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Purpose: The aim of this work is to compare greenhouse gas (GHG) emissions from producing tissue paper from virgin pulp (VP) or recycled waste paper (RWP). In doing so, the study aims to inform decision makers at both company and national levels which are the main causes of emissions and to suggest the actions required to reduce pollution. Methods: An attributional life cycle assessment (LCA) was performed in order to estimate and compare the GHG emissions of the two processes. LCA allows us to assess how the choice of raw material for VP and RWP processes influences total GHG emissions of tissue paper production, what are the main drivers behind these emissions and how do the direct materials; energy requirements and transportation contribute to the generation of emissions. The cradle-to-gate approach is carried out. Results and discussion: The results show that demands for both thermal energy and electricity are higher for the RWP than for the VP if only the manufacturing stages are considered. However, a different picture emerges when the analysis looks at the entire life cycle of the production. GHG from the VP are about 30 % higher than the RWP, over the life cycle emitting 568 kg CO eq more per kilogram of tissue paper. GHG emissions from the wood pulping alone were 559 g CO eq per kilogram of tissue paper, three times higher than waste paper collection and transportation. Conclusions: In terms of GHG emissions from cradle to gate, the recycled process less intensive than the virgin one for two reasons. First, as shown in the results the total GHG emissions from RWP are lower than those from VP due to relatively lower energy and material requirements. Second is the non-recyclability nature of tissue paper. Because the tissue paper is the last use of fibre, using RWP as an input would be preferable over using VP. The environmental profile of the tissue products both from RWP and VP can be improved if the following conditions are considered by the company. First, the company should consider implementing a cogeneration unit to simultaneously generate both useful heat and electricity. Second, it may consider changing the VP mix, in order to avoid the emissions associated with long distance transpiration effort. Third, there is the option of using sludge as fuel, which would reduce the total fossil fuel requirement. [ABSTRACT FROM AUTHOR]

A number of life-cycle assessment studies have compared the environmental impacts of material recycling and incineration of waste paper. They have shown that, in most cases, a recycling scenario results in lower total energy use, but greater use of fossil fuels. If waste paper and fossil fuels are complements, parts of the environmental argument for recycling is abolished. This paper estimates a cost function for the Swedish paper industry. If the cost shares are co-integrated, an error correction model will be used to model the dynamics. Short- and long-run elasticities are then calculated to address the relationship between waste paper and the other inputs: capital, labor, purchased pulp, fiber, fossil fuels and electricity. Contrary to the life-cycle assessment studies, the results show that waste paper and fossil fuels are substitutes, and that waste paper and electricity are complements. [ABSTRACT FROM AUTHOR]

Globally, there is a growing momentum toward achieving universal access to clean energy, particularly electricity, as it plays a crucial role in improving the overall quality of life. Addressing the challenge of universal electricity access requires the development of various mechanisms that enable individuals to access electricity at affordable rates while having the freedom to choose their service providers. In many countries, governments are actively involved in enhancing electricity access, but this effort can put strain on state finances, leading to increased fiscal deficits. This paper aims to investigate the relationship between power expenditure and state finances in Jammu and Kashmir, India, in light of the push for improved electricity access. Using data from the RBI Bulletins for the period 1990–91 to 2019–20, the paper employs panel data analysis to investigate the relationship between power expenditure and gross fiscal deficit. Results indicate a positive and significant correlation between expenditure on power and state gross fiscal deficit in Jammu and Kashmir, indicating lower fiscal sustainability with higher power expenditure. For the purpose of reducing the pressure on state finances caused by enormous domestic power subsidies, the study recommended targeted subsidies with a focus on small energy projects utilizing renewable energy sources and energy-saving technologies, as well as incentives and regulations to promote energy conservation. [ABSTRACT FROM AUTHOR]

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. [ABSTRACT FROM AUTHOR]

This paper focuses on day-ahead electricity load forecasting for substations of the distribution network in France; therefore, the corresponding problem lies between the instability of a single consumption and the stability of a countrywide total demand. Moreover, this problem requires to forecast the loads of over one thousand substations; consequently, it belongs to the field of multiple time series forecasting. To that end, the paper applies an adaptive methodology that provided excellent results at a national scale; the idea is to combine generalized additive models with state-space representations. However, extending this methodology to the prediction of over a thousand time series raises a computational issue. It is solved by developing a frugal variant that reduces the number of estimated parameters: forecasting models are estimated only for a few time series and transfer learning is achieved by relying on aggregation of experts. This approach yields a reduction of computational needs and their associated emissions. Several variants are built, corresponding to different levels of parameter transfer, to find the best trade-off between accuracy and frugality. The selected method achieves competitive results compared to individual models. Finally, the paper highlights the interpretability of the models, which is important for operational applications. [ABSTRACT FROM AUTHOR]

Generating sustainable electricity from ambient humidity and natural evaporation has attracted tremendous interest recently as it requires no extra mechanical energy input and is deployable across all weather and geography conditions. Here, we present a device prototype for enhanced power generation from ambient humidity. This prototype uses both heterogenous materials assembled from a LiCl-loaded cellulon paper to facilitate moisture adsorption and a carbon-black-loaded cellulon paper to promote water evaporation. Exposing such a centimeter-sized device to ambient humidity can produce voltages of around 0.78 V and a current of around 7.5 μA, both of which can be sustained for more than 10 days. The enhanced electric output and durability are due to the continuous water flow that is directed by evaporation through numerous, negatively charged channels within the cellulon papers. The voltage and current exhibit an excellent scaling behavior upon device integration to sufficiently power commercial devices including even cell phones. The results open a promising prospect of sustainable electricity generation based on a synergy between spontaneous moisture adsorption and water evaporation. Generating electricity from air opens a promising way for green energy harvesting. Here, authors present a prototype driven by the integration of moisture adsorption with evaporation to generate continuous electricity for a long duration. [ABSTRACT FROM AUTHOR]

