Search Results (216)

The widespread adoption of electricity market trading platforms has enhanced the standardization and transparency of trading processes. As markets become more liberalized, regulatory policies are phasing out protective electricity pricing mechanisms, leaving retailers exposed to price volatility risks. In response, demand for risk management tools has grown significantly. Futures contracts serve as a core instrument for managing risks in the energy sector. This paper proposes a futures-based risk hedging model grounded in electricity price forecasting. A price prediction model is constructed using historical data from electricity markets and energy futures, with SHAP values used to analyze the transmission effects of energy futures prices on monthly electricity trading prices. The Monte Carlo simulation method, combined with a t-GARCH model, is applied to calculate CVaR and determine optimal portfolio weights for futures products. This approach captures the volatility clustering and fat-tailed characteristics typical of energy futures returns. To validate the model’s effectiveness, an empirical analysis is conducted using actual market data. By forecasting electricity price trends and formulating futures strategies, the study evaluates the hedging and profitability performance of futures trading under different market conditions. Results show that the proposed model effectively mitigates risks in volatile market environments. Full article

Radio Frequency Identification (RFID) systems operating in the 860–960 MHz frequency range are widely used in applications such as supply chain management, retail, access control, healthcare, and transportation. This study presents the design, modeling, and fabrication of two antennas for this frequency range, followed by a comparative analysis to identify the antenna with superior gain. Key parameters, including corner fillets and chamfering, as well as antenna length, were varied to evaluate their impact on gain and S-parameters for the initial antenna considered the best from the two structures analyzed, aiming to optimize performance while minimizing size and keeping the frequency unchanged. Additionally, the antennas’ interaction with the human body was assessed through numerical modeling by evaluating the electric and magnetic fields and calculating the specific absorption rate for a human leg and hand in order to analyze the impact of these types of antennas on the human body. The dimensions of the initial structure were minimized while the antenna operated in the same frequency range, leading to a small decrease in the gain. It was discovered that when analyzing the values of the parameters of interest regarding the interaction with a human body, the RFID will not exceed them when considering the human hand, but it will harm a human foot when not placed at a specific distance from it. Full article

This study develops a carbon emissions reduction strategy for solar-powered coffee machines in Taiwanese convenience stores, aiming to strike a balance between profitability and decarbonization. An integrated framework of the fuzzy nonlinear multi-objective programming (FNMOP) model, Sobol sensitivity analysis, and Monte Carlo simulation was applied to quantify uncertainties in electricity supply, consumer demand, and investment costs. Solar-powered machines reduce annual CO₂ emissions by 172–215 kg per store. Allocating 0.49–0.61% of coffee profits as subsidies shortens payback to [6.5, 9.375] years. Monte Carlo simulation confirms robustness with a 95% confidence interval of [5.8, 11.2] years, while urban stores achieve payback 18–25% faster. Sobol analysis identifies annual savings and net profit margins as key drivers. The framework demonstrates scalability and international applicability, providing empirical evidence for policymakers and retailers to accelerate the adoption of renewable energy in consumer-facing operations. Its methodological integration and consumer-side focus offer a replicable model for convenience store chains in high-density retail markets worldwide, with potential multiplier effects across sectors and supply chains. Full article

To address the issues of imprecise user segmentation, inadequate handling of fuzzy evaluation information, and low recommendation accuracy in current electricity retail package recommendations, a novel recommendation method based on K-prototypes clustering and interval-valued intuitionistic fuzzy theory is proposed. First, a multi-dimensional user profile is constructed, incorporating five numerical tags—such as monthly average electricity consumption and monthly load factor—and two categorical tags: industry characteristics and value-added service demand. The K-prototypes algorithm is employed to cluster users, effectively resolving the profile distortion problem caused by the neglect of categorical features in traditional K-means clustering. Second, interval-valued intuitionistic fuzzy numbers are introduced to transform user linguistic evaluations into quantitative indicators. A projection measure-based model is established to objectively determine attribute weights, thereby eliminating subjective weighting bias. Finally, a comprehensive ranking of electricity retail packages is generated by integrating satisfaction levels of similar users and similar measures of new users. The recommendation performance is validated using Root Mean Square Error (RMSE), Kendall’s τ, Normalized Discounted Cumulative Gain (NDCG@5), and Discrimination Index (S). A case study involving users from a region in China demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) to 0.32, which is 31.25% lower than the next best traditional method (K-prototypes + equal weight clustering with RMSE = 0.48), accurately addresses the core demands of diverse user groups, significantly improves recommendation precision and user satisfaction, and exhibits substantial practical application value. Full article

