Search Results (184)

This study presents a scenario analysis of Kazakhstan’s electricity market using the PyPSA-KZ model, with a focus on the integration of renewable energy sources (RES). As Kazakhstan transitions towards a low-carbon economy, this study evaluates the technical and economic implications of increasing RES penetration under various scenarios, ranging from 10% to 60% RES shares, with projections targeted for the year 2030. The study simulates system behavior across scenarios and analyzes key indicators, including total system cost, electricity tariff, generation mix, thermal ramping, and CO₂ emissions. Results indicate that up to 30% RES integration is feasible without significant structural changes, delivering reduced system costs and emissions. However, scenarios beyond 30% reveal growing flexibility challenges, necessitating investment in grid modernization, energy storage, and flexible backup capacity. The model outcomes are benchmarked against the International Energy Agency’s 2030 carbon neutrality scenarios and show strong alignment, particularly at 45% RES share. Comparative insights are also drawn from international experiences in Denmark and China. This research demonstrates that the PyPSA-KZ model is a powerful tool for planning Kazakhstan’s energy transition and offers data-driven recommendations to support national energy security and climate goals. Full article

South African small, medium and micro enterprises, particularly township-based spaza shops, face barriers to adopting solar photovoltaic systems due to upfront costs, regulatory uncertainty, and limited technical capacity. This article presents a reproducible methodology for evaluating and selecting solar photovoltaic systems that jointly considers economic, technological, and legal/policy criteria for such enterprises. We apply multi-criteria decision making using the Multi-Objective Optimization by the Ratio Analysis method, integrating simulation-derived techno-economic metrics with a formal policy-alignment score that reflects registration requirements, tax incentives, and access to green finance. Ten representative system configurations are assessed across cost and benefit criteria using vector normalization and weighted aggregation to enable transparent, like-for-like comparison. The analysis indicates that configurations aligned with interconnection and incentive frameworks are preferred over non-compliant options, reflecting the practical influence of policy eligibility on investability and risk. The framework is lightweight and auditable, designed so that institutional actors can prepare shared inputs while installers, lenders, and shop owners apply the ranking to guide decisions. Although demonstrated in a South African context, the procedure generalizes by substituting local tariffs, irradiance, load profiles, and jurisdiction-specific rules, providing a portable decision aid for small enterprise energy transitions. Full article

Background: Caesarean section (CS) is a critical intervention, yet stark inequities in access persist across low- and middle-income countries (LMICs). Over the last decade, governments have introduced policies to eliminate or subsidize user fees; however, the collective impact of these initiatives on utilization, equity, and financial protection has not been fully synthesized. Methods: We conducted a systematic review in line with PRISMA 2020 guidelines. Searches were conducted in PubMed, Dimensions, Google Scholar, Scopus, Web of Science, and government portals for studies published between 1 January 2009 and 30 May 2025. Eligible studies evaluated government-initiated financing reforms, including full user-fee exemptions, partial subsidies, vouchers, insurance schemes, and provider-payment restructuring. Two reviewers independently applied the PICOS criteria, extracted data using a 15-item template, and assessed the study quality. Given heterogeneity, results were synthesized narratively. Results: Thirty-seven studies from 28 LMICs were included. Most (70%) evaluated fee exemptions. Mixed-methods and cross-sectional designs predominated, while only six studies employed interrupted time series designs. Twenty-two evaluations (59%) reported increased CS uptake, ranging from a 1.4-fold rise in Senegal to a threefold increase in Kano State, Nigeria. Similar surges were also observed in non-African contexts such as Iran and Georgia, where reforms included incentives for vaginal delivery or punitive tariffs to curb overuse. Fourteen of 26 fee-exemption studies documented pro-rich or pro-urban drift, while catastrophic expenditure persisted for 12–43% of households, despite the implementation of “free” policies. Median out-of-pocket costs ranged from USD 14 in Burkina Faso to nearly USD 300 in Dakar’s slums. Only one study linked reforms to a reduction in neonatal mortality (a 30% decrease in Mali/Benin), while none demonstrated an impact on maternal mortality. Qualitative evidence highlighted hidden costs, delayed reimbursements, and weak accountability. At the same time, China and Bangladesh demonstrated how demographic reforms or voucher schemes could inadvertently lead to CS overuse or expose gaps in service readiness. Conclusions: Government-led financing reforms consistently increased CS volumes but fell short of ensuring equity, financial protection, or sustained quality. Effective initiatives combined fee removal with investments in surgical capacity, timely reimbursement, and transparent accountability. Future CS policies must integrate real-time monitoring of equity and quality and adopt robust quasi-experimental designs to enable mid-course correction. Full article

