Search Results (36)

Free trading of distributed energy resources (DERs) is an effective way to enhance local renewable consumption and user-side economic efficiency. Yet unrestricted sharing may threaten operational security. To address this, this paper proposes a voltage-constrained, differentiated resource-sharing framework for active distribution networks (ADNs). The framework maximizes users’ economic benefits and renewable absorption while keeping system voltages within safe limits. A local energy market with prosumers and the distribution network operator (DNO) is established. Prosumers optimize trading decisions considering transaction costs, wheeling charges, and operational costs. Based on this, a generalized Nash bargaining model is developed with two sub-problems: cost optimization under voltage constraints and payment negotiation. The DNO verifies prosumer decisions to ensure system constraints are satisfied. This paper quantifies prosumer heterogeneity by integrating market participation and voltage regulation contributions, and proposes a differentiated bargaining model to improve fairness and efficiency in DER trading. Finally, an ADMM-based distributed algorithm achieves market clearing under AC power flow constraints. Case studies on modified IEEE 33-bus and 123-bus systems validate the method’s effectiveness, the allocation of benefits between producers and consumers is more equitable, and the costs for highly engaged producers and consumers can be reduced by 46.75%. Full article

The unequal status between manufacturers and live-streamers often undermines supply chain profitability and social welfare. However, the “volume guarantee” commission mode, designed to mitigate this issue, has proven ineffective in practice. This paper adopts a Nash bargaining fairness framework to analyze this paradox, incorporating two defining features of live-streaming commerce: the social network effect and the streamer’s cost of purchasing public domain traffic. We develop a dynamic game model involving the platform, manufacturer, streamer, and consumers to examine commission mode selection and supply chain decision-making. Our analysis yields four key findings: (1) Under Nash bargaining fairness, the “volume guarantee” mode is invariably redundant, regardless of who sets the sales threshold. Bargaining power only influences profit distribution via commission rates without distorting optimal product pricing or traffic acquisition decisions. (2) The social network effect boosts product prices, traffic purchases, total profit, and social welfare, with its impact amplified by the streamer’s fanbase size. Thus, collaborating with top-streamers is advantageous for manufacturers. (3) While higher platform traffic costs do not affect the optimal product price, they reduce traffic purchase volume, thereby decreasing supply chain profits and social welfare. (4) To enhance social welfare, platforms can implement differentiated traffic pricing, offering discounts to top-streamers. This study provides critical managerial insights for designing fair contracts and fostering equitable cooperation in live-streaming ecosystems. Full article

Integrated energy systems (IESs) are increasingly being deployed and expanded, which integrate various energy infrastructures to enable flexible conversion and utilization among different energy forms. To facilitate collaboration among operators of varying scales and fully leverage the economic and environmental benefits of multi-integrated energy systems (MIESs), this study develops a peer-to-peer (P2P) energy sharing framework for MIES based on asymmetric Nash bargaining. First, an IoT-based P2P energy sharing architecture for MIES is proposed, which incorporates coordinated electricity–heat–gas multi-energy synergy within IES models. Carbon capture systems (CCS) and power-to-gas (P2G) units are integrated with carbon trading mechanisms to reduce carbon emissions. Then, an MIES energy sharing operational model is established using Nash bargaining theory, subsequently decoupled into two subproblems: alliance benefit maximization and individual IES benefit distribution optimization. For subproblem 2, an asymmetric bargaining method employing natural exponential functions quantifies participant contributions, enabling fair distribution of cooperative benefits. Finally, the alternating direction method of multipliers (ADMM) is employed to solve both subproblems distributively, effectively preserving participant privacy. The effectiveness of the proposed method is verified by case simulation, demonstrating reduced operational costs across all IESs alongside equitable benefit allocation proportional to energy-sharing contributions. Carbon emission amounts are simultaneously reduced. Full article

To address the increasing need for clean energy and efficient resource utilization, this paper aims to provide a cooperative framework and a fair profit allocation mechanism for integrated photovoltaic (PV) and energy storage systems that are shared among different types of users within a regional alliance, including industrial, commercial, and residential users. A cooperative game model is proposed and formulated by a two-level optimization problem: the upper level determines the optimal PV and storage capacities to maximize the alliance’s net profit, while the lower level allocates profits using an improved Nash bargaining approach based on Shapley value. The model simultaneously incorporates different real-world factors such as time-of-use electricity pricing, system life cycle cost, and load diversity. The results demonstrate that coordination between energy storage systems and PV systems can avoid 18% of solar curtailment losses. Compared to independent deployment by individual users, the cooperative sharing model increases the net present value by 8.41%, highlighting improvements in cost-effectiveness, renewable resource utilization, and operational flexibility. Users with higher demand or better load–generation matching gain greater economic returns, which can provide decision-making guidance for the government in formulating differentiated subsidy policies. Full article

Against the backdrop of global low-carbon transition, the integrated development of electricity and carbon markets demands higher efficiency in the optimal operation of virtual power plants (VPPs) and distribution networks, yet conventional trading mechanisms face limitations such as inadequate recognition of differentiated contributions and inequitable benefit allocation. To address these challenges, this paper proposes a collaborative optimal trading mechanism for VPP clusters and distribution networks in an electricity–carbon coupled market environment by first establishing a joint operation framework to systematically coordinate multi-agent interactions, then developing a bi-level optimization model where the upper level formulates peer-to-peer (P2P) trading plans for electrical energy and carbon allowances through cooperative gaming among VPPs while the lower level optimizes distribution network power flow and feeds back the electro-carbon comprehensive price (EACP). By introducing an asymmetric Nash bargaining model for fair benefit distribution and employing the Alternating Direction Method of Multipliers (ADMM) for efficient computation, case studies demonstrate that the proposed method overcomes traditional models’ shortcomings in contribution evaluation and profit allocation, achieving 2794.8 units in cost savings for VPP clusters while enhancing cooperation stability and ensuring secure, economical distribution network operation, thereby providing a universal technical pathway for the synergistic advancement of global electricity and carbon markets. Full article

