and assignment model is formulated to capture the spatial distribution of plug-in electric vehicles across the transportation network and estimate the electrical loads they impose on the power distribution network. Power flow equations are subsequently solved to estimate real power losses. Prices of electricity at public charging stations and road tolls are then optimized to minimize both real power losses in the distribution grid and total travel time in the urban transportation network. The pricing model is formulated as a mathematical program with complementarity constraints and solved by a manifold suboptimization algorithm and a pattern search method. Numerical examples are presented to demonstrate the proposed model and solution algorithms.

At present, different concentrating solar thermal technologies (CST) have reached varying degrees of commercial availability. This emerging nature of CST means that there are market and technical impediments to accelerating its acceptance, including cost competitiveness, an understanding of technology capability and limitations, intermittency, and benefits of electricity storage. Many developed and some developing countries are currently working to address these barriers in order to scale up CST-based power generation.Given the considerable growth of CST development in several World Bank Group partner countries, there is a need to assess the recent experience of developed countries in designing and implementing regulatory frameworks and draw lesson that could facilitate the deployment of CST technologies in developing countries. Merely replicating developed countries’ schemes in the context of a developing country may not generate the desired outcomes.Against this background, this report (a) analyzes and draws lessons from the efforts of some developed countries and adapts them to the characteristics of developing economies; (b) assesses the cost reduction potential and economic and financial affordability of various CST technologies in emerging markets; (c) evaluates the potential for cost reduction and associated economic benefits derived from local manufacturing; and (d) suggests ways to tailor bidding models and practices, bid selection criteria, and structures for power purchase agreements (PPAs) for CST projects in developing market conditions.

Rapid urbanization and economic growth, new demographic trends, and climate change are key challenges that developing countries must face as they strive to meet growing energy demand. The main objectives of this study are to offer: (a) a global taxonomy of the economic and financial incentives provided by renewable support schemes and (b) an economic modeling of the sustainability and affordability of such support schemes. In an attempt to contribute to the lively debate, this study provides a global taxonomy of the economic and financial incentives provided by renewable energy (RE) support schemes. It summarizes economic models of the sustainability and affordability of such support schemes, alongside operational advice on how the regulatory design may need to be modified to minimize the impact on the budget and be affordable to the poor, as well as how to identify and fill the financing gap. This analytical framework: (a) differentiates and illustrates tradeoffs among local, regional, and national impacts, in the short and long run; (b) captures distributional impacts (since subsidies to cover the incremental costs of RE may have very different beneficiaries); and (c) captures externalities and compares (where possible) alternative projects based on equivalent output and cost (comparing, for example, RE and energy efficiency projects against those using fossil fuels). The report is organized as follows: chapter one gives introduction. Chapter two presents the analytical framework that underpins the case studies, and provides the background for the principal research hypothesis of this report, which is better attention to the principles of economic analysis and market efficiency leads to more sustainable and effective policies. Chapters three to ten present country case studies for Vietnam, Indonesia, Sri Lanka, South Africa, Tanzania, Egypt, Brazil, and Turkey. The conclusions of the study are presented in chapter eleven.

Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

Deregulation exposes the inherent volatility of the electricity price. Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits. The existing methods have limited capability of integrating other external factors into the forecasting model, such as weather, electricity consumption and natural gas price. This study proposes a deep recurrent neural network (DRNN) method to forecast day-ahead electricity price in a deregulated electricity market to explore the complex dependence structure of the multivariate EPF model. The proposed method can learn the indirect relationship between electricity price and external factors through its efficient diverse function and multi-layer structure. The effectiveness of the method is validated using data from the New England electricity market. Compared with the up-to-date techniques, the proposed DRNN outperforms the single support vector machine (SVM) by 29.71%, and the improved hybrid SVM network by 21.04% in terms of mean absolute percentage error.

