Search Results (26)

With the rapid development of energy transformation, the proportion of new energy is increasing, and the efficient trading mechanism of multi-microgrids can realize energy sharing to improve the consumption rate of new energy. A distributed multi-microgrid cooperative energy sharing strategy is proposed based on Nash bargaining. Firstly, by comprehensively considering the adjustable heat-to-electrical ratio, ladder-type positive and negative carbon trading, peak–valley electricity price and demand response, a multi-microgrid system with wind–solar-storage-load and combined heat and power is constructed. Then, a multi-microgrid cooperative game optimization framework is established based on Nash bargaining, and the complex nonlinear problem is decomposed into two stages to be solved. In the first stage, the cost minimization problem of multi-microgrids is solved based on the alternating direction multiplier method to maximize consumption rate and protect privacy. In the second stage, through the established contribution quantification model, Nash bargaining theory is used to fairly distribute the benefits of cooperation. The simulation results of three typical microgrids verify that the proposed strategy has good convergence properties and computational efficiency. Compared with the independent operation, the proposed strategy reduces the cost by 41% and the carbon emission by 18,490 kg, thus realizing low-carbon operation and optimal economic dispatch. Meanwhile, the power supply pressure of the main grid is reduced through energy interaction, thus improving the utilization rate of renewable energy. 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

In distribution networks, energy storage serves as a crucial means to mitigate power fluctuations from renewable energy sources. However, due to its high cost, energy storage remains a resource whose large-scale adoption in power systems faces significant challenges. In recent years, the emergence of shared energy storage business models has provided new opportunities for the efficient operation of multi-distribution networks. Nevertheless, distribution network operators and shared energy storage operators belong to different stakeholders, and traditional centralized scheduling strategies suffer from issues such as privacy leakage and overly conservative decision-making. To address these challenges, this paper proposes a Nash bargaining game-based optimal energy management and trading strategy for multi-distribution networks with shared energy storage. First, we establish optimal scheduling models for active distribution networks (ADNs) and shared energy storage operators, respectively, and then develop a cooperative scheduling model aimed at maximizing collaborative benefits. The interactive variables—power exchange and electricity prices between distribution networks and shared energy storage operators—are iteratively solved using the Alternating Direction Method of Multipliers (ADMM). Finally, case studies based on modified IEEE-33 test systems validate the effectiveness and feasibility of the proposed method. The results demonstrate that the presented approach significantly outperforms conventional centralized optimization and distributed robust techniques, achieving a maximum improvement of 3.6% in renewable energy utilization efficiency and an 11.2% reduction in operational expenses. While maintaining computational performance on par with centralized methods, it effectively addresses data privacy concerns. Furthermore, the proposed strategy enables a substantial decrease in load curtailment, with reductions reaching as high as 63.7%. Full article

Terahertz metamaterial dual-band absorbers are used for multi-target detection and high-sensitivity sensing in complex environments by enhancing information that reflects differences in the measured substances. Traditional design processes are complex and time-consuming. Machine learning-based methods, such as neural networks and deep learning, require a large number of simulations to gather training samples. Existing design methods based on single-objective optimization often result in uneven multi-objective optimization, which restricts practical applications. In this study, we developed a metamaterial absorber featuring a circular split-ring resonator with four gaps nested in a “卍” structure and used the Multi-Objective Firefly Algorithm based on Multiple Cooperative Strategies to achieve fast optimization of the absorber’s structural parameters. A comparison revealed that our approach requires fewer iterations than the Multi-Objective Particle Swarm Optimization and reduces design time by nearly half. The absorber designed using this method exhibited two resonant peaks at 0.607 THz and 0.936 THz, with absorptivity exceeding 99%, indicating near-perfect absorption and quality factors of 31.42 and 30.08, respectively. Additionally, we validated the absorber’s wave-absorbing mechanism by applying impedance-matching theory. Finally, we elucidated the resonance-peak formation mechanism of the absorber based on the surface current and electric-field distribution at the resonance frequencies. These results confirmed that the proposed dual-band metamaterial absorber design is efficient, representing a significant step toward the development of metamaterial devices. 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