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks. Full article

With the rapid growth in the penetration of Electric Vehicles (EVs), their large-scale uncoordinated charging behavior presents significant challenges to the hosting capacity of traditional distribution networks (DNs). The novelty of this paper lies in its methodology, which integrates a Markov Chain Monte Carlo (MCMC) method for realistic load profiling with a bi-level optimization framework for Time-of-Use (TOU) pricing, whose effectiveness is then rigorously evaluated through an Optimal Power Flow (OPF)-based assessment of the grid’s hosting capacity. First, to compensate for the limitations of historical data, the MCMC method is employed to simulate the uncoordinated charging process of a large-scale EV fleet. Second, the bi-level optimization model is constructed to formulate a globally optimal TOU tariff that maximizes charging cost savings for EV users. At the same time, its lower-level simulates the optimal economic response of the EV user population. Finally, the change in the minimum daily hosting capacity is calculated based on the OPF. Case study simulations for IEEE 33-bus and IEEE 69-bus systems demonstrate that the proposed model effectively shifts charging loads to off-peak hours, achieving stable user cost savings of 20.95%. More importantly, the findings reveal substantial security benefits from this economic strategy, validated across diverse network topologies. In the 33-bus system, the minimum daily capacity enhancement ranged from 174.63% for the most vulnerable node to 2.44% for the strongest node. In the 69-bus system, vulnerable nodes still achieved a significant 78.62% improvement. This finding highlights the limitations of purely economic assessments and underscores the necessity of the proposed integrated framework for achieving precise, location-dependent security planning. Full article

Water scarcity and rising urban demand pose growing challenges for sustainable water management in Brazil, where over 73 million people may face shortages by 2035. Given this scenario, rainwater utilisation has emerged as a strategic alternative for preserving water resources, helping to reduce potable water consumption and relieving demand on public supply systems. This study aimed to evaluate the potential for potable water savings through the implementation of a rainwater harvesting system in a fitness centre without a swimming pool, located in southern Brazil—a building typology rarely addressed in the literature. Water end-uses were empirically characterised using water flow measurements and questionnaires conducted in an existing facility operated by the same franchise. A daily balance simulation was performed using the Netuno computer programme (Version 4), and an economic feasibility assessment was conducted based on local costs and tariff structures. The results showed that non-potable end-uses represented 24.4% of total water consumption. The rainwater harvesting simulation indicated an ideal tank capacity of 11,000 L, enabling potable water savings of 7.04%. The economic analysis showed an implementation cost of R$13,240.72 and a consequent return on investment of fifteen months. These findings confirm the technical and economic viability of rainwater harvesting systems for fitness centres and highlight the relevance of local conditions in shaping performance and investment returns. Full article