Countries heavily dependent on imported oil to power a significant portion of their electricity generation are especially vulnerable to high and volatile oil prices. In net oil-importing countries worldwide, high and volatile oil prices ripple through the power sector to numerous segments of the economy. As prices move up and down, so does the cost of electricity production, which has far-reaching effects on the economy, fiscal and trade balances, businesses, and household living standards. High and volatile oil prices affect economies at both a macro and micro level. The major direct effects at the macro level are a deteriorating trade balance, through a higher import bill, reflecting a worsening in terms of trade; and a weakening fiscal balance due to greater government transfers and subsidies to insulate movements in international energy markets. At the micro level, investment uncertainty results from the higher risk of engaging in new projects and associated development and sunk costs, which, in turn, affects policy decisions and economic growth. This study responds to the needs of policy makers and energy planners in oil-importing countries to better manage exposure to oil price risk. The study's objective is threefold. First, it analyzes the economic effects of higher and volatile prices on oil-importing countries, with emphasis on the power sector, using examples from Latin America and the Caribbean (LAC). Second, it proposes a menu of complementary options that can be applied over multiple time frames. Several structural measures are designed to reduce oil generation and consumption, while a range of financial instruments are suggested for managing price risk in the short term. Finally, it attempts to quantify some of the macroeconomic and microeconomic benefits that could accrue from implementing such options.

This paper aims to present a detailed analysis of the performance of a wind-farm using the wind turbine power measurement standard IEC61400-12-1 (2017). Ten minutes averaged wind data are obtained from LIDAR over the period of twelve months and it is compared with the 38 years’ data from weather station with the objective of determining the wind resources at the wind-farm. The performance of one of the wind turbines located in the wind-farm is assessed by comparing the wind power potential of the wind turbine with its actual power production. Our analysis shows that the wind farm under study is rated as ‘good’ in terms of wind power production and has wind power density of 479 W/m2. The annual wind-farm’s income is estimated based on the real-data collected from the wind turbines. The effect of price of electricity and the spot prices of Norwegian-Swedish green certificate on the income will be illustrated by means of a Monte-Carlo Simulation (MCS) approach. Our study provides a different perspective of wind resource evaluation by analyzing LIDAR measurements using Windographer and combines it with the lesser explored effects of price components on the income using statistical tools.

In recent years, as a result of emerging renewable energy markets, several developed regions have already launched Real‐Time Pricing (RTP) strategies for electricity markets. Establishing optimal pump operation for water companies in RTP electricity markets presents a challenging problem. In a RTP market, both positive and negative electricity prices are possible. These negative prices create economically attractive opportunities for Water Distribution System (WDS) to dispatch their energy consumption. On the other hand, excessively high prices may put WDS at risk of supply disruptions and reduced service levels. However, the continuous development of wind power and photovoltaics results in more volatile and unpredictable fluctuations in the price of renewable energy. The risk arising from uncertainty in electricity prices can lead to a significant increase in actual costs. To address this issue, this paper develops an a posteriori random forest (AP‐RF) approach to forecast the probability density function of electricity prices for the next day and provide a risk‐constrained pump scheduling method toward RTP electricity market. The experimental results demonstrate that the developed method effectively addresses the issue of increased costs caused by inaccurate electricity price forecasting. Plain Language Summary With the emergence of renewable energy markets in recent years, several developed regions have introduced Real‐Time Pricing (RTP) strategies for their electricity markets. This has created a difficult challenge for water companies seeking to establish the optimal pump operation in RTP markets. This study investigates the use of a risk‐constrained optimization scheduling approach for water distribution networks to mitigate the risks associated with inaccurate real‐time electricity price forecasting. Our proposed method is designed to reduce the costs associated with inaccurate electricity price prediction. Key Points A robust pump scheduling approach toward real‐time electricity price market is developed Developing a posteriori random forest algorithm to predict the probability density function of Real‐time electricity price Optimal scheduling with risk constraints is an effective approach to mitigating the risks associated with inaccurate electricity forecasting

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model’s robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel “policy-versus-policy” strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R2, MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF.