Search Results (468)

To address the collaborative optimization challenge in multi-microgrid systems with significant renewable energy integration, this study presents a dual-layer optimization model incorporating power-hydrogen coupling. Firstly, a hydrogen energy system coupling framework including photovoltaics, storage batteries, and electrolysis hydrogen production/fuel cells was constructed at the architecture level to realize the flexible conversion of multiple energy forms. From a modeling perspective, the upper-layer optimization aims to minimize lifecycle costs by determining the optimal sizing of distributed PV systems, battery storage, hydrogen tanks, fuel cells, and electrolyzers within the microgrid. At the lower level, a distributed optimization framework facilitates energy sharing (both electrical and hydrogen-based) across microgrids. This operational layer maximizes yearly system revenue while considering all energy transactions—both inter-microgrid and grid-to-microgrid exchanges. The resulting operational boundaries feed into the upper-layer capacity optimization, with the optimal equipment configuration emerging from the iterative convergence of both layers. Finally, the actual microgrid in a certain area is taken as an example to verify the effectiveness of the proposed method. Full article

Despite the ongoing electromobility revolution in urban areas, fleet managers still prefer combustion engines over electric vehicles. Fleet electrification can deliver tangible benefits not only for the urban environment but also for the company itself. However, this requires a robust economic and technical analysis approach. This study assesses the technical and economic viability of integrating electric vehicle (EV) fleets into the Turkish manual frequency recovery reserve (mFRR) market. Using a life-cycle costing (LCC) framework, three operational scenarios are modeled: Baseline (leased EVs without V2G), V2G+ (leased EVs with aggregator-based mFRR), and High Utilization (owned EVs with full V2G integration and increased rental activity). The baseline scenario assumes a net cost of USD 142,500 over 10 years, excluding revenue share. V2G+ reduces this amount to USD 137,000, generating an annual income of approximately USD 4400 from its share of the frequency reserve. A high utilization scenario, combining V2G with ownership and higher rental income, reduces the net LCC to USD 125,500 and generates over USD 12,000 annually, reaching breakeven around year 7. Sensitivity analyses show that the financial profitability of the system is significantly influenced by EV purchase prices, aggregator fees, mFRR capacity payments, and vehicle utilization rates. Adding a 30–50% solar-powered charging enclosure further reduces operating costs by up to USD 21,500, demonstrating the synergistic potential of integrating V2G and distributed photovoltaics. These results influence not only the priorities for electrifying the urban vehicle fleet, but also smart city regulations in the area of energy management, through the development of bidirectional charging standards and pilot implementation of V2G in emerging markets such as Turkey. Full article

Transition to electric vehicles has accelerated in diverse consumer sectors all over the world. Electric School Buses (ESBs) are a particular area of interest due to their environmental and financial potential benefits, including Vehicle-to-Grid (V2G) synergies. Storing electricity in times of lower demand to supply the grid at optimal times can provide significant sustainability benefits, among them a reduction in new generation capacity and financial revenue for battery owners. ESBs, with their high-capacity batteries, have significant potential to supply the grid in V2G systems. There are more than half a million school buses in the US, with a wide geographical distribution, which have significant idle times during school days and holidays. This presents very attractive investment possibilities, providing school districts with additional revenue and supplying local communities with sustainable electricity at high-demand times. This study develops a framework to financially evaluate sustainability of ESB V2G schemes in the US. It applies data analytics, GIS, and Business Intelligence to integrate and assess publicly available data to provide stakeholders with decision-making tools in selecting optimal locations and operation times for these projects. Results indicate that revenue for these projects is significant in most schools, with some locations generating very high revenue potential. Geospatial analysis for most locations and time frames indicates very promising results, with schools potentially receiving significant income from these systems. The framework provides, therefore, relevant information for stakeholders to make sustainable decisions on the development of these projects. Full article

The distributed renewable energy sources have been rapidly increasing in distribution networks, and some of them are configured with energy storage devices. Indeed, sharing surplus energy storage capacities for subsidizing the investment costs is economically attractive. Although such willingness is emerging, targeted trading mechanisms are less explored. Inspired by the electricity markets, this paper innovates a peer-to-peer energy storage flexibility market within distribution networks, which involves multiple vendors and customers, accompanied by a marginal pricing mechanism to enable the economic reallocation of surplus energy storage capacities in distribution systems. A small-scale market is first studied to show the proposed market mechanism and a larger-scale case is used to further demonstrate the scalability and effectiveness of the mechanism. Case studies set three distinct scenarios: markets with or without deficits and with carryover energy constraints. The numerical simulation validates its ability in reflecting the capacity supply–demand relationship, ensuring revenue adequacy and effectively improving economic efficiency. Full article

Carbon neutrality has emerged as a critical issue in the 21st century, particularly in the Middle East and North Africa (MENA) region. These nations have demonstrated significant commitment to investing in renewable energy and implementing initiatives aimed at achieving carbon neutrality. The global spotlight on environmental concerns, encompassing the responsibilities of all economic stakeholders, has prompted the convening of COP 27, a pivotal meeting dedicated to reducing carbon emissions on a global scale. However, research on carbon neutrality in the MENA region remains relatively limited, particularly in terms of in-depth analysis of green energy, green technology, oil revenues, and the efficacy of government interventions. This study seeks to address this gap in existing research by investigating the factors influencing the attainment of carbon neutrality in the MENA region from 2000 to 2022. Specifically, the research focuses on the roles of green energy, green technology, oil revenues, and government effectiveness in this context. Utilizing the Method of Moments’ Quantile Regression, this study aims to analyze the impact of location and scale on the conditional distribution of carbon emissions. The findings reveal that investments in green energy, adoption of green technology, increases in service-added value, and oil revenues are associated with decreased carbon emissions, while greater trade openness correlates with emission reductions. However, all governance metrics examined exhibit a positive correlation with carbon emissions. These results underscore the importance of prioritizing investments in green energy and enhancing the effectiveness of governmental initiatives to steer economic growth towards achieving carbon neutrality. Moving forward, policymakers in the MENA region are encouraged to place greater emphasis on sustainable energy solutions and to implement strategies that enhance the efficacy of government interventions to accelerate progress towards carbon neutrality. Full article

Rural transformation is crucial to alleviating development pressure on traditional agricultural areas and stimulating rural vitality. This study aims to comprehensively analyze the spatio-temporal patterns, identify the key influencing factors, and propose targeted development strategies for rural transformation specifically within Northern Anhui, a quintessential traditional agricultural area in China. Utilizing the entropy method, exploratory spatial analysis, and geographic detector, we systematically evaluated the level of rural transformation and its spatial distribution characteristics across 35 counties and districts in Northern Anhui from 2011 to 2023. The results demonstrate a significant 35.93% increase in the average rural transformation level over the past decade, evolving from an initially low-level pattern to one characterized by “Central high, peripheral low”, with significantly narrowing disparities between counties and districts. Significant global positive spatial autocorrelation was consistently observed, alongside distinct localized clustering, including high-value clusters (H-H) and low-value clusters (L-L). A driver analysis identified investment efficiency, economic development level, industrialization, transportation accessibility, and fiscal revenue level as the predominant factors driving the spatial differentiation of rural transformation, with interaction detection revealing crucial synergistic effects among these factors. These findings provide valuable empirical insights and a scientific basis for formulating differentiated rural development strategies tailored to specific county types within traditional agricultural areas like Northern Anhui, thereby facilitating the rural transformation process in developing countries. Full article

