Search Results (153)

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

As electric multiple unit (EMU) operations and maintenance demands have expanded, spare parts supply chain management has become increasingly crucial. This study emphasizes the supply challenges of EMU spare parts, including inadequate minimum inventory levels and prolonged response times. Redesigning the OEM–railway bureau vendor-managed inventory (VMI) model contract incentive and penalty system is the key goal. Connecting the spare parts supply system with its characteristics yields a game theory model. This study analyzes and compares the equilibrium strategies and profits of supply chain members under different mechanisms for managing critical spare parts. The findings demonstrate that mechanism contracts can enhance supply chain performance in a Pareto-improving manner. An in-depth analysis of downtime loss costs, procurement challenges, and order losses reveals their effects on supply chain coordination and profit allocation, providing railway bureaus and OEMs with a theoretical framework for supply chain decision-making. This study offers theoretical justification and a framework for decision-making on cooperation between OEMs and railroad bureaus in the management of spare parts supply chains, particularly for extensive EMU operations. Full article

In light of the current challenges posed by complex multi-agent interactions and competing interests in integrated energy systems, an economic optimization operation model is proposed. This model is based on a two-layer game comprising a one-master–many-slave structure consisting of an energy retailer, energy suppliers, and a user aggregator. Additionally, it considers energy suppliers to be engaged in a non-cooperative game. The model also incorporates a carbon trading mechanism between the energy retailer and energy suppliers, considers integrated demand response at the user level, and categorizes users in the community according to their energy use characteristics. Finally, the improved differential evolutionary algorithm combined with the CPLEX solver (v12.6) is used to solve the proposed model. The effectiveness of the proposed model in enhancing the benefits of each agent as well as reducing carbon emissions is verified through example analyses. The results demonstrate that the implementation of non-cooperative game strategies among ESs can enhance the profitability of ES1 and ES2 by 27.83% and 18.67%, respectively. Furthermore, the implementation of user classification can enhance user-level benefits by up to 39.51%. Full article

In reality, there is often information asymmetry between upstream and downstream enterprises in a closed-loop supply chain (CLSC) system, which can have a profound impact on the decisions of member enterprises and the operation of the system. Under asymmetric market demand information, this study examines CSR input and recycling decision making in CLSC. Four decision-making models were developed for CLSC, and the effects of consumer sensitivity to CSR input and demand information asymmetry on CLSC optimization were studied. The results indicate that higher consumer sensitivity to CSR input enhances both CSR levels and recycling rates, benefiting both manufacturer and retailer by increasing profits. In terms of increasing CSR levels, the manufacturer achieves the best results when independently managing CSR input and recycling. However, for improving recycling rates and market demand, the retailer is more effective when responsible for CSR input, with the manufacturer handling recycling. Additionally, demand information asymmetry reduces the manufacturer’s profit but may not affect the retailer’s profit. The retailer–manufacturer cooperation model proves more beneficial for overall CLSC system performance compared to information symmetry. Full article

Global demands for sustainable energy and advanced therapeutics necessitate innovative interdisciplinary solutions. Integrated biorefining emerges as a strategic response, enabling the co-production of biofuels and pharmaceutical compounds through biomass valorization. This integrated model holds promise in enhancing resource utilization efficiency while ensuring economic viability. Our critical review methodically evaluates seven pivotal methodologies: seven key strategies: microbial metabolites, synthetic biology platforms, biorefinery waste extraction, nanocatalysts, computer-aided design, extremophiles, and plant secondary metabolites. Through systematic integration of these approaches, we reveal pivotal synergies and potential technological innovations that can propel multi-product biorefinery systems. Persistent challenges, particularly in reconciling complex metabolic flux balancing with regulatory compliance requirements, are analyzed. Nevertheless, advancements in systems biology, next-generation bioprocess engineering, and artificial intelligence-enhanced computational modeling present viable pathways for overcoming these obstacles. This comprehensive analysis substantiates the transformative capacity of integrated biorefining in establishing a circular bioeconomy framework, while underscoring the imperative of transdisciplinary cooperation to address existing technical and policy constraints. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

In order to improve the ability to suppress unbalanced voltage in bipolar DC microgrids, a comprehensive power regulation control of a novel shared energy storage system is proposed for a multi-scenario bipolar DC microgrid. The novel shared energy storage system is composed of an electric spring (ES) with a full-bridge DC/DC converter and non-critical load (NCL) in series, considering demand-side response. The proposed comprehensive power regulation control can enable the bipolar DC microgrid to deal with various scenarios. When operating in stand-alone mode, the unbalanced voltage caused by greater unbalanced power can still be suppressed under the proposed control of the shared energy storage. In case of distributed energy storage (DES) failure on the source side, the shared energy storage can realize DC voltage regulation and maintain system operation by reducing NCL power. In grid-connected operation, the shared energy storage can actively cooperate with the power dispatching of the utility grid for storage reduction of DES on the source side. Thus, the reliability and resilience of the bipolar microgrid have been improved. Finally, to verify the effectiveness of the proposed control strategy, hardware-in-the-loop experimental results are presented in this paper. Full article