Clean, affordable, and efficient energy sources are inevitable for a sustainable world. Energy crisis, especially the poor access and affordability, demand–supply mismatches, energy inequality, and high dependence on non-renewable energy sources, are the challenges before the attainment of clean energy goals for sustainable development. The 5-year review from the adoption of sustainable development goals (SDGs) by using bibliometric and thematic analysis was conducted in this review. This review is a synthesis of 175 scientific papers on SDG 7. Policy reforms and better funding; technology innovation and inclusion; and economic growth, rapid promotion of renewable, and alternative fuels are the recommendations for the achievement of the energy goals. Future research on energy-related goals should focus on energy justice, policy reforms, energy poverty, poor affordability, off-grid transmissions, renewable energy sources, alternative fuels, reforms in the energy supply chain, and international cooperation for better implementation of projects and for attracting foreign capital and private funds. This paper invites the attention of practitioners, academicians, funding agencies, and international agencies to collaborate in the initiatives for a clean, green, and energized world. [ABSTRACT FROM AUTHOR]

In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing. [ABSTRACT FROM AUTHOR]

This paper extends our previous algorithm, termed as Particle Swarm Optimization with Distributed Acceleration Constants (PSODAC), which has been introduced for mitigating congestion based on rescheduling in a deregulated environment. The proposed variant adopts a sequential hybridization of the PSODAC principle with Differential Evolution, which is a well-known evolutionary algorithm. The variant in this paper is termed as Sequentially Hybridized Differential Evolution with Particle Swarm Optimization (SH-DEPSO). The experimental investigations are carried out in the IEEE 14 bus system in two scenarios namely, single point congestion and multipoint congestion. Firstly, the performance investigation is carried out on mitigating the congestion using cost analysis, stability analysis, complexity analysis, and strategy analysis. Secondly, the characteristics of the algorithm are observed by performing convergence analysis and investigating the quality of the solution dynamics. The studies demonstrate the competing performance of SH-DEPSO over PSODAC and the traditional PSO. [ABSTRACT FROM AUTHOR]

Purpose: Life cycle assessment (LCA) is a widely used method for the evaluation of buildings' environmental impacts, but these analyses contain high levels of uncertainty. Decarbonization of electricity production is key to reach climate goals, influencing all sectors including construction The objective of this paper is to study the sensitivity of the environmentally optimum building design solution to a changing electricity mix to assist decision-making. Methods: In this paper, multi-objective optimization was applied to minimize the life cycle global warming potential and life cycle costs of a building at the same time, using dynamic energy simulation and LCA. The variables include building envelope parameters such as window ratio, insulation type and thickness in a typical new multi-family apartment building heated with a heat pump. A static, largely fossil-based electricity mix and a dynamic, gradually decarbonizing alternative are considered, as well as two electricity price increase scenarios. New metrics have been introduced to explore the results and describe the Pareto-optimal solutions, for example the improvement potential to contextualize the achievements through the optimization. Results and discussion: The results show that, with the current electricity mix, building envelope optimization can improve the design by 18% on average in terms of the life cycle greenhouse gas emissions compared to typical new designs and by 10% with a dynamic mix in Hungary. With today's static electricity mix, the optimization proved that the minimum energy efficiency requirements in force are close to cost optimality. However, from an environmental point of view, much higher insulation thicknesses have been shown as justified (U-values of less than 0.1 W/m2K). Conclusions: Optimization of building design based on LCA is still not widely applied and the effect of a decarbonizing electricity mix on the optimum has not been studied before. The results of the paper demonstrate the importance of this question. Optimal solutions are different depending on the applied mix but an energy-efficient building envelope remains important: today's cost-optimal building envelope proves to be both cost- and environmentally optimal in the long run. [ABSTRACT FROM AUTHOR]

Disruptions to key lifelines, especially electrical power, can cause outsized impacts on human functioning. The state of the art on developed countries has focused on enhancing resilience to electrical grid infrastructure but has neglected to track changes regarding how the private market has developed electricity continuity measures over time. Backup generators are among the most accessible tools to maintain electricity continuity in case of power failure, but their role as a buffer remains understudied outside the technical domain, along with the humanitarian and emergency response sectors. This paper analyzes generator sales across the U.S. to understand some underlying trends that may have influenced changes in consumer preference for electricity resilience. Reports from major backup generator sellers and import data of backup generators reveal an increase in backup generators across the U.S. and find that private demand for energy resilience is likely increasing due to consumers' perceived risk and rising levels of intolerance to power disruptions. The discussion finds that an increase in private demand and use of backup generators may be impacting electricity resilience at a communal and societal level, which seems to be underexamined by studies focusing on private generator usage in the U.S.. [ABSTRACT FROM AUTHOR]

Renewable energy production is one of the most important policy instruments to fight climate change. However, despite global benefits, renewable energy production entails some local challenges, such as requiring more space per unit production capacity. In this paper, we study the external effects of large-scale conventional and renewable electric power generation facilities on local house prices. We combine information of all coal, gas, and biomass plants, as well as all wind turbines in the Netherlands, with 1.5 million housing transactions over a period of 30 years. Using a difference-in-difference as well as a repeated sales model, we study the effects of facility openings and closings. Our results show negative external price effects for gas plants and wind turbines, but positive effects for biomass plants, conditionally upon ex-ante lower priced locations. The external effects of power generating facilities on local housing markets are important to consider, especially with the current focus of public policies on the expansion of renewable energy generation. Our paper is one of the first to present a large-scale study, using detailed information, and comparing several different energy sources in one framework. [ABSTRACT FROM AUTHOR]

This article examines the impact of market restructuring on retail prices, using the restructuring of the electricity industry as a case study. Utilizing synthetic control as an estimation strategy, this paper finds retail competition reduced retail prices across all sectors by an average of $1.5/MWh, relative to their counterfactual outcome. On average, prices fell for residential and commercial users and rose for industrial users. The price differential is consistent with the enactment of price ceilings and increased pass-through of changes in the price of natural gas. [ABSTRACT FROM AUTHOR]

During peak electricity demand periods, prices in wholesale markets can be up to nine times higher than during off-peak periods. This is because if a vast number of users is consuming electricity at the same time, power plants with higher greenhouse gas emissions and higher system costs are typically activated. In the UK, the residential sector is responsible for about one third of overall electricity demand and up to 60% of peak demand. This paper presents an analysis of the 2014–2015 Office for National Statistics National Time Use Survey with a view to derive an intrinsic flexibility index based on timing of residential electricity demand. It analyses how the intrinsic flexibility varies compared with wholesale electricity market prices. Findings show that spot prices and intrinsic flexibility to shift activities vary harmoniously throughout the day. Reflections are also drawn on the application of this research to work on demand side flexibility. [ABSTRACT FROM AUTHOR]