The energy sector is currently under enormous transition, moving from fossil fuels to renewable energies and integrating energy efficiency measures. This transition can hold opportunities for new and innovative energy systems. This study presents an energetic and economic assessment of an innovative tri-generation unit working with a two-phase thermodynamic cycle. The tri-generation unit is driven by heat and is capable of providing heat at lower level, cold, and electricity to end users. The use cases—residential, day-use offices, commercial retail, and manufacturing industry—are integrated in a dynamic simulation model, indicating the operation mode of the unit. The results show that the tri-generation unit is able to provide heat and cold with an Energy Utilization Factor of 35% to 68%, depending on the use case. Solar thermal has a limited to potential to supply the unit with heat, due to the high temperature of 180 °C and the required unit operation at nighttime. The economic comparison indicates that the driving heat must be as low as possible and that savings through self-consumption is most relevant. Full article

This paper addresses the retail pricing problem for electricity retailers who also act as virtual power plant (VPP) operators, aggregating distributed energy resources (DERs). In future power markets where multiple such retailers compete for customers, a key challenge is to design pricing strategies that balance economic profitability with low-carbon objectives. Existing research often overlooks the impact of retailers’ heterogeneous resource portfolios, particularly the share of low-carbon resources like photovoltaics (PVs), on their competitive advantage and pricing decisions. To bridge this gap, we propose a novel retail pricing model that integrates a non-cooperative game framework with Markov Decision Processes (MDPs). The model enables each retailer to formulate optimal real-time pricing strategies by anticipating competitors’ actions and customer responses, ultimately reaching a Nash equilibrium. A distinctive feature of our approach is the incorporation of spatially differentiated carbon emission factors, which are adjusted based on each retailer’s share of PV generation. This creates a tangible low-carbon incentive, allowing retailers with greener resource mixes to leverage their environmental advantage. The proposed framework is validated on a modified IEEE 30-bus system with six competing retailers. Simulation results demonstrate that our method effectively incentivizes optimal load distribution, alleviates network congestion, and improves branch loading indices. Critically, retailers with a higher share of PV resources achieved significantly higher profits, directly translating their low-carbon advantage into economic value. Notably, the Branch Load Index (BLI) was reduced by 12% and node voltage deviations were improved by 1.32% at Bus 12, demonstrating the model’s effectiveness in integrating economic and low-carbon objectives. Full article

The recommendation of electricity retail packages is challenged by diversified user demands and the complexity of evaluation information in liberalized electricity markets. Existing approaches are often limited by the subjectivity of user clustering and the difficulty of accurately capturing cognitive fuzziness and dynamic weight variations in the decision-making process. To address these challenges, this paper proposes a novel recommendation framework that integrates Improved Density Peaks Clustering (IDPC) with group decision-making based on trapezoidal fuzzy numbers. First, an IDPC-based model is constructed to objectively identify and partition users into homogeneous groups based on similar electricity consumption characteristics. Subsequently, a dynamic multi-attribute group decision-making model, which synergizes trapezoidal fuzzy numbers and the Multi-Criteria Compromise Ranking Method (MCRM), is designed to aggregate evaluation information from these user groups and to score the retail packages. Furthermore, a full-ranking recommendation strategy is established based on group satisfaction levels. Finally, a case study using a real-world dataset from a region in Eastern China is conducted. The empirical results demonstrate the framework’s superior performance: the IDPC algorithm achieves a stable Davies–Bouldin index of approximately 1.4, and the final recommendation ranking yields a Spearman correlation coefficient of 0.9 against simulated actual choices, significantly outperforming benchmark methods. This study shows that the proposed method can effectively enhance the precision and relevance of package recommendations, providing crucial decision support for electricity retailers in implementing refined marketing strategies. Full article