With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems. Full article

Many electric vehicle (EV) batteries will retire in the next 5–10 years around the globe. These batteries are retired when no longer suitable for energy-intensive EV operations despite having 70–80% capacity left. The second-life use of these battery packs has the potential to address the increasing demand for battery energy storage systems (BESSs) for the electric grid, which will also create a robust circular economy for EV batteries. This article proposes a two-layered energy management algorithm (monthly layer and daily layer) for demand charge reduction for an industrial consumer using photovoltaic (PV) panels and BESSs made of retired EV batteries. In the proposed algorithm, the monthly layer (ML) calculates the optimal dispatch for the whole month and feeds the output to the daily layer (DL), which optimizes the BESS dispatch, BESSs’ degradation, and energy imported/exported from/to the grid. The effectiveness of the proposed algorithm is tested as a case study of an industrial load using a real-world demand charge and Real-Time Pricing (RTP) tariff. Compared with energy management with no consideration of degradation or demand charge reduction, this algorithm results in 71% less degradation of BESS and 57.3% demand charge reduction for the industrial consumer. Full article

The vast volume of media content, combined with the costs of manual annotation, challenges scalable codebook analysis and risks reducing decision-making accuracy. This study evaluates the effectiveness of large language models (LLMs) and multi-agent teams in structured media content analysis based on codebook-driven annotation. We construct a dataset of 200 news articles on U.S. tariff policies, manually annotated using a 26-question codebook encompassing 122 distinct codes, to establish a rigorous ground truth. Seven state-of-the-art LLMs, spanning low- to high-capacity tiers, are assessed under a unified zero-shot prompting framework incorporating role-based instructions and schema-constrained outputs. Experimental results show weighted global F1-scores between 0.636 and 0.822, with Claude-3-7-Sonnet achieving the highest direct-prompt performance. To examine the potential of agentic orchestration, we propose and develop a multi-agent system using Meta’s Llama 4 Maverick, incorporating expert role profiling, shared memory, and coordinated planning. This architecture improves the overall F1-score over the direct prompting baseline from 0.757 to 0.805 and demonstrates consistent gains across binary, categorical, and multi-label tasks, approaching commercial-level accuracy while maintaining a favorable cost–performance profile. These findings highlight the viability of LLMs, both in direct and agentic configurations, for automating structured content analysis. Full article

The study examines the impact of pricing strategies on the competition and cooperation of cross-border ports, focusing on unified pricing and differential pricing. The results show that the inside border port can adopt a differentiation strategy to enhance its benefits, as this strategy allows for better control. Additionally, while the differentiated pricing strategy is an equilibrium strategy for the inside border port, blockchain technology can enhance the economic benefits of the inside border port under certain conditions, which also demonstrates the commercial value of blockchain in data sharing. Moreover, the expansion of port capacity can reduce the congestion of the inside border port to some extent under specific conditions. Finally, the study analyzes the environmental impact, tariff impact, and influence of port cooperation, which provides some management implications for inside border port. In summary, the findings highlight the potential of blockchain to optimize pricing strategy and promote cooperation between regional ports, thus improving economic benefits. Full article

Accurate gas consumption forecasting is critical for modern energy systems due to complex consumer behavior and regulatory requirements. Deep neural networks (DNNs), such as Seq2Seq with attention, TiDE, and Temporal Fusion Transformers, are promising for modeling complex temporal relationships and non-linear dependencies. This study compares state-of-the-art architectures using real-world data from over 100,000 consumers to determine their practical viability for forecasting gas consumption under operational and regulatory conditions. Particular attention is paid to the impact of data quality, feature attribution, and model reliability on performance. The main use cases for natural gas consumption forecasting are tariff setting by regulators and system balancing for suppliers and operators. The study used monthly natural gas consumption data from 105,527 households in the Volyn region of Ukraine from January 2019 to April 2023 and meteorological data on average monthly air temperature. Missing values were replaced with zeros or imputed using seasonal imputation and the K-nearest neighbors. The results showed that previous consumption is the dominant feature for all models, confirming their autoregressive origin and the high importance of historical data. Temperature and category were identified as supporting features. Improvised data consistently improved the performance of all models. Seq2SeqPlus showed high accuracy, TiDE was the most stable, and TFT offered flexibility and interpretability. Implementing these models requires careful integration with data management, regulatory frameworks, and operational workflows. Full article