Nested Benders's decomposition is an efficient means to solve large-scale optimization problems with a natural time sequence of decisions. This paper examines the use of the technique to decompose and solve efficiently capacity-expansion problems for electricity systems with hydroelectric and renewable generators. To this end we develop an archetypal planning model that captures key features of hydroelectric and renewable generators and apply it to a case study that is based on the Columbia River system in the northwestern United States of America. We apply standard network and within-year temporal simplifications to reduce the problem's size. Nevertheless, the remaining problem is large-scale and we demonstrate the use of nested Benders's decomposition to solve it. We explore refinements of the decomposition method which yield further performance improvements. Overall, we show that nested Benders's decomposition yields good computational performance with minimal loss of model fidelity. [ABSTRACT FROM AUTHOR]

Short-term Load Forecasting (STLF) plays a crucial role in balancing the supply and demand of load dispatching operations and ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast the hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%. [ABSTRACT FROM AUTHOR]

Stakeholders and communities' involvement is vital for shaping novel intergenerational resource governance frameworks. This is crucial for modelling upcoming energy transitions towards cleaner and more sustainable production systems. New models envisage energy mixes in which renewable resources are prominent and offer sustainable development advantages. Examining electricity production figures is, hence, necessary to understand trends and formulate policies and actions. This paper aims at drafting the state-of-the-art of electricity production in the Nordic-Baltic Sea area, moving an exploratory regional appraisal. Aggregated and disaggregated data on electricity production sources are explored and compared for all the eleven selected countries—at domestic, sub-regional, and regional levels. The region shows great national diversity, which implies diversified energy policies. Thus, the role of local initiatives—such as power grid implementation—in local communities empowerment is revealed. From this outcome, possible strategies are prescribed locally to assist the regional sustainable energy appraisal and furnish indications for the European energy and ecological transition. [ABSTRACT FROM AUTHOR]

The global production of electricity contributes significantly to the release of carbon dioxide emissions. Therefore, a transformation of the electricity system is of vital importance in order to restrict global warming. This paper proposes a modelling methodology for electricity systems with a large share of variable renewable electricity generation, such as wind and solar power. The model developed addresses the capacity expansion problem, i.e. identifying optimal long-term investments in the electricity system. Optimal investments are defined by minimum investment and production costs under electricity production constraints—having different spatial resolutions and technical detail—while meeting the electricity demand. Our model is able to capture a range of strategies to manage variations and to facilitate the integration of variable renewable electricity; it is very large due to the high temporal resolution required to capture the variations in wind and solar power production and the chronological time representation needed to model energy storage. Moreover, the model can be further extended—making it even larger—to capture a large geographical scope, accounting for the trade of electricity between regions with different conditions for wind and solar power. Models of this nature thus typically need to be solved using some decomposition method to reduce solution times. In this paper, we develop a decomposition method using so-called variable splitting and Lagrangian relaxation; the dual problem is solved by a deflected subgradient algorithm. Our decomposition regards the temporal resolution by defining 2-week periods throughout the year and relaxing the overlapping constraints. The method is tested and evaluated on some real-world cases containing regions with different energy mixes and conditions for wind power. Numerical results show shorter computation times as compared with the non-decomposed model and capacity investment options similar to the optimal solution provided by the latter model. [ABSTRACT FROM AUTHOR]

This paper is concerned with the risk management practices of an electricity retailer motivated by the Dutch electricity market. We examine the effectiveness of the existing base- and peak-load futures contracts as a risk management tool for the electricity retailers. We analytically characterize the retailer's optimal hedging policy as a function of the serial correlation of the prices and the demand profiles of its customers. We find that the retailer typically over-hedges in the futures market, and the over-hedging amount increases when both base- and peak-load contracts are used. Our findings indicate that although the existing contracts in the futures market are quite efficient to replicate the exposure from profiled customers, when industrial consumers and renewable generation are included to the retailer's portfolio, the effectiveness of such contracts decreases substantially. In our motivating example, hedging the risk of the profiled customers with base-load contracts, the firm may reduce the variance of its cash flows by 85.9%. In addition to the base-load contracts, including peak-load contracts into the hedging portfolio of the retailer increases the efficiency of hedging to 89.3%. However, when we consider the aggregate portfolio of the retailer including profiled customers, industrial consumers and renewable contracts, the efficiency of hedging through the existing futures contracts goes down as low as 32.8% during certain periods. [ABSTRACT FROM AUTHOR]

Electricity load forecasting is an essential, however complicated work. Due to the influence of a large number of uncertain factors, it shows complicated nonlinear combination features. Therefore, it is difficult to improve the prediction accuracy and the tremendous breadth of applicability especially for using a single method. In order to improve the performance including accuracy and applicability of electricity load forecasting, in this paper, a concept named minimum cycle decomposition (MCD) that the raw data are grouped according to the minimum cycle was proposed for the first time. In addition, a hybrid prediction model (HMM) based on one-order difference, ensemble empirical model decomposition (EEMD), mind evolutionary computation (MEC) and wavelet neural network (WNN) was also proposed in this study. The HMM model consists of two parts. Part one, pre-processing, known as one order difference to remove the trend of subsequence and EEMD to reduce the noise, was performed by HMM model on each subset. Part two, the WNN optimized by MEC (WNN $$+$$ MEC) was applied on resultant subseries. Finally, a number of different models were used as the comparative experiment to validate the effectiveness of the presented method, such as back propagation neural network (BP-1), BPNN combined MCD (BP-2), WNN combined MCD (WNNM), a HMM (DEEPLSSVM) based on one-order difference, EEMD, particle swarm optimization and least squares support vector machine and a hybrid model (DEESGRNN) based on one-order difference, EEMD, simulate anneal and generalized regression neural network. Certain evaluation measurements are taken into account to assess the performance. Experiments were carried out on QLD (Queensland) and NSW (New South Wales) electricity markets historical data, and the experimental results show that the MCD has the advantages of improving model accuracy and of generalization ability. In addition, the simulation results also suggested that the proposed hybrid model has better performance. [ABSTRACT FROM AUTHOR]

