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This study proposes a multi-objective fuzzy-based procedure for solving reactive power management in competitive environment. The proposed procedure incorporates both economical and technical aspects of reactive power support. The economical aspect aims to minimise the total costs of reactive power purchase from service providers as primary objective function, whereas the technical aspect is taken care of through the secondary objective function that minimises the total transmission losses. The proposed procedure achieves security constraints such as the bus voltage limits, reactive power capability limits and transmission line reactive transfer limits. The proposed procedure is applied to the west-delta region system as a part of the Egyptian Unified network. The numerical results show that the proposed procedure achieves a minimum real power loss with maximal reactive reserve for power systems for different operating conditions.

At the end of 2011, the Indian power sector found itself in financial crisis, just a decade after the 2001 bailout of state electricity boards (SEBs) by the central government. Bankrupt state power distribution utilities in several states were unable to pay their bills or repay their debts. Despite the passage of the landmark 2003 Electricity Act and implementation of a broad set of reforms over the past decade, the sector today is looking at another rescue from the center, four times larger than before. This financial rescue scheme amounts to about Rs 1.9 trillion ($42 billion) and was instigated by the nonperforming assets of the banks and other financial institutions. The Electricity Act was envisaged to create independent companies functioning on commercial principles, but they are still far away from that goal. This report presents a diagnostic of the financial and operational performance of segments in the power sector value chain between adoption of the Electricity Act, 2003, and 2011, including analysis of the factors that contributed to the recent crisis. The report focuses on efficiency and productivity, whether performance has improved over time, and which states have emerged as performance leaders. Analysis of this kind is not new or unique, but this report aims to integrate historical performance, the current situation, future projections of the impact of worsening sector finances, and the actions that need to be taken to check the downturn. The report draws primarily from utility data collected by the Power Finance Corporation in successive years on utilities operational and financial performance. The Power Finance Corporation data were collated into a single database with the addition of various operational parameters at the plant level and the utility level from the Central Electricity Authority.

This report assesses the potential of electricity contract auctions as a procurement option for the World Bank's client countries. It focuses on the role of auctions of electricity contracts designed to expand and retain existing generation capacity. It is not meant to be a 'how-to' manual. Rather, it highlights some major issues and options that need to be taken into account when a country considers moving towards competitive electricity procurement through the introduction of electricity auctions. Auctions have played an important role in the effort to match supply and demand. Ever since the 1990s, the use of long-term contract auctions to procure new generation capacity, notably from private sector suppliers, has garnered increased affection from investors, governments, and multilateral agencies in general, as a means to achieve a competitive and transparent procurement process while providing certainty of supply for the medium to long term. However, the liberalization of electricity markets and the move from single-buyer procurement models increased the nature of the challenge facing system planners in their efforts to ensure an adequate and secure supply of electricity in the future at the best price. While auctions as general propositions are a means to match supply with demand in a cost-effective manner, they can also be and have been used to meet a variety of goals.

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.

This book reviews the major developments in and the lessons learned from the 21-year (1991-2012) experience with private sector participation (PSP) in the power sector in India. It discusses the political economy context of the policy changes, looks at reform initiatives that were implemented for the generation sector, describes transmission and distribution segments at different points in the evolution of the sector, and concludes with a summary of lessons learned and a suggested way forward. The evolution of private participation in the Indian power sector can be divided into different phases. Phase one was launched with the opening of the generation sector to private investment in 1991. Phase two soon followed - early experiments with state-level unbundling and other reform initiatives, including regulatory reform, culminating in divestiture, and privatization in Orissa and Delhi respectively. Phase three, the passage of the electricity act of 2003 by the central government, followed by a large increase in private entry into generation and forays into transmission and experiments with distribution franchise models in urban and rural areas during the 11th five-year plan (2007-12) period. In phase four, at the start of the 12th five-year plan (2012-17), the sector is seeing a sharp reduction in bid euphoria and greater risk aversion on the part of bidders, who are concerned about access to basic inputs such as fuel and land. In this context, the report is structured as follows: chapter one gives introduction; chapter two presents private sector participation in thermal generation; chapter three presents private sector participation in transmission; chapter four deals with private sector participation in distribution; chapter five deals with private sector participation in the Indian solar energy sector; chapter six deals with financing of the power sector; chapter seven presents emerging issues and proposed approaches for the Indian power sector; and chapter eight give updates.