In 2021, Winter Storm Uri severely affected several Texan agricultural sectors, including dairy production. To understand how dairy producers experienced this extreme weather event, this qualitative phenomenological case study explored perceptions of preparedness, coping strategies, and information needs and preferences for dealing with extreme weather events among dairy producers in Texas, conducting individual semi-structured interviews. The findings indicated that farmers felt unprepared to deal with extreme weather events and suffered significant economic losses due to this lack of preparedness. In response to winter storm Uri, dairy farmers modified traditional operations and management practices to mitigate negative impacts on farm labor, infrastructure, and herds. Our results, along with the existing literature on communication for extreme weather event management, highlighted that dairy farmers do not receive adequate information to effectively prevent and cope with similar occurrences in the future. Consequently, this study recommends exploring effective strategies to help agricultural producers develop plans to manage the effects of extreme weather events. Additionally, it integrates place-based, pluralistic, and demand-driven approaches to identify the best communication practices, enhance timely information dissemination on extreme weather, and strengthen the technical capacities of public and private entities, including Cooperative Extension Systems, as trusted resources for agricultural producers. Full article

As environmental challenges become increasingly pressing, companies are integrating low-carbon innovations into supply chain management to achieve economic performance while promoting environmental sustainability and social responsibility. This study explores how low-carbon innovation in supply chains can be driven by consumer orientation and market-driven strategies, contributing to sustainable development. Using Stackelberg game theory, the study develops centralized and decentralized decision-making models and solves them through differential game methods. Numerical simulations are employed to analyze the impact of consumer preferences for low-carbon products and market strategies on supply chain decisions and overall profitability. The results show that consumer demand for low-carbon products plays a crucial role in driving low-carbon innovation within supply chains. Market strategies, particularly their sensitivity to consumer preferences, significantly influence decision-making processes. Further analysis reveals that the centralized decision-making model offers greater advantages in resource optimization and responsiveness to market shifts, while the decentralized model allows independent decision-making by supply chain participants, balancing competition and co-operation. This enables firms to achieve both economic benefits and reduce their environmental footprint, thereby contributing to sustainable development. This research highlights the importance of aligning consumer demand with market strategies to foster low-carbon innovation. The findings provide valuable theoretical insights and practical strategies to help supply chain companies enhance their competitiveness and contribute to the sustainable development of global supply chains. Full article

Recently, electric distribution grids supply not only electric loads but also heating and cooling loads simultaneously to increase the efficiency of the system and reduce the emission of greenhouse gases. An energy management system (EMS) to reduce the combined total expense including environmental damage cost of the combined cooling, heating, and power (CCHP) smart distribution grids in a cooperative framework is proposed in this paper. The entire problem is modelled as a unit commitment interval mixed integer quadratic program (UCIMIQP). The UC is developed to respond to the operation of the electric, heating, and cooling systems and takes into consideration the exchange of energy between these systems. In addition, the demand response (DR) is incorporated with the optimization problem as a decision variable to shave the peak load and reduce the total system cost. The environmental damage is converted to expense, and the entire combined problem is converted to a unified function that is possible to solve in one step, where this is suitable for online operation. Furthermore, a set of realistic constraints is considered to make the approach close to a real scenario. To verify the effectiveness and robustness of the proposed model, the analysis is applied to the distribution grids, which include electrical, heating, and cooling systems, where these systems operated cooperatively. The interaction between these systems makes the operation more flexible and economical. The results show that the total cost is reduced through an exchange in energy between the systems. Additionally, the consideration of the demand response reduces the maximum load and decreases the total cost. Full article