Existing resource allocation models for multi-hop relay networks lack the systematic joint optimization of relay selection and power allocation. Therefore, a multi-hop relay network model based on a joint optimization strategy is proposed, aimed at realizing the energy efficiency optimization of the system through the cooperative optimization of relay selection and power allocation. The proposed model not only takes into account the node transmitting power and communication link but also combines the specified system quality of service requirements. On this basis, the FD-Dink energy efficiency optimization algorithm is proposed. By integrating an enhanced D* algorithm with a forward maximum signal-to-noise ratio (FMSNR) and the Dinkelbach–Lagrange multiplier method, the proposed strategy resolves relay selection and power control problems in a coordinated framework so as to determine the optimal energy efficiency communication link of a multi-hop relay network model. Case studies demonstrate that this joint optimization strategy significantly improves the system energy efficiency of the multi-hop relay network and shows superiority in dynamic path planning and global power allocation, offering significant theoretical and practical implications. Full article

Under the goal of “dual carbon”, the power market and carbon market are developing synergistically, which is strongly promoting the transformation of the power system in a clean and low-carbon direction. In order to realise the synergistic optimisation of multi-virtual power plants, economic and low-carbon operation, and the reasonable distribution of revenues, this paper proposes a multi-VPP power–carbon sharing operation optimisation strategy considering multiple uncertainties. Firstly, a cost model for each VPP power–carbon sharing considering the uncertainties of market electricity price and new energy output is established. Secondly, a multi-VPP power–carbon sharing operation optimisation model is established based on the Nash negotiation theory, which is then decomposed into a multi-VPP coalition cost minimisation subproblem and a revenue allocation subproblem based on asymmetric bargaining. Thirdly, the variable penalty parameter alternating directional multiplier method is used for the solution. Finally, an asymmetric bargaining method is proposed to quantify the contribution size of each participant with a nonlinear energy mapping function, and the VPPs negotiate with each other regarding the bargaining power of their electricity–carbon contribution size in the co-operation, so as to ensure a fair distribution of co-operation benefits and thus to motivate and maintain a long-term and stable co-operative relationship among the subjects. Example analyses show that the method proposed in this paper can significantly increase the revenue level of each VPP and reduce carbon emissions and, at the same time, improve the ability of VPPs to cope with uncertain risks and achieve a fair and reasonable distribution of the benefits of VPPs. Full article

This study presents a comprehensive comparative analysis of the operational strategies for multi-microgrid systems that integrate battery energy storage systems and electric vehicles. The analyzed strategies include individual operation, community-based operation, a cooperative game-theoretic method, and the alternating direction method of multipliers for multi-microgrid systems. The operation of multi-microgrid systems that incorporate electric vehicles presents challenges related to coordination, privacy, and fairness. Mathematical models for each strategy are developed and evaluated using annual simulations with real-world data. Individual operation offers simplicity but incurs higher costs due to the absence of power sharing among microgrids and limited optimization of battery usage. However, individual optimization reduces the multi-microgrid system cost by 47.5% when compared to the base case with no solar PV or BESS and without optimization. Community-based operation enables power sharing, reducing the net cost of the multi-microgrid system by approximately 7%, as compared to individual operation, but requires full data transparency, raising privacy concerns. Game theory ensures fair benefit allocation, allowing some microgrids to achieve cost reductions of up to 13% through enhanced cooperation and shared use of energy storage assets. The alternating direction method of multipliers achieves a reduction in the electricity costs of each microgrid by 6–7%. It balances privacy and performance without extensive data sharing while effectively utilizing energy storage. The findings highlight the trade-offs between cost efficiency, fairness, privacy, and computational efficiency, offering insights into optimizing multi-microgrid operations that incorporate advanced energy storage solutions. Full article