Are environmental regulations the primary driver of rising electricity prices? Evidence from the Regional Greenhouse Gas Initiative (RGGI) suggests a more nuanced reality. This paper examines the impact of RGGI on wholesale and retail electricity prices using a difference-in-differences framework. We analyze three key policy events—the 2005 announcement, the 2009 implementation, and the 2014 adjustment of the emissions cap—drawing on detailed panel data from power plants in both RGGI and non-RGGI states. Our results indicate that wholesale electricity prices in RGGI states did not increase following the 2005 announcement relative to non-RGGI states. By contrast, retail electricity prices rose by about 11% in the short run, coinciding with electricity market restructuring, though this retail price gap declined over time. Over the subsequent decade, RGGI states achieved substantial reductions in CO₂ emissions alongside a transition to cleaner generation technologies. Importantly, the industry’s response to environmental regulation did not immediately affect electricity prices. However, as the emissions cap tightened, price effects became more pronounced: following the 2014 adjustment that reduced the cap to roughly 50% of its 2008 level, wholesale prices increased by 0.68 to 5.57 cents/kWh. These findings suggest that while the short-run effects of environmental regulation on electricity prices are limited, more stringent caps over time can lead to measurable price impacts. Full article

The importance of Last-Mile Delivery (LMD) in the current economy cannot be overstated, as it is the final and most crucial step in the supply chain between retailers and consumers. In major cities, absent intervention, urban LMD emissions are projected to rise by >30% by 2030 as e-commerce grows (top-100-city “do-nothing” baseline). Sustainable, innovative ground-based solutions for LMD, such as Electric Vehicles, autonomous delivery robots, parcel lockers, pick-up points, crowdsourcing, and freight-on-transit, can revolutionize urban logistics by reducing congestion and pollution while improving efficiency. However, developing these solutions presents challenges in Operations Research (OR), including problem modeling, optimization, and computations. This systematic review aims to provide an OR-centric synthesis of sustainable, ground-based LMD by (i) classifying these innovative solutions across problem types and methods, (ii) linking technique classes to sustainability goals (cost, emissions/energy, service, resilience, and equity), and (iii) identifying research gaps and promising hybrid designs. We support this synthesis by systematically screening 283 records (2010–2025) and analyzing 265 eligible studies. After the gap analysis, the researchers and practitioners are recommended to explore new combinations of innovative solutions for ground-based LMD. While they offer benefits, their complexity requires advanced solution algorithms and decision-making frameworks. Full article

While industrial emissions research has historically focused on energy-intensive sectors like steel, cement, and chemicals, this study addresses a critical gap by examining barriers across all the manufacturing industry in the U.S. Sectors like food processing, retail, plastics, and transportation face unique challenges distinct from heavy industry, operating on thin margins with limited bargaining power while experiencing heightened consumer and stakeholder pressure for improved environmental responsibility. Through structured interview data collection process and using quantitative ratings and qualitative analysis, this research identifies and categorizes emission reduction barriers across four key themes: financial, technical, organizational, and regulatory. Unlike energy-intensive industries that may pursue hydrogen or carbon capture technologies, discrete manufacturing industry like automotive, electrical and electronics, and machine manufacturers typically focus on energy efficiency, electrification of thermal processes, and alternate fuel switching, solutions better aligned with their lower-temperature processes and distributed facility profiles. The study’s primary contribution lies in documenting specific barrier manifestations within organizations and identifying proven mitigation strategies that companies have successfully implemented or observed among peers. Full article

Commercial buildings and shopping malls face rising electricity costs and increasing pressure to adopt sustainable practices. This paper presents the first long-term, multi-site empirical validation of Automatic Voltage Regulator (AVR) deployment in Thai retail facilities, providing robust evidence for tropical, motor-heavy load contexts. The study evaluates the engineering, economic, and environmental performance of an AVR with an autotransformer core under real operating conditions. High-resolution measurements were collected before and after AVR installation, using Class 0.2s analyzers and a Building Energy Management System (BEMS) across multiple branches during a four-month monitoring campaign (February–May). Results indicate that a modest voltage reduction of 8.06% yielded a 12.02% decrease in active power demand, a 6.22% current reduction, and a 2.26% improvement in power factor. The greatest savings occurred in HVAC (8.19%) and refrigeration loads (8.20%), while lighting loads remained nearly unchanged. Economically, the system delivered ~177 kWh/day savings, equivalent to 262,212 THB/year, with a simple payback of 2.67 years and an ROI of 37.5%. Environmentally, the AVR reduced 36.6 tCO₂/year (±5%), aligning with Thailand’s Energy Efficiency Plan (EEP) 2018–2037 and Carbon Neutrality Roadmap and offering additional potential for T-VER monetization. These findings confirm AVR technology as a scalable, standards-compliant, and high-return retrofit solution for commercial facilities in tropical climates. Full article