Megaproject construction endeavors and technological innovation activities, led by industry–university–research (IUR) collaboration, demonstrate marked disparities in value orientations, implementing entities, and constituent components. These discrepancies lead to a mismatch between innovation demands and actual activities, as well as insufficient innovation motivation among construction entities, subsequently impacting innovation effectiveness and the commercialization of outcomes and failing to adequately support engineering construction needs. In response to this predicament, the academic community widely acknowledges IUR collaborative innovation as a solution. This research integrates fundamental theoretical analysis with a multi-case study approach and systematically dissects the distinctive features at the micro, meso, and macro levels, grounded in the collaborative innovation practices of IUR in three iconic railway engineering projects in China. Subsequently, it unravels the inherent operational mechanics of the IUR collaborative innovation system within large-scale projects. Specifically, at the micro level, the profound engagement of governments and project owners fosters a solid supportive environment and collaborative platform for IUR collaboration, while past successful cooperation experiences among key innovation entities enhance their technological and knowledge interactions. At the meso level, shared industry cognitions and values, hierarchical organizational structures, flexible institutional designs, and resource allocation strategies based on balancing risks and benefits collectively constitute the supporting system for megaproject collaborative innovation. At the macro level, the tight integration of the innovation chain and industrial chain promotes the formation of an open cooperation ecosystem, ensuring the continuity and systematic nature of innovation activities and accelerating the rapid commercialization and efficient utilization of innovation outcomes. This study not only enriches the theoretical connotations of IUR collaborative innovation in the context of major engineering projects but also provides theoretical foundations for strategy formulation and management practices for major project managers, holding significant value in guiding the innovation management of future major engineering projects. Full article

As the global demand for minerals critical to clean energy and technological innovation continues to rise, the sustainable exploitation of mineral resources in high-altitude regions becomes increasingly essential for global sustainable development. Employing SWOT analysis, deep learning, and heatmap techniques, this study delves into the mineral resource policies of China, the United States, Canada, and Chile, assessing their alignment with and impact on Sustainable Development Goals (SDGs). Despite distinct policy frameworks, a shared focus on technological innovation and environmental sustainability is evident. China’s strategic resource allocation and stringent regulations drive a green, low-carbon shift, aligning with SDG 13 (Climate Action). The United States fosters SDG 9 (Industry, Innovation, and Infrastructure) through market-driven technological advancements. Canada’s collaborative approach, emphasizing indigenous rights, underpins SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production). Chile’s national governance and international collaboration mix bolsters the mining industry’s efficiency and sustainability, supporting SDG 7 (Affordable and Clean Energy). The study underscores a trend toward sustainable practices in mineral resource management and stresses the critical need for international cooperation. The study advocates for global collaboration and sharing of green mining technologies to accelerate the industry’s transition to a sustainable and responsible future and boost SDG achievements worldwide. Full article

Efficiently reporting soil-specific information is of key importance for plant growth but can be quite demanding as well. Indeed, it may require expensive digitizers, subscriptions to services for communication links between each sensor and the cloud, and the incorporation of power-hungry elements. Added to this, soil sensors may vary drastically, e.g., in terms of power characteristics, response times, or interfacing options. The need for improved energy autonomy increases reporting complexity, as it presupposes that the participating components will enter a low-power (sleep) state when not in action. Furthermore, the IoT nodes hosting the sensing instruments should be able to work unattended for long periods under varying environmental conditions. In response to the aforementioned physical and technical challenges, this work highlights the details behind the cooperation of a cost-effective microprocessor equipped with a radio transceiver and some simple and widely available electronic components to form nodes that can host a diverse set of soil sensors and deliver reliable data in satisfactory ranges. The sensitivity and power efficiency of the LoRa protocol make it ideal for rural agri-field use; in the meantime, optimized action/sleep management, along with tiny solar panels, guarantee sustainable operation. The proposed system was tested utilizing various typical soil instruments, and its range coverage, consumption, and measurement quality were thoroughly evaluated under different installation settings, thus providing guidance for similar implementations and indicating its suitability for a wide set of monitoring applications. Full article

The increased integration of renewable energy sources (RESs), such as photovoltaic and wind turbine systems, in microgrids poses significant challenges due to fluctuating weather conditions and load demands. To address these challenges, this study introduces an innovative approach that combines Unscented Transformation (UT) with the Enhanced Cheetah Optimization Algorithm (ECOA) for optimal microgrid management. UT, a robust statistical technique, models nonlinear uncertainties effectively by leveraging sigma points, facilitating accurate decision-making despite variable renewable generation and load conditions. The ECOA, inspired by the adaptive hunting behaviors of cheetahs, is enhanced with stochastic leaps, adaptive chase mechanisms, and cooperative strategies to prevent premature convergence, enabling improved exploration and optimization for unbalanced three-phase distribution networks. This integrated UT-ECOA approach enables simultaneous optimization of continuous and discrete decision variables in the microgrid, efficiently handling uncertainty within RESs and load demands. Results demonstrate that the proposed model significantly improves microgrid performance, achieving a 10% reduction in voltage deviation, a 10.63% decrease in power losses, and an 83.32% reduction in operational costs, especially when demand response (DR) is implemented. These findings validate the model’s efficacy in enhancing microgrid reliability and efficiency, positioning it as a viable solution for optimized performance under uncertain renewable inputs. Full article