Natural disasters threat the resilience of the electricity system. However, little literature has investigated the electricity system's recovering process and progress after natural disasters' hit which strongly influence the system operators' planning and quality of the security of supply for the electricity customers. To fill the research gap, this paper applies an unsupervised machine learning method, the k means clustering algorithm, to investigate the normal/abnormal electricity load profiles, identify natural disaster- and electrical fault-impacted electricity load profiles with a case study of the Lombok electricity system, Indonesia, and ½-hourly electricity load data from 2015 until 2021. The results show that electricity consumption in Lombok has increased over the years, which match the installed production capacity of Lombok. The results prove that the disturbance-induced electricity load patterns and especially natural disaster-impacted load profiles can be identified by the k means clustering algorithm. Especially, the pre-, during, and post-natural disaster impacted load patterns can be portrayed. Furthermore, the investigation results regarding the impacts of natural disasters and electrical faults on the performance of the electricity system, show that the natural disaster-induced load reductions and electrical fault-induced load reductions differ from the short and long-term perspectives. Moreover, the results can facilitate the electricity system operators to better understand the load patterns, predict ND strikes' impact on the electricity system and conduct better long-term energy management strategies. [ABSTRACT FROM AUTHOR]

Performance based regulation ("PBR") directly regulates public utilities' prices or revenues with the goal to provide greater incentives for achieving efficiencies and other cost savings than cost-of-service (profit) regulation provides. PBR plans typically include a formula capping the allowed prices or revenues with the cap calculated to reflect what we would expect to observe in competitive markets in the long run: prices are set to equal input prices minus productivity "I–X", where I represents inflation and X represents industry-wide productivity. The PBR formula may also include a consumer stretch factor ("stretch factor")—sometimes referred to as a consumer productivity dividend. Some regulators view the stretch factor as a one-time component meant to share between the company and customers the immediate expected increase in productivity growth as the regulated firm transitions from cost of service to PBR regulation. Other regulators view it more as a permanent component of PBR meant to incentivize the regulated firm beyond the initial switch to PBR by benchmarking its costs to a comparable group of companies and rewarding (penalizing) it for superior (inferior) cost performance. This paper focuses on economic aspects of utilizing the stretch factor as a permanent feature of PBR, and importantly, on the theoretical underpinnings of utilizing cost benchmarking to determine the stretch factor in a PBR plan. We provide a review of the academic literature on econometric cost benchmarking and assess that literature with respect to the stretch factor. We provide an econometric cost benchmarking analysis, using data on U.S. electricity transmission. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality by 2050, Japan should speed up reducing fossil fuel reliance on production, especially for energy-intensive sectors. One way is by implementing a carbon pricing system, converting emissions from fossil fuels to costs of production and consumption. This study focuses on the correlation between the price of wholesale electricity spot market and carbon cost of nine regions in Japan through carbon cost pass-through rate. This paper applies polynomial OLS regression with degree two through machine learning technics to better fit the relationship between electricity price and demand and also applies a generalized additive model to capture the nonlinear relationship between fuel spread and carbon cost and test the robustness of estimated CPTR. The results show that Hokuriku, Kyushu, Shikoku, Tohoku, and Tokyo have a lower value carbon cost pass-through rate while Kansai, Chubu, and Chugoku have a higher rate of carbon cost pass-through. There is a special case in Hokkaido as the negative relationship between electricity price and carbon cost. Those findings are also crucial in supporting future policy adjustments. [ABSTRACT FROM AUTHOR]

Residential and service (office) buildings consume a large proportion of primary energy in Europe in the form of electricity and all other energy carriers. In response to this, the concept of near Zero Energy Buildings (nZEB) has been developed. These buildings have very low energy demands and integrate renewable energy to supply residual demand. nZEBs aim to increase energy efficiency from a demand-side user perspective. When looking at the entire energy system, there are also energy efficiency gains to be achieved on the supply-side. For example, from a district heating system. If an energy system becomes more efficient on the supply-side, then the question is how much energy needs to be saved on the demand-side, for instance by low energy buildings such as nZEBs. The purpose of this paper is to analyse and understand the implications from building new low energy buildings, i.e. nZEBs, within an energy system that is (a) transitioning to 100% renewable energy and (b) has substantially improved supply-side energy efficiency. A case study from Denmark is used to understand the outcome for the energy system when these new buildings are built in this context. The methodology and results of this study could be replicated for other European countries as well. The analysis looks at the total energy system heat savings, costs and biomass consumption. The paper shows that these new low energy buildings with very low heat demand do not deliver the expected benefits for the 100% renewable energy system transition in Denmark. This is due to the increased efficiency and flexibility of the energy supply system in the future. However, deep renovations of existing buildings are necessary. Furthermore, this paper demonstrates based on the Danish case, that as European countries decarbonise their energy systems over the next decades, they will need to carry out detailed energy system analysis to determine the extent to which heat demand should be reduced in buildings within the context of the transitioning energy system. [ABSTRACT FROM AUTHOR]

Blackouts aggravate the situation during an extreme river-flood event by affecting residents and visitors of an urban area. But also rescue services, fire brigades and basic urban infrastructure such as hospitals have to operate under suboptimal conditions. This paper aims to demonstrate how affected people, critical infrastructure, such as electricity, roads and civil protection infrastructure are intertwined during a flood event, and how this can be analysed in a spatially explicit way. The city of Cologne (Germany) is used as a case study since it is river-flood prone and thousands of people had been affected in the floods in 1993 and 1995. Components of vulnerability and resilience assessments are selected with a focus of analysing exposure to floods, and five steps of analysis are demonstrated using a geographic information system. Data derived by airborne and spaceborne earth observation to capture flood extent and demographic data are combined with place-based information about location and distance of objects. The results illustrate that even fire brigade stations, hospitals and refugee shelters are within the flood scenario area. Methodologically, the paper shows how criticality of infrastructure can be analysed and how static vulnerability assessments can be improved by adding routing calculations. Fire brigades can use this information to improve planning on how to access hospitals and shelters under flooded road conditions. [ABSTRACT FROM AUTHOR]