Fresh-cut fruit salads (fruit cocktails) are marketed as a convenient food item with a limited shelf-life (4 days at 4 °C). Given rising electricity prices, increased cooling temperature during production, transport, and retail from 4 °C to 8 °C and extended shelf-life from four to eight days without compromising food safety are discussed. This study investigates the proliferation of Listeria monocytogenes in ready-to-eat (RTE) fresh-cut fruit cocktails at three temperature regimes. The fruit cocktail, consisting of pineapple, red apples, cantaloupe, and red grapes, was inoculated with a clinical strain of L. monocytogenes (SLV 444; CCUG 69007) and stored at 4 °C, 8 °C, or a dynamic temperature regime (4 °C for one day, 8 °C for seven days). After four-day storage at 4 °C, growth of L. monocytogenes was not supported. Despite the fruit cocktail’s pH below the minimum requirements of the target organism, all other treatments supported growth of L. monocytogenes, but below the legal limit of 2 log CFU + 1 g⁻¹ per fruit cocktail. There is an increased risk of exceeding the microbiological safety end product criteria, especially at 8 °C or dynamic storage temperatures, if seemingly insignificant Listeria contamination is present in or on fruit cocktail ingredients. Full article

Forecasting day-ahead electricity prices is a crucial research area. Both wholesale and retail sectors highly value improved forecast accuracy. Renewable energy sources have grown more influential and effective in the US power market. However, current forecasting models have shortcomings, including inadequate consideration of renewable energy impacts and insufficient feature selection. Many studies lack reproducibility, clear presentation of input features, and proper integration of renewable resources. This study addresses these gaps by incorporating a comprehensive set of input features, while these features are engineered to capture complex market dynamics. The model’s unique aspect is its inclusion of renewable-related inputs, such as temperature data for solar energy effects and wind speed for wind energy impacts on US electricity prices. The research also employs data preprocessing techniques like windowing, cleaning, normalization, and feature engineering to enhance input data quality and relevance. We developed four advanced hybrid deep learning models to improve electricity price prediction accuracy and reliability. Our approach combines variational mode decomposition (VMD) with four deep learning (DL) architectures: dense neural networks (DNNs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and bidirectional LSTM (BiLSTM) networks. This integration aims to capture complex patterns and time-dependent relationships in electricity price data. Among these, the VMD-BiLSTM model consistently outperformed the others across all window implementations. Using 24 input features, this model achieved a remarkably low mean absolute error of 0.2733 when forecasting prices in the MISO market. Our research advances electricity price forecasting, particularly for the US energy market. These hybrid deep neural network models provide valuable tools and insights for market participants, energy traders, and policymakers. Full article

This study examines the causal impact of China’s New Energy Demonstration City construction policy on corporate energy consumption. The results demonstrate that this policy effectively reduces corporate energy consumption. The policy significantly decreases the consumption of coal, natural gas, and diesel. Although the policy significantly reduces energy consumption in both local state-owned enterprises (SOEs) and non-SOEs, its effect does not show statistically significant variation across different types of controlling shareholders. The energy-saving effect is particularly pronounced in the following industries: Manufacturing, Electricity, Heat, Gas, and Water Production & Supply, Wholesale & Retail Trade, Information Technology Services, Leasing & Business Services, and Water Conservancy, Environment, and Public Infrastructure Management. The policy operates through multiple channels: internal mechanisms including direct innovation effect, accelerated green M&As effect as well as digital empowerment effect, and external moderators including marketization level and green finance environment. The findings yield important insights for scholars, policymakers and corporate stakeholders. Full article

We study Portugal’s household electricity retail market after legal liberalisation, quantifying market concentration (Herfindahl–Hirschman Index (HHI) and the four-firm concentration ratio (CR4)), consumer switching, and asymmetric wholesale-to-retail price pass-through. Using monthly data for January 2014–December 2019 (primary sample) and robustness checks for 2008–2022, we compute concentration indices from ERSE supplier shares, analyse switching dynamics, and estimate nonlinear autoregressive distributed lag (NARDL) models that decompose wholesale price changes into positive and negative components. The retail market remains highly concentrated during the primary window (HHI ≈ 6300–6800 using shares expressed as percentages on a 10,000 scale); switching rose after deregulation but stabilised at moderate monthly rates; and long-run pass-through is estimated at β⁺ ≈ 0.55–0.61 for wholesale increases and β⁻ ≈ 0.49 for decreases (Wald tests reject symmetry at conventional levels). Results are robust to alternative concentration metrics, exclusion of 2022, and varied lag orders. Policy implications emphasise tariff simplification, active consumer-activation measures, and regular monitoring of concentration and pass-through metrics. Full article