Effective operation of any service system requires optimal organization of the sharing of resources between the users (customers). To this end, it is necessary to elaborate on the mechanisms that allow for the mitigation of congestion, i.e., the accumulation of many users requiring service. Due to the randomness of the user’s arrival process, congestions can occur even when an arrival rate is constant, e.g., the arrivals are described by the stationary Poisson process, which is assumed in the majority of existing papers. However, congestions can be more severe if the possibility of fluctuation of the instantaneous arrival rate exists. Such a possibility is an inherent feature of many systems and can be taken into account via the description of arrivals by the Markov arrival process (MAP). This makes the problem of congestion avoidance drastically more challenging. In many real-world systems, there exists the possibility of customer admission control via dynamic pricing. We propose a novel predictive mechanism of dynamic pricing. Decision moments coincide with the transition moments of the underlying process of the MAP. A customer may join or balk the system or postpone joining the system depending on the current cost. We illustrate the application of this mechanism in a multi-server retrial queueing model with dynamic service pricing. The behavior of the system is described by a multidimensional Markov chain with state-inhomogeneous transitions. Its stationary distribution is computed and may be used for solving the various problems of system revenue maximization via the choice of the proper pricing strategy. Full article

The global movement towards a cashless society has prompted the payment of tax obligations through digital platforms and sources. In this international race to ensure that transaction payments are not hindered by the lack of physical cash, Nigeria is also making progress. Therefore, the focus of this study is to assess the implications of digital payment systems in enhancing the effectiveness of tax revenue collection in Nigeria. The analysis spans from the first quarter of 2009 to the fourth quarter of 2023, utilizing the Autoregressive Distributed Lag and Error Correction Model. The research uses the most active digital payment systems that have been in operation during the study period. These electronic payment types include digital cheques (CHQs), Automated Teller Machines (ATMs), Point-of-Sales (POSs), Mobile payment (MPY), and Web-based payment (WPY). These are the predictor variables, while the tax revenue collection (TXC) during this period is the dependent variable. The control variables include information and telecommunication technology penetration rate (ICTPR), inflation, and gross domestic product. The outcomes of this study reveal that, over the long term, a percentage change in CHQs, ATMs, MPY, and ICTPR is linked to a decline of 8.1%, 12.5%, 6.7%, and 22.4% in TXC, respectively. In contrast, WPY indicates a 7.2% positive increase in TXC while inflation exerts a positive increase of 46.7%. The Error Correction Model (ECM) suggests that the deviations from the long-term equilibrium in earlier years are being corrected at a rate of 3.9% in the current year. In the short term, it is noted that digital payment systems do not influence TXC. On the other hand, GDP maintains a significant negative influence on TXC, in both the long- and short-term. Given these results, the study recommends the establishment of a robust information and communication technology (ICT) infrastructure to enhance effective tax collection, even from rural areas and the informal sector. It is also important for the government to develop strategies that will bring the informal sector into the tax net. Full article

Background. Length of stay, operationalized here as nights per booking (NPB), is a first-order driver of yield, labor planning, and environmental pressure. The COVID-19 pandemic and the rise of long-stay remote workers (often labeled “slomads”, a slow-travel subset of digital nomads) plausibly altered stay-length distributions, yet national, booking-weighted evidence for the United States remains scarce. Purpose. This study quantifies COVID-19 pandemic-era and post-pandemic shifts in U.S. Airbnb stay lengths, and identifies whether higher averages reflect (i) more long stays or (ii) longer long stays. Methods. Using every U.S. Airbnb reservation created between 1 January 2019 and 31 December 2024 (collapsed to booking-count weights), the analysis combines: weighted descriptive statistics; parametric density fitting (Gamma, log-normal, Poisson–lognormal); weighted negative-binomial regression with month effects; a two-part (logit + NB) model for ≥28-night stays; and a monthly SARIMA$(0,1,1){(0,1,1)}_{12}$ with COVID-19 pandemic-phase indicators. Results. Mean NPB rose from 3.68 pre-COVID-19 to 4.36 during restrictions and then stabilized near 4.07 post-2021 (≈10% above 2019); the booking-weighted median shifted permanently from 2 to 3 nights. A two-parameter log-normal fits best by wide AIC/BIC margins, consistent with a heavy-tailed distribution. Negative-binomial estimates imply post-vaccine bookings are 6.5% shorter than restriction-era bookings, while pre-pandemic bookings are 16% shorter. In a two-part (threshold) model at 28 nights, the booking share of month-plus stays rose from 1.43% (pre) to 2.72% (restriction) and settled at 2.04% (post), whereas the conditional mean among long stays was in the mid-to-high 50 s (≈55–60 nights) and varied modestly across phases. Hence, a higher average NPB is driven primarily by a greater prevalence of month-plus bookings. A seasonal ARIMA model with pandemic-phase dummies improves fit over a dummy-free specification (likelihood-ratio = 8.39, df = 2, p = 0.015), indicating a structural level shift rather than higher-order dynamics. Contributions. The paper provides national-scale, booking-weighted evidence that U.S. short-term-rental stays became durably longer and more heavy-tailed after 2020, filling a gap in the tourism and revenue-management literature. Implications. Heavy-tailed pricing and inventory policies, and explicit regime indicators in forecasting, are recommended for practitioners; destination policy should reflect the larger month-plus segment. Full article