Electricity generation is the largest sector with decarbonization potential for China and the world. Based on the new emission factors, this paper aims to identify the structural and technological determinants of provincial carbon intensity in the electricity generation sector (CIE) using the multiplicative LMDI-II method. Results demonstrate that (1) China's overall CIE decreases by 7.3% in 2001–2015, and the research period can be divided into four stages according to CIE changes (i.e., rapid growth, rapid decline, slow growth, and transition). The CIE in the 12th FYP estimated in this paper, 24.9% lower than that using the emission factors from IPCC, is closer to China's actual situation. (2) There exists huge heterogeneity in the determinants of provincial CIE changes in four stages. CIE growth in the Northwest and Northeast is caused by the coal-dominated energy structure. CIE growth in the Southwest is attributed to the electricity structure effect, while that of the Coast region is caused by the geographic distribution effect. The electricity efficiency effect is attributed to the CIE growth for these regions and the Southwest should also place focus on the electricity trade effect. The impact of electricity trade-related factors depends on the region being a net exporter or importer of electricity. (3) To achieve carbon intensity reduction targets, 30 provinces are categorized into four types based on various combinations of structural and technological determinants. The findings provide insights into capturing future emission-mitigating focus as well as defining the emission-mitigating responsibilities between electricity exporters and importers in China. [ABSTRACT FROM AUTHOR]

Efficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a comprehensive dataset containing generator aggregations, generator costs, transmission limits, load distributions, and electricity prices for the WECC and NPCC systems based on real-world grid operation data at year 2020, including power plant geographic locations, generation profiles, regional power flow interchanges, and load distributions in both regions. The electricity price from the developed dataset is simulated based on the other items in the dataset, and we show that the variation of the simulated electricity price reasonably aligns with the real-world electricity price in both the WECC and NPCC regions. Overall, the developed dataset is of interest for various electricity market and economic studies, such as the economic dispatch and locational marginal price (LMP) analysis. [ABSTRACT FROM AUTHOR]

Background: The transition to carbon neutral mobility will require a lot of carbon neutral energy, but a lot of space as well. In many countries, it will be a challenge to find this space or to import enough energy. Land use indicators related to sustainable mobility usually focus on space for transport infrastructure and parking, i.e. direct land use, and do not include energy supply. Existing literature on the emerging research field of 'energy landscapes' has not covered the transport sector. Objective: The aim of this paper is to estimate the order of magnitude of land or sea area required for carbon neutral mobility. Method: In a well-to-wheel analysis, we investigate the land use for the production, transport, storage, distribution, and charging/refuelling of carbon neutral energy carriers for various modes of transport. The analysis focuses on the Netherlands, but part of the results are expected to be broadly applicable to other countries as well. Findings: The results show that electricity from wind or solar energy supplied to electric vehicles is the most space efficient. Use of hydrogen and synthetic fuels in vehicles takes 2–5 times more land, while use of biofuels from energy crops takes 100 times more land compared to the electricity route. We also conclude that the indirect land use for energy supply for carbon neutral road transport in the Netherlands is in the same order of magnitude as the current direct land use of road mobility. [ABSTRACT FROM AUTHOR]

Despite the recent development in technology, Battery Electric Vehicle (BEV) pose several drawbacks including recharging time, limited range, and inadequate number of charging facilities. In an effort to address these drawbacks, Dynamic Wireless Charging (DWC) technology is gaining attention. DWC can be implemented by embedding the induction coil under a roadway pavement to dynamically charge the BEV in motion without a need to stop. This prompts an important question for infrastructure planning of BEVs: how to optimally locate DWC infrastructure in a road network. Planning for optimal DWC facility location needs to consider how BEV drivers will react to the newly implemented DWC in terms of route choice to reflect their unilateral utility minimization objective. Further complexities of DWC implementation include availability of zonal surplus electricity. In this paper, we propose a bi-level planning approach considering both the objectives of the planners and the drivers. The approach explicitly incorporates five elements: system-level social costs, travel patterns of individuals, trip completion assurance, zonal DWC implementation constraint due to energy availability from grid, and total budget availability from the public agency. The proposed framework is first demonstrated in a numerical experiment setting using Sioux Falls network. Then the framework is also implemented using city of Chicago sketch network to demonstrate its applicability to real-size networks. The numerical results using these two networks provide valuable insights for planners for developing an optimal DWC implementation plan. [ABSTRACT FROM AUTHOR]

Accurate short-term electricity load forecasts are critical for the secure and economic operation of power systems. This paper presents a computationally efficient and powerful three-stage model to accurately forecast short-term electricity load. Variational mode decomposition (VMD) was used in the first stage to extract features from the historical load signal. The stacked kernel extreme learning machine (KELM)-based auto-encoders were utilized in the unsupervised feature learning-based second stage. In the third stage, the high-order learned features were used as the inputs for the KELM-based regression model. The proposed deep KELM architecture combines stacked KELM-based auto-encoders and a KELM-based regression model to forecast short-term electricity load effectively. In order to examine the performance improvement of the proposed forecasting model, several performance comparison tests were realized using publicly available electricity load and day-ahead forecast data from the Turkish transmission system operator (TSO). The proposed model results were compared with state-of-the-art deep extreme learning machine (ELM) architectures as well as the benchmark forecasting models based on original ELM, KELM, artificial neural network (ANN), support vector machine (SVM), and regression tree (RT). The comparison results indicated that the proposed model outperformed state-of-the-art architectures and was significantly more successful than the TSO model. [ABSTRACT FROM AUTHOR]

This paper discusses a force sensing method using a built-in position sensing system for an electrostatic visuo-haptic display. The display provides passive haptic feedback on a flat panel visual monitor, such as LCD, using electrostatic friction modulation via multiple contact pads arranged on a surface-insulated transparent electrode. The display demonstrated in previous studies measured the positions of the pads in a similar manner to surface-capacitive touch-screens. This paper extends the sensor such that the system can monitor the electrostatic interaction force provided to the contact pads. The extension is realized by estimating the capacitance between the contact pads and the display surface. The paper investigates its basic characteristics to show that the force estimation is possible, regardless of the pad positions and the pushing force exerted by users. The force estimation capability is used for feedback control of interaction force, which improves the accuracy of the interaction force. The paper further extends the method such that the system can detect the moving direction of contact pads. By dividing the electrode of a contact pad and comparing their capacitances, the system can detect in which direction the user is trying to move the pad. Such capability is effective for solving the sticky-wall problem, which is known to be a common problem in passive haptic systems. A pilot experiment shows that the proposed system can considerably reduce the sticky-wall effect. [ABSTRACT FROM AUTHOR]