Electrolytic hydrogen produced using renewable electricity can help lower carbon dioxide emissions in sectors where feedstocks, reducing agents, dense fuels or high temperatures are required. This study investigates the implications of various standards being proposed to certify that the grid electricity used is renewable. The standards vary in how strictly they match the renewable generation to the electrolyser demand in time and space. Using an energy system model, we compare electricity procurement strategies to meet a constant hydrogen demand for selected European countries in 2025 and 2030. We compare cases where no additional renewable generators are procured with cases where the electrolyser demand is matched to additional supply from local renewable generators on an annual, monthly or hourly basis. We show that local additionality is required to guarantee low emissions. For the annually and monthly matched case, we demonstrate that baseload operation of the electrolysis leads to using fossil-fuelled generation from the grid for some hours, resulting in higher emissions than the case without hydrogen demand. In the hourly matched case, hydrogen production does not increase system-level emissions, but baseload operation results in high costs for providing constant supply if only wind, solar and short-term battery storage are available. Flexible operation or buffering hydrogen with storage, either in steel tanks or underground caverns, reduces the cost penalty of hourly versus annual matching to 7%–8%. Hydrogen production with monthly matching can reduce system emissions if the electrolysers operate flexibly or the renewable generation share is large. The largest emission reduction is achieved with hourly matching when surplus electricity generation can be sold to the grid. We conclude that flexible operation of the electrolysis should be supported to guarantee low emissions and low hydrogen production costs.

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.

To promote the sustainable management of hydropower, decision makers require information about cost trade-offs between the restoration of fish passage and hydropower production. We provide a systematic overview of the construction, operational, monitoring, and power loss costs associated with upstream and downstream fish passage measures in the European context. When comparing the total costs of upstream measures across different electricity price scenarios, nature-like solutions (67–88 EUR/kW) tend to cost less than technical solutions (201–287 EUR/kW) on average. Furthermore, nature-like fish passes incur fewer power losses and provide habitat in addition to facilitating fish passage, which presents a strong argument for supporting their development. When evaluating different cost categories of fish passage measures across different electricity price scenarios, construction (45–87%) accounts for the largest share compared to operation (0–1.2%) and power losses (11–54%). However, under a high electricity price scenario, power losses exceed construction costs for technical fish passes. Finally, there tends to be limited information on operational, power loss, and monitoring costs associated with passage measures. Thus, we recommend that policy makers standardize monitoring and reporting of hydraulic, structural, and biological parameters as well as costs in a more detailed manner.

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.

It is commonly known that a number of variables, including price, supply levels, time, and green level, affect how quickly certain things are in demand. Furthermore, the inventory carrying cost is considered to be a nonlinear representation of time and is subject to variation throughout time. More precisely, it rises with time since longer storage times necessitate more costly warehouse space. This study presents a fully backlogged situation inventory system for a single commodity where the product’s selling price, green level, and time are used to simultaneously compute the demand rate in accordance with a power pattern. Purchase price is determined by the product’s nonlinear green level. Complete backorders are available for shortages. The impact of the product’s selling price, green level and time power function are combined to determine the product’s demand. Moreover, the holding cost also rises as the product is stored for a longer period of time. The primary goal is to determine the best inventory policy to maximise total profit per unit of time. Though the problem is highly nonlinear in nature. Hence, we cannot solve it analytically. To overcome these difficulties, we have applied several well-known popular metaheuristic algorithms (Water Cycle Algorithm (WCA), Artificial Electric Field Algorithm (AEFA), Teaching Learning Based Optimization Algorithm (TLBOA), Grey Wolf Optimizer Algorithm (GWOA), Sparrow Search Algorithm (SSA), Whale Optimizer Algorithm (WOA), Prairie Dog Optimization Algorithm (PDOA), Gazelle Optimization Algorithm (GOA), A Sinh Cosh Optimizer Algorithm (SCHOA) and White Sherk Optimizer Algorithm (WSOA), Archimedes Optimization Paradigm Algorithm (AOPA), Marine Predator Optimization Algorithm (MPOA), Geyser Inspired Algorithm (GIA), Runge Kutta Optimization Algorithm (RKOA), Lungs Performance-based Optimization Algorithm (LPOA) and Dwarf Mongoose Optimization Algorithm (DMOA)). It is observed that WCA perform better than other algorithms with respect to the convergence rate. A numerical example is taken in order to validate the proposed model. Finally, a post optimality analysis is performed in order to make a fruitful conclusion.