With the integration of high-penetration renewable energy, existing uniform marginal pricing mechanisms face critical challenges, including difficulty in recovering flexibility resource capacity costs and free-riding phenomena caused by renewable energy’s variability. To address these issues, this paper proposes a differentiated pricing mechanism for renewable energy based on analytical uncertainty representation to avoid marginal price distortion and promote the rational allocation of ancillary service costs. Firstly, a joint clearing model for energy and reserve ancillary service is developed, incorporating a distributional robust chance constraint based on moment information to model the uncertainty of renewable energy. Then, the composition structure of the nodal marginal price for ancillary service demand is redefined, offering clearer and more explicit price signals compared with traditional uniform marginal pricing. After that, quantification of the impact of energy storage on renewable energy forecast errors and ancillary service pricing is conducted, with a systematic analysis of its role in reducing ancillary service costs and optimizing generation revenue. Simulation results on the modified IEEE 30-bus system demonstrate significant advantages over traditional uniform pricing: the proposed mechanism ensures fair cost allocation, effectively mitigates free-riding problems, and provides clear economic signals. With energy storage units regulating renewable power output, it could lead to a 12.9% reduction in ancillary service costs while increasing total generation revenue by 6.73%. Full article

Under China’s dual-carbon strategic objectives, virtual power plants (VPPs) actively participate in the coupled electricity–carbon market through the optimized scheduling of distributed energy resources, simultaneously stabilizing grid operations and reducing carbon emissions. Photovoltaic (PV) generation, a cornerstone resource within VPP systems, introduces significant challenges in scheduling due to its inherent output variability. To increase the accuracy in the characterization of the PV output uncertainty, a weighted probability distribution of solar irradiance, based on historical irradiance data, is newly proposed. The leveraging rejection sampling technique is applied to generate solar irradiance scenarios that are consistent with the proposed weighted solar irradiance probability model. Further, a confidence interval-based filtering mechanism is applied to eliminate extreme scenarios, ensuring statistical credibility and enhancing practicability in actual dispatch scenarios. Based on the filtered scenarios, a novel dispatch strategy for the VPP operation in the electricity–carbon market is proposed. Numerical case studies verify that scenarios generated by the weighted solar irradiance probability model are capable of closely replicating historical PV characteristics, and the confidence interval filter effectively excludes improbable extreme scenarios. Compared to conventional normal distribution-based methods, the proposed approach yields dispatch solutions that are more closely aligned with the optimal dispatch of the historical irradiance data, demonstrating the improved accuracy in the probabilistic modelling of the PV output uncertainty. Consequently, the obtained dispatch strategy shows the improved capability to ensure the market revenue of the VPP considering the fluctuations of the PV output. Full article