The performance of the heat exchanger has a huge impact on the cycle efficiency and levelised cost of electricity in solar thermal power generation. This paper proposes a novel scaled structure for the cold-side channel of the printed circuit heat exchanger (PCHE) that combines good heat transfer performance with lower pressure drop. A numerical simulation is employed to investigate and compare the performance of PCHEs with scaled, zigzag, and straight channels. When using the comprehensive performance evaluation criteria (PEC) as evaluation indicator, the scaled channel demonstrate the best overall performance, with an increase in comprehensive performance by 6 % and 12 % compared to zigzag and straight channels, respectively. The above conclusions indicate that the adoption of scaled channel structures could enhance the heat transfer performance of PCHE, providing valuable insights for the development and improvement of novel PCHE and other heat exchanger's heat transfer efficiency. [ABSTRACT FROM AUTHOR]

This paper presents experimental results of a study of the occurrence of electricity emission in the process of chemical-and-thermal treatment (CTT) and their theoretical justification. Oxide materials with semiconductor properties, along with metals, can form electric fields in technological mixtures that are used in processes related to the transfer of saturating elements to the hardened surface of a part made of steel X35CrNi2-3. The contribution of these fields to the transfer of saturating elements is comparable with that of a concentration field. A design and manufacturing technique for a measuring cell is proposed to determine the magnitude of the emission current arising in the process mixture. An equation is obtained that describes the general process of solid-phase saturation of parts with the participation of concentration and electric fields during CTT. [ABSTRACT FROM AUTHOR]

The performance of the heat exchanger has a huge impact on the cycle efficiency and levelised cost of electricity in solar thermal power generation. This paper proposes a novel scaled structure for the cold-side channel of the printed circuit heat exchanger (PCHE) that combines good heat transfer performance with lower pressure drop. A numerical simulation is employed to investigate and compare the performance of PCHEs with scaled, zigzag, and straight channels. When using the comprehensive performance evaluation criteria (PEC) as evaluation indicator, the scaled channel demonstrate the best overall performance, with an increase in comprehensive performance by 6 % and 12 % compared to zigzag and straight channels, respectively. The above conclusions indicate that the adoption of scaled channel structures could enhance the heat transfer performance of PCHE, providing valuable insights for the development and improvement of novel PCHE and other heat exchanger's heat transfer efficiency. [ABSTRACT FROM AUTHOR]

Electricity theft not only results in higher electricity costs for regular paying customers but is also a safety threat to the public due to illegal power connections made for cheating. Many governments and private companies have suffered substantial losses from electricity theft or fraud. Several approaches have been presented in the literature for electricity fraud detection. The existing methods for electricity fraud detection show poor performance as the electricity datasets are generally unbalanced or skewed. To handle skewness issues, SMOTE and ADASYN-based oversampling techniques are employed in some electricity fraud detection methods. However, existing oversampling methods typically increase the overlapping of classes and introduce additional noise. Therefore, in this paper, a new oversampling method named ADASYN-SGWO has been presented. The proposed ADASYN-SGWO employs ADASYN for oversampling and SSO-GWO for intelligent undersampling, which mitigates class imbalance issues in electricity datasets. After reducing the issue of class imbalance, random forest pipeline has been used to detect electricity fraud. To assess the efficiency of the proposed approach, precision, recall, F1 score, accuracy, Matthews correlation coefficient, and balance classification rate of the proposed and other state-of-the-art, including XG-boost classifier, Logistic regression, Gaussian naive Bayes, Random Forest classifier, Ada Boost classifier, LGBM classifier, and Quadrant discriminative analysis, have been computed and compared. Further, the proposed approach is also statistically validated using Friedman and post hoc analysis. The statistical analysis and experimental outcomes witnessed that the proposed ADASYN-SGWO-PRF method has outperformed all the other considered methods with 98.2% accuracy, which is significant for real-time applications of electricity theft detection problems. [ABSTRACT FROM AUTHOR]

Manufacturers and electricity companies can play a vital role in sustainable development through eco-innovation implementation and renewable electricity generation. Investigating electricity suppliers' and product manufacturers' decisions is essential to illustrate the evolution of their behavior. This paper establishes a two-population evolutionary game, focusing on the strategies of electricity suppliers and manufacturers. We consider two strategies for the electricity suppliers: (1) renewable electricity generation along with non-renewable electricity and (2) non-renewable electricity generation, and two strategies for manufacturers: (1) eco-innovation implementation and (2) Non-eco-innovation implementation. Also, pricing decisions for products and renewable electricity and determining the level of eco-innovation are investigated in this research. The results demonstrate that suppliers' evolutionary behavior leads to renewable electricity generation, and manufacturers' behavior drives eco-innovation implementation under the government's support. Also, the results indicate that the entire population of electricity suppliers shift toward their sustainable strategy, i.e., renewable electricity generation, in a shorter time than manufacturers. [ABSTRACT FROM AUTHOR]

Smart grid leverages distribution networks and intelligent transmission techniques to advance the delivery of electricity, with a primary focus on enhancing reliability, security, and efficiency. This advancement is realized through bidirectional data exchange for consumption monitoring and operation optimization within the electrical system. Smart grid infrastructures commonly depend on precise monitoring of electrical system parameters to enhance the reliability and robustness of the grid. It is crucial to underscore the significance of maintaining data integrity while disseminating these measurements across diverse grid stakeholders over expansive network infrastructures. In this paper, we propose a lightweight privacy-enhanced secure data sharing scheme for the smart grid, which not only ensures that only authorized users in the smart grid can efficiently access the smart grid data, but also prevents sensitive information leakage from the access policies revealing about the protected data, data owners, or recipients in smart grid. Besides, we also present strict security proofs to prove the resistance to chosen-ciphertext attacks (CCA). Finally, some efficiency analyses are made to indicate the most prominent energy-efficiency our scheme enables. [ABSTRACT FROM AUTHOR]

