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This analysis explores the possibility of merging into a ‘combined’ proposal two standard I-O methods identifying key sectors, the Classical Multiplier and Hypothetical Extraction. In the context of the latest revision of the European Union Energy Efficiency Plan, we use this proposal to single out key sectors that boost potential energy savings in the economic system—specifically, in the production and distribution of electricity. Using the main distinctions and complementarities of the two traditional I-O key-sector approaches, the combined formulation allows us to disaggregate the backward stimuli of the electricity sector into three indicators: total, internal and external backward indicators. This combined proposal provides additional insights into the structure of the industrial linkages that participate in the production and distribution of electricity. Our results reveal that the explanation for the intensity of the backward effects of the electricity sector depends not only on other energy sectors but also on some manufacturing industries. We put forward that these findings may be important for developing a more balanced and cost-effective design for energy efficiency policies.

One of the harsher realities of independence for the former Soviet republics has been the loss of subsidized transfers from the center for fuel and utilities. In the years since independence, Georgians, with other “energy poor” republics, have been subject to higher costs and declining service levels for household utilities – particularly energy. The combination of low household incomes, high international prices for fuel, the need for utilities to rely on internally generated funds for capital investment, low household incomes, and the political ramifications of removing subsidies at a time of general economic decline have led to a “worst of all worlds” situation. Revisiting Reforms reviews the changes in the supply of electricity and gas in Georgia from the perspective of households, utility operators, and the government. It highlights lessons from the reforms implemented and applies them to the future reform program planned for the rest of the energy sector. The title concludes that improved service quality and the increased supply of clean and subsidized natural gas have offset the potentially negative impact of higher electricity prices.

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed.

Electricity infrastructures are critical lifeline systems that are designed to serve with a high degree of reliability the power supply of consumers under normal operating conditions and in case of common failures or expected disturbances. However, many recent weather-induced disasters have brought unprecedented challenges to the electricity networks, underlining that power systems remain unprepared to absorb disruptive large-scale and severe events. Worse still, it is expected that such climate hazards will take place at rising frequency and intensity rates due to climate change. The intensification of meteorological extremes will lead to higher losses and changes in transmission capacity, increasing the frequency and importance of material damage to the aging electric infrastructure, thus resulting in significant disruptions, cascading failures, and unpredictable power outages. This review paper presents real-life examples of different types of extreme weather incidents and their impacts on the distribution network in Greece, a country that is highly vulnerable because of its location, geomorphology, and the existing overhead network assets, highlighting lessons learned related to adaptation options and disaster management best practices. Literature review and benchmarking with other grid operators are also employed to explore resilience-enhancing technical capabilities, weatherproof solutions, and operational strategies on which policy-making initiatives should focus.

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.

In many countries, most one-phase residential electricity consumers are supplied from three-phase, four-wire local networks operated in radial tree-like configurations. Uneven consumer placement on the wires of the three-phase circuit leads to unbalanced phase loads that break the voltage symmetry and increase the energy losses. One way to mitigate these problems is to balance the phase loads on the feeders by choosing the optimal phase of connection of the consumers. The authors proposed earlier a phase balancing algorithm based on metaheuristic optimization. For networks with a high number of supply nodes, this algorithm requires finding a solution for all the consumers simultaneously. Two alternative approaches are proposed in this paper that use the tree-like structure of the network to divide the optimization between a main distribution feeder and several branches, creating a multistage process, with the aim of minimizing energy losses. A case study is performed using a real low-voltage distribution network and a comparison is made between the three algorithms. The resulting losses have marginal variations between the proposed approaches, with a maximum of 1.3% difference.

Purpose This study aims to investigate the impact of the 2013 privatization of Nigeria’s energy sector on the technical performance of the Benin Electricity Distribution Company (BEDC) and its workforce. Design/methodology/approach This study used a questionnaire-based approach, and 196 participants were randomly selected. Analytical tools included standard deviation, Spearman rank correlation and regression analysis. Findings Before privatization, the energy sector, managed by the power holding company of Nigeria, suffered from inefficiencies in fault detection, response and billing. However, privatization improved resource utilization, replaced outdated transformers and increased operational efficiency. However, in spite of these improvements, BEDC faces challenges, including unstable voltage generation and inadequate staff welfare. This study also highlighted a lack of experience among the trained workforce in emerging electricity technologies such as the smart grid. Research limitations/implications This study’s focus on BEDC may limit its generalizability to other energy companies. It does not delve into energy sector privatization’s broader economic and policy implications. Practical implications The positive outcomes of privatization, such as improved resource utilization and infrastructure investment, emphasize the potential benefits of private ownership and management. However, voltage generation stability and staff welfare challenges call for targeted interventions. Recommendations include investing in voltage generation enhancement, smart grid infrastructure and implementing measures to enhance employee well-being through benefit plans. Social implications Energy sector enhancements hold positive social implications, uplifting living standards and bolstering electricity access for households and businesses. Originality/value This study contributes unique insights into privatization’s effects on BEDC, offering perspectives on preprivatization challenges and advancements. Practical recommendations aid BEDC and policymakers in boosting electricity distribution firms’ performance within the privatization context.

Energy losses and bus voltage levels are key parameters in the operation of electricity distribution networks (EDN), in traditional operating conditions or in modern microgrids with renewable and distributed generation sources. Smart grids are set to bring hardware and software tools to improve the operation of electrical networks, using state-of the art demand management at home or system level and advanced network reconfiguration tools. However, for economic reasons, many network operators will still have to resort to low-cost management solutions, such as bus reactive power compensation using optimally placed capacitor banks. This paper approaches the problem of power and energy loss minimization by optimal allocation of capacitor banks (CB) in medium voltage (MV) EDN buses. A comparison is made between five metaheuristic algorithms used for this purpose: the well-established Genetic Algorithm (GA); Particle Swarm Optimization (PSO); and three newer metaheuristics, the Bat Optimization Algorithm (BOA), the Whale Optimization Algorithm (WOA) and the Sperm-Whale Algorithm (SWA). The algorithms are tested on the IEEE 33-bus system and on a real 215-bus EDN from Romania. The newest SWA algorithm gives the best results, for both test systems.

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.

Electricity distribution systems face increasing challenges due to demand growth, regulatory requirements, and the integration of distributed generation. In this context, distribution companies must make strategic and well-supported investment decisions, particularly in asset reinforcement actions such as reconductoring. This paper presents a multi-stage methodology to optimize reconductoring investments under load forecasting uncertainties. The approach combines a decomposition strategy with Monte Carlo simulation to capture demand variability. By discretizing a lognormal probability density function and selecting the largest loads in the network, the methodology balances computational feasibility with modeling accuracy. The optimization model employs exhaustive search techniques independently for each network branch, ensuring precise and consistent investment decisions. Tests conducted on the IEEE 123-bus feeder consider both operational and regulatory constraints from the Brazilian context. Results show that uncertainty-aware planning leads to a narrow investment range—between USD 55,108 and USD 66,504—highlighting the necessity of reconductoring regardless of demand scenarios. A comparative analysis of representative cases reveals consistent interventions, changes in conductor selection, and schedule adjustments based on load conditions. The proposed methodology enables flexible, cost-effective, and regulation-compliant investment planning, offering valuable insights for utilities seeking to enhance network reliability and performance while managing demand uncertainties.