To address the conflicts between high-penetration distributed photovoltaics (PV) integration causing voltage limit violations, reverse power flow issues, and the grid connection needs of industrial and commercial users, this paper proposes an optimal capacity planning method for distributed PV considering the user’s grid connection locations. This method effectively increases the acceptance capacity of the distribution transformer network for distributed PV while ensuring the safe and stable operation of the distribution network. First, the source–load uncertainty is considered, and the k-means clustering algorithm is used to select multiple typical daily probability scenarios. Then, the PV optimal connection node range is obtained through a PV site selection and sizing model. For the planning of nodes within the optimal range, an optimal capacity planning model focusing on the economic benefits of users is established. This model aims to optimize the improvement of wheeling cost and maximize the economic benefits of grid-connected users by determining the optimal PV access capacity for each node. Finally, for PV users outside this range, after determining the maximum allowable capacity for each node, the capacity margin and static voltage stability are comprehensively considered to evaluate the network access scheme. Simulation examples are used to verify the effectiveness of the proposed method, and the simulation results show that the proposed method can effectively increase the acceptance capacity of the distribution network for photovoltaic systems. By fully considering the wheeling cost collection strategy, the distributed PV acceptance capacity is increased by 20.14%, while both user benefits and the operational safety and economic performance of the distribution network are significantly improved, ultimately resulting in a 27.77% increase in total revenue. Full article

As one of the main sources of global greenhouse gas emissions, the low-carbon transformation and emission reduction in the construction industry are inevitable requirements for addressing climate change. Under cap-and-trade regulations, Carbon emission rights have become a key production factor. However, the price of carbon emission rights is highly random. Taking the EU carbon market in 2024 as an example, the carbon price fluctuated by more than 35%, soaring from 65 euros per ton to 80 euros per ton and then falling back. Such sharp fluctuations not only increase the cost uncertainty of enterprises but also complicate the investment decisions for emission reduction. Therefore, enterprises can enhance the flexibility of carbon emission rights trading decisions through option strategies, helping them hedge against the risks of carbon price fluctuations, and at the same time improve market liquidity and risk management capabilities. Against this background, based on the carbon cap-and-trade policy, this paper introduces the two-way option strategy into the construction supply chain game model composed of general contractors and subcontractors, and studies to obtain the optimal carbon reduction volume, carbon option purchase volume, maximum expected profit of general contractors, subcontractors and profit distribution ratio. This study shows that two-way options play a crucial role in optimizing supply decision-making and emission reduction strategies. Under the decentralized model, emission reduction responsibilities are often shifted to subcontractors by the general contractor, resulting in a decline in overall mitigation effectiveness. Furthermore, appropriately lowering the carbon emission benchmark can strengthen enterprises’ incentives for emission reduction and significantly enhance the profitability of the supply chain. The study further suggests that general contractors should enhance their competitiveness by developing environmentally friendly technologies and improving their ability to reduce emissions on their own. Meanwhile, subcontractors need to actively participate in the collaborative efforts through revenue-sharing contracts. This study reveals the strategic value of two-way carbon options in construction supply chain carbon trading and provides theoretical support for the formulation of carbon market policies, contributing to the low-carbon transition of the construction supply chain. Full article

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

The growing concern about climate change and increased carbon emissions has promoted the electric vehicle market. Lithium-Ion Batteries (LIBs) are now the prevailing technology in electromobility, and large amounts will soon reach their end-of-life (EoL). Most counties have not designed sustainable reverse logistics networks to collect, remanufacture and recycle EoL electric vehicle batteries (EVBs). This study is focused on estimating the future EoL LIBs generation through dynamic material flow analysis using a three parameter Weibull distribution function under two scenarios for battery lifetime and then designing a reverse logistics network for the region of Attica (Greece), based on a generalizable modeling framework, to handle the discarded batteries up to 2040. The methodology considers three different battery handling strategies such as recycling, remanufacturing, and disposal. According to the estimated LIB waste generation in Attica, the designed network would annually manage between 5300 and 9600 tons of EoL EVBs by 2040. The optimal location for the collection and recycling centers considers fixed costs, processing costs, transportation costs, carbon emission tax and the number of EoL EVBs. The economic feasibility of the network is also examined through projected revenues from the sale of remanufactured batteries and recovered materials. The resulting discounted payback period ranges from 6.7 to 8.6 years, indicating strong financial viability. This research underscores the importance of circular economy principles and the management of EoL LIBs, which is a prerequisite for the sustainable promotion of the electric vehicle industry. Full article