Purpose: In grid electricity consumption models, the location-based method uses regional average emission factors to account for environmental impacts. The market-based method is based on contractual agreements, verifying the exclusive claim on electricity from specific energy sources. An inconsistent application of these methods in life cycle assessment (LCA) and GHG accounting can lead to double counting. Especially, double counting electricity associated with rather low environmental impacts, such as renewable energy, might lead to impact underestimations. The aim of this paper is to identify, describe and propose solutions to double counting challenges. Methods: A four-step procedure is carried out. First, the specifications on grid electricity mix selection in frequently applied standards for LCA and GHG accounting are analysed. Besides the ISO norms for LCA (14040/44) and carbon footprinting (14064/67), the GHG Protocol and the Product and Organizational Environmental Footprint (PEF/OEF) are considered. Based on this analysis, challenges of double counting electricity from specific sources are identified. In the third step, potential solutions for avoiding double counting are proposed. The last research step consists of an illustrative case study to demonstrate the calculation of market-based electricity mixes and identify potential adjustments necessities for LCA application. Results and discussion: A parallel application of the location-based and the market-based method poses the main double-counting challenge. Thus, avoiding double counting demands consistent method application throughout the whole life cycle. Whereas this is relatively straightforward for the location-based method, consistent market-based method application is more challenging. LCAs rely on average life cycle inventory processes, which mostly include location-based electricity mixes. However, for consistent market-based method application throughout the life cycle, electricity-related environmental impacts in the inventory system also need to be market-based. This would demand a partial recalculation of LCI datasets using market-based residual electricity mixes. Besides illustrating the calculation of market-based electricity mixes, the case study is used to identify and propose solutions for two main challenges for residual mix application in LCA: countries without residual mix and electricity under a double marketing ban. Conclusion: Double counting of electricity from specific energy sources is a challenge, since it can lead to under- or overestimations of environmental impacts. Both the location-based and market-based method can avoid double counting. However, parallel or inconsistent applications of both methods lead to double counting. In order to avoid double counting, there is a need to enable and use consistent electricity accounting rules in LCA and GHG accounting. [ABSTRACT FROM AUTHOR]

It is important to achieve an efficient home energy management system (HEMS) because of its role in promoting energy saving and emission reduction for end-users. Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy. However, current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications. This can lead to an insufficient description of behavior and suboptimal management strategy. To address these gaps, this paper proposes non-intrusive load monitoring (NILM) assisted graph reinforcement learning (GRL) for intelligent HEMS decision making. First, a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads. Thus, efficient identification of user behavior and description of state transition can be achieved. Second, based on the online updating of the behavior correlation graph, a GRL model is proposed to extract information contained in the graph. Thus, reliable strategy under uncertainty of environment and behavior is available. Finally, the experimental results on several datasets verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Conventional residential electricity consumers are becoming prosumers who not only consume electricity but also produce it. This shift is expected to occur over the next few decades at a large scale, and it presents numerous uncertainties and risks for the operation, planning, investment, and viable business models of the electricity grid. To prepare for this shift, researchers, utilities, policymakers, and emerging businesses require a comprehensive understanding of future prosumers' electricity consumption. Unfortunately, there is a limited amount of data available due to privacy concerns and the slow adoption of new technologies such as battery electric vehicles and home automation. To address this issue, this paper introduces a synthetic dataset containing five types of residential prosumers' imported and exported electricity data. The dataset was developed using real traditional consumers' data from Denmark, PV generation data from the global solar energy estimator (GSEE) model, electric vehicle (EV) charging data generated using emobpy package, a residential energy storage system (ESS) operator and a generative adversarial network (GAN) based model to produce synthetic data. The quality of the dataset was assessed and validated through qualitative inspection and three methods: empirical statistics, metrics based on information theory, and evaluation metrics based on machine learning techniques. [ABSTRACT FROM AUTHOR]

In this paper, a new bi-level model is presented in order to set the interaction between different players in electricity market contracts. The proposed model framework is multi-objective so that the aims of retailer and customers are met simultaneously. In this study, new parameters and indexes for interaction of players will be introduced. In the upper level of this model, the retailer's profit will be maximized and in the lower level, the customer's cost will be minimized. Based on the retailer's interaction with customers in the proposed model, some customers will be selected from the customers group to contract with power retailer. In the proposed model, a robust optimization approach (ROA) is used to manage the retailer's risk in different conditions due to the uncertainty of energy supply by the wholesaler. The analysis of multi-objective approach in this model is also carried out based on Torabi-Hassini (TH) method. Finally, after the required linearization, the proposed Mixed Integer linear programming (MILP) model in the presence of Karush–Kuhn–Tucker conditions will implement in GAMS software and solved via GUROBI solver. [ABSTRACT FROM AUTHOR]

In this research paper, the problem of scheduling the home appliances is addressed. Generally, the householder's main attention is focused on minimizing his electricity payment by moving the use of the appliance from hours of high electricity rates to hours of low electricity rates. Consequently, the problem of peak demand is created. Thus, the main target behind this work is to propose an efficient scheduling strategy so that the electricity payment is reduced, the peak demand is minimized, and the user's comfort is maintained. The proposed scheduling problem is mathematically elaborated as Mixed-Integer Linear Programming where the decision variables indicate whether the electrical appliances are switched ON/OFF. The proposed strategy is implemented in four different scenarios with 10 appliances and proved to be efficient, simple, and reliable. To scrutinize the efficiency of the suggested method, similar studies in literature are used for comparison. Findings prove that the proposed method accurately minimizes the electricity payments, the peak demand while completing the 100% task required at the end of the day with lower computational time response. [ABSTRACT FROM AUTHOR]

Most of the electricity at present is generated from hydrocarbons like coal and gas. Their combustion is polluting the environment and raising the global temperature. Hence, there is an enhancement in catastrophes like floods, tornados, and droughts. Consequently, some part of the Earth is sinking, whereas there is a dearth of drinking water in some other part. A tribo-generator-integrated rainwater harvesting system for electricity and drinking purposes is proposed in the present paper to address these issues. A setup of the generating section of the scheme is developed and experimented in the laboratory. The obtained results show that the triboelectricity from rainwater depends on the rate of droplets falling per unit time, the height from which they are descending, and the coverage area of hydrophobic material. When released from 96 cm, the low- and high-intense rain generates 67.9 and 189 mV, respectively. Conversely, the electricity from the nano-hydro generator is proportional to the flow rate of water. 71.8 mV is observed at an average flow rate of 49.05 ml/s. [ABSTRACT FROM AUTHOR]