In response to the growing prevalence of tariffs as instruments of economic policy and strategic competition, this paper introduces a formal mathematical framework for optimizing counter-tariff strategies. We model the global trade ecosystem as a multi-layered, directed, weighted hypergraph, where vertices represent countries, industries, and subindustries, and hyperedges capture complex trade relationships and supply chain dependencies. The proposed framework employs bilevel optimization techniques to maximize strategic impact on target economies while minimizing self-inflicted economic costs. Through integration of graph theory, spectral analysis, and multilevel optimization methods, we develop a rigorous formalism that enables policymakers to identify optimal counter-tariff portfolios under various constraints. Our model explicitly accounts for industrial interdependencies, where export competitiveness depends on imported inputs, thus providing a more realistic representation of global value chains. Case studies applying our model to historical trade disputes demonstrate its capacity to generate superior strategic outcomes compared to conventional approaches. The framework’s axiomatic foundation allows for rapid recalibration in response to shifting economic conditions and policy objectives. Full article

This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition of residential grid feed-in, which limits the use of conventional peer-to-peer energy models. Additionally, fixed pricing is required to ensure simplicity and trust among users. SEAMS coordinates prosumer and consumer households, a shared battery energy storage system (BESS), and a centralized aggregator (AGG) to minimize total electricity costs while maintaining financial neutrality for the aggregator. A mixed-integer linear programming (MILP) model is developed to jointly optimize PV sizing, BESS capacity, and internal buying price, accounting for Time-of-Use (TOU) tariffs and local policy limitations. Simulation results show that a 6 kW PV system and a 70–75 kWh shared BESS offer optimal performance. A 60:40 prosumer-to-consumer ratio yields the lowest total cost, with up to 49 percent savings compared to grid-only systems. SEAMS demonstrates a scalable and policy-aligned approach to support Thailand’s transition toward decentralized solar energy adoption and improved energy affordability. Full article

With the rapid development of a new power system under the “dual carbon” goal, pumped storage has gained increasing attention for its role in integrating renewable energy and enhancing power system flexibility and security. This study proposes a dynamic benefit evaluation method for pumped storage projects, addressing the limitations of static analyses in capturing the evolving benefit trends. In this paper, the multi-stage dynamic benefit evaluation model was constructed by introducing time-of-use tariffs, periodic capacity pricing mechanism, and ancillary service revenue prediction based on machine learning and the multiple regression method. Sensitivity analysis was applied to explore the impact of key parameter variations on economic indicators. The results show that the benefit structure differs significantly across stages, and with electricity market development, a diversified pattern supported by electricity, capacity, and ancillary service revenues will emerge. The application of the model to an actual operating pumped storage power station yielded an internal rate of return of 8.18%, a payback period of 16.4 years, and a 26% increase in net present value compared with traditional methods. The proposed model expands the theoretical framework for pumped storage benefit evaluation and provides strong support for investment decisions, policy design, and operational strategy optimization. Full article

The Renewable Energy Communities (RECs) and self-consumption frameworks defined in Directive (EU) 2023/2413 and Directive (EU) 2024/1711 are currently being integrated into national regulations across EU member states, adapting legislation to incorporate these new entities. These regulations establish key principles for individual and collective self-consumption, outlining operational rules such as proximity constraints, electricity sharing mechanisms, surplus electricity management, grid tariffs, and various organizational aspects, including asset sizing, licensing, metering, data exchange, and role definitions. This study introduces a model tailored to optimize investment and energy-sharing decisions within RECs, enabling multiple members to invest in solar photovoltaic (PV) and wind generation assets. The model determines the optimal generation capacity each REC member should install for each technology and calculates the energy shared between members in each period, considering site-specific constraints on renewable deployment. A case study with a four-member REC is used to showcase the model’s functionality, with simulation results underscoring the benefits of CSC over ISC. Full article