With the advancement of global digital transformation, data trading has become a pivotal element in value circulation and innovation among enterprises. In particular, pricing strategies in the industrial chain’s data trading process critically influence the cooperation and market competitiveness of upstream and downstream enterprises. To address this issue, this study develops a Business-to-Business data transaction pricing model tailored to a single industry chain. The model incorporates factors such as data scarcity, encryption protection efforts, and market demand dynamics. By employing a Stackelberg dynamic model, the study systematically examines the pricing strategies of upstream and downstream enterprises under various incentive mechanisms and evaluates the impacts of encryption protection efforts and incentive mechanism coefficients on the profitability of the industry chain. The experimental results reveal that introducing incentive mechanisms for downstream enterprises modestly increases the profits of both upstream and downstream entities. Meanwhile, incentivizing upstream enterprises yields a multiplier effect, significantly boosting their profits while causing a slight decline in the profitability of downstream enterprises. Full article

To improve the energy utilization rate and realize the low-carbon emission of a park integrated energy system (PIES), this paper proposes an optimal operation strategy for multiple PIESs. Firstly, the electrical power cooperative trading framework of multiple PIESs is constructed. Secondly, the hydrogen blending mechanism and carbon capture system and power-to-gas system joint operation model are introduced to establish the model of each PIES. Then, based on the Nash bargaining game theory, a multi-PIES cooperative trading and operation model with electrical power cooperative trading is constructed. Then, the alternating direction method of multipliers algorithm is used to solve the two subproblems. Finally, case studies analysis based on scene analysis is performed. The results show that the cooperative operation model reduces the total cost of a PIES more effectively compared with independent operation. Meanwhile, the efficient utilization and production of hydrogen are the keys to achieve carbon reduction and an efficiency increase in a PIES. Full article

With the growing global environmental concerns and the push for carbon neutrality, rural multi-regional integrated energy systems (IESs) face challenges related to low energy efficiency, high carbon emissions, and the transition to cleaner energy sources. This paper proposes a cooperative game-based low-carbon economic dispatch strategy for rural IESs, integrating carbon trading mechanisms. A novel multi-regional IESs architecture is developed to exploit the synergy between photovoltaic (PV) and biomass energy systems. The proposed model described the anaerobic fermentation heat loads, incorporates variable-temperature fermentation, and employs a Nash bargaining model solved via the Alternating Direction Method of Multipliers (ADMM) to optimize cooperation while preserving stakeholder privacy. Simulation results show that the proposed strategy reduces total operating costs by 16.9% and carbon emissions by 7.5%, validating its effectiveness in enhancing efficiency and sustainability in rural energy systems. Full article

To improve the regulatory capacity of distributed resources within the park and enhance the flexibility of market transactions, this paper introduces a distributed dispatch and profit allocation method grounded in cooperative game theory and the generalized Nash bargaining framework. Initially, models for individual park equipment are established. Subsequently, a distributed dispatch model is constructed, followed by the development of a profit allocation strategy based on contribution levels, using the generalized Nash bargaining method. The model is solved using the alternating direction method of multipliers. The results show that the proposed approach achieves fast convergence, optimizes resource sharing and mutual support within the park, lowers operational costs, ensures a fairer distribution of profits, and promotes increased cooperation among park entities. Full article

Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method. Full article

Clinical reasoning is an essential component of nursing. It has emerged as a concept that integrates the core competencies of quality and safety education for nurses. In cooperation with five European partners, Instituto Politécnico de Setúbal (IPS) realized the “Clinical Reasoning in Nursing and Midwifery Education and Practice” project as part of the Erasmus+ project. As a partner, our team designed a multiplier event—the student training course. The aim of this report is to describe the construction and development of this clinical reasoning training course for nursing students. We outline the pedagogical approach of an undergraduate training course on clinical reasoning in 2023, which we separated into four stages: (i) welcoming, (ii) knowledge exploration, (iii) pedagogical learning, and (iv) sharing experience. This paper presents the learning outcomes of the collaborative reflection on and integration of the clinical reasoning concept among nursing students. This educational experience fostered reflection and discussion within the teaching team of the nursing department regarding the concept, models, and teaching/learning methods for clinical reasoning, with the explicit inclusion of clinical reasoning content in the nursing curriculum. We highlight the importance of implementing long-term pedagogical strategies in nursing education. Full article

In the context of “dual carbon”, restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction. Full article