This article investigates the relationship between energy poverty and public expenditure for a sample of 20 Sub-Saharan countries over the period 2006–2020. This paper focuses on three research objectives. First is using a panel data threshold model with fixed effects proposed by Hansen (J Econom 93(2):345-368, 1999) to detect the presence of the nonlinear effect of public expenditure on energy poverty. Second, we try to explain the effect of public expenditure transmission channels on energy poverty. Third, we explore the impact of public expenditure on energy poverty considering the role of institution factors. Thus, our analysis approves the existence of a link between energy poverty and public expenditure with a threshold of about 17.65% of GDP. In addition, CO2 emissions can accelerate the growth of energy poverty in SSA countries. Indeed, the results show that in countries with high energy poverty, there is a bidirectional causal link between CO2 emissions and energy poverty. This would provide a better understanding of the relationship between public expenditure and energy poverty suggesting useful implications for policymakers in targeting sustainable energy. [ABSTRACT FROM AUTHOR]

Electricity theft is a major cause of non-technical loss (NTL) in smart grids. However, existing research on electricity theft detection (ETD) lacks a generalized analysis of the characteristics of theft behaviors and fails to effectively address the problem of unbalanced sample distribution; the electricity theft features they recognize are also singular. To address these problems, a novel approach consisting of a sample augmentation mechanism based on convolutional transformer-Wasserstein generative adversarial networks (CT-WGANs) and an electricity theft detection scheme using bridged multiscale convolutional neural network-bidirectional gate recurrent units (MCNN-BiGRUs) is proposed in this paper. First, the generalized characteristics of electricity theft behavior in multiple time dimensions are analyzed, and the data slices are constructed. Then, with the aim of reducing the influence of unbalanced sample distribution, CT-WGAN, which focuses on the generalized characteristics of electricity theft in various time dimensions, is designed and has better sample generation capability. Finally, a bridged MCNN-BiGRU is proposed to recognize the temporal, transition, and persistence characteristics of electricity theft to improve the efficiency of ETD. Experimental results on the State Grid Corporation of China (SGCC) and Irish Smart Energy Trial (ISET) datasets show that the proposed approach outperforms traditional schemes in terms of the area under curve (AUC), precision, recall, f1-score, and accuracy. [ABSTRACT FROM AUTHOR]

As an important part of demand-side management, residential demand response (DR) can not only reduce consumer's electricity costs, but also improve the stability of power system operation. In this regard, this paper proposes an optimal scheduling model of household appliances for smart home energy management considering DR. The model includes electricity cost, incentive and inconvenience of consumers under time-of-use (TOU) electricity price. Further, this paper discusses the influence of inconvenience weighting factor on total costs. At the same time, the influence of incentive on optimization results is also analyzed. The simulation results show the effectiveness of the proposed model, which can reduce 34.71% of consumer's total costs. It also illustrates that the total costs will be raised with the increase in inconvenience weighting factor. Thus, consumers will choose whether to participate in DR programs according to their preferences. Moreover, the result demonstrates that incentives are conducive to shifting load and reducing the consumer's total energy costs. The presented study provides new insight for the applications of residential DR. [ABSTRACT FROM AUTHOR]

This paper is focussed on employing satellite night lights (SNLs) to investigate access to electricity across the geographical regions in Nigeria. Specifically, we explore how SNLs interact with human and socioeconomic development indicators (population, poverty, and household consumption) to demonstrate the implications of slow and/or delayed progress in closing the electricity access gap in Nigeria. Our findings suggest that minimal progress has been made and there remains significant evidence of disproportionate spread of electricity across the country with most of the electricity visibility concentrated in the Southern regions, state capitals and industrial centres. Crucially, policy challenges and trade offs emerge. On one hand, is the need to address the long-standing issue of stranded and underutilised assets around power generation, transmission, and distribution and how these balance (or not) against additional and new capacity to enable sufficient, reliable and sustained electricity supply. On the other hand, is the challenge of ensuring that closing the access to electricity gap in Nigeria is done in a way that is just, fair, and equitable, with no part of society becoming worse-off or excluded. [ABSTRACT FROM AUTHOR]

In this paper, we propose an uncertain energy model with a time-varying volatility factor to describe the electricity and gas futures price dynamics. The corresponding spark-spread option pricing problem is also discussed. Numerical experiments show the effectiveness of proposed pricing method. Compared with the existing stochastic models, our uncertain energy model has a better performance to catch the price evolution of both gas and electricity futures. [ABSTRACT FROM AUTHOR]

This paper focuses on the forward contract price under a mean-reverting jump-diffusion electricity model and the Grandell idea. Based on historical spot prices from the electricity Nord Pool market, model parameters are calibrated under different conditions. First, we remove the jump data and calibrate the seasonal model parameters using least squares and Newton–Raphson methods. Then, the jump and the other parameters are obtained by the MLE method. Moreover, the first passage time is derived as a probability function and used it as an appropriate tool to know the behavior of the electricity spot price. Ultimately, months and quarters ahead forward data are achieved by the forward contract formula. [ABSTRACT FROM AUTHOR]

The estimation of feeder line losses plays a crucial guiding role in power system planning, design, production technology, and management level of a distribution system. It is also most important for the monitor of abnormal electricity consumption, such as electricity leakage or theft. To achieve this goal, the paper proposes a boost k-means based method for the estimation of feeder line losses. The index system for the power line loss analysis was established, and the characteristic indicators including the average line loss rate, line loss rate range, line loss rate fluctuation, and line loss rate gradient are calculated depending upon the line loss intensity and time change information. Furthermore, the established characteristic indicators are input to the boost k-means algorithm for the clustering calculation, and the final results are obtained along with the line loss characteristic analysis of each cluster. The proposed method is simple, fast, practical, and outperforms the traditional method with a highest exceeding accuracy of 20.37%. The experimental findings demonstrate the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]