As an essential platform for aggregating and coordinating distributed energy resources (DERs), the virtual power plant (VPP) has attracted widespread attention in recent years. With the increasing scale of VPPs, energy interaction and sharing among VPP clusters (VPPCs) have become key approaches to improving energy utilization efficiency and reducing operational costs. Therefore, studying the coordinated operation mechanism of VPPCs is of great significance. This paper proposes a two-stage coordinated operation model for VPPCs based on energy interaction to enhance the overall economic performance and coordination of the cluster. In the day-ahead stage, a cooperative operation model based on Nash bargaining theory is constructed. The inherently non-convex and nonlinear problem is decomposed into a cluster-level benefit maximization subproblem and a benefit allocation subproblem. The Alternating Direction Method of Multipliers (ADMM) is employed to achieve distributed optimization, ensuring both the efficiency of coordination and the privacy and decision independence of each VPP. In the intra-day stage, to address the uncertainty in renewable generation and load demand, a real-time pricing mechanism based on the supply–demand ratio is designed. Each VPP performs short-term energy forecasting and submits real-time supply–demand information to the coordination center, which dynamically determines the price for the next trading interval according to the reported imbalance. This pricing mechanism facilitates real-time electricity sharing among VPPs. Finally, numerical case studies validate the effectiveness and practical value of the proposed model in improving both operational efficiency and fairness. Full article

The cooperative interconnection of multi-microgrid systems offers significant advantages in enhancing energy utilization efficiency and economic performance, providing innovative pathways for promoting sustainable development. To establish a fair energy trading mechanism for electricity–hydrogen sharing within multi-energy multi-microgrid (MEMG) systems, this study first analyzes the operational architecture of MEMG energy sharing and establishes a multi-energy coordinated single-microgrid model integrating electricity, heat, natural gas, and hydrogen. To achieve low-carbon operation, carbon capture systems (CCSs) and power-to-gas (P2G) units are incorporated into conventional combined heat and power (CHP) systems. Subsequently, an asymmetric Nash bargaining-based optimization framework is proposed to coordinate the MEMG network, which decomposes the problem into two subproblems: (1) minimizing the total operational cost of MEMG networks, and (2) maximizing payment benefits through fair benefit allocation. Notably, Subproblem 2 employs the energy trading volume of individual microgrids as bargaining power to ensure equitable profit distribution. The improved alternating direction multiplier method (ADMM) is adopted for distributed problem-solving. Experimental results demonstrate that the cost of each MG decreased by 5894.14, 3672.44, and 2806.64 CNY, while the total cost of the MEMG network decreased by 12,431.22 CNY. Additionally, the carbon emission reduction ratios were 2.84%, 2.77%, and 5.51% for each MG and 11.12% for the MEMG network. 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

Network flow task management involves the efficient allocation and scheduling of data flow tasks within dynamic satellite networks, aiming to effectively address frequent changes in network topology and dynamic traffic fluctuations. Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. However, these approaches often depend on extensive historical data for training, making real-time adaptation to rapidly changing network topologies and traffic patterns challenging in dynamic satellite environments. Additionally, their high computational complexity and slow convergence rates hinder their efficiency in large-scale networks. To address these issues, this paper proposes a collaborative optimization framework based on Coding Multi-Path Theory (CMPT). The framework utilizes a Nash bargaining game model to simulate resource competition among the different participants, ensuring fair resource distribution and load balancing. It also integrates real-time network state monitoring with optimization algorithms, within a multi-path scheduling strategy, enabling the dynamic selection of optimal transmission paths to accommodate frequent network topology changes and traffic variations. Experimental results indicate that the proposed method reduced resource allocation task execution time by at least 18.03% compared to traditional methods and enhanced task scheduling efficiency by at least 14.01%. Although CMPT exhibited a slightly higher task latency on certain small-scale datasets compared to some baseline algorithms, its performance remains exceptional in large-scale and high-dimensional scenarios. Full article

With economic development and shifting consumption trends, branding has become an important way to improve the efficiency of poverty alleviation supply chains (PASCs) in practice. However, academic research on this topic is limited. To fill this gap in the literature, we constructed a differential game of a PASC that examines how to build a sustainable poverty reduction model through branding, considering government subsidies and supplier’s Nash bargaining fairness concerns. Our findings show the following: (1) Government subsidies can improve the decision-making level and channel efficiency of leading enterprises (E) and poor suppliers (F). Government subsidies are necessary for a PASC to establish a sustainable poverty alleviation mechanism. (2) F’s Nash bargaining fairness concerns only reduce their level of production effort but do not affect the brand construction and corporate social responsibility levels of E. (3) As F’s bargaining power increases, Nash bargaining fairness concerns have a more significant effect on the PASC’s performance. While F’s fairness concerns can enhance their utility to some extent, it ultimately leads to more significant profit losses for both parties. (4) The proposed mixed cost-sharing and revenue-sharing contract can effectively align members’ incentives, enhancing profitability for both parties. Full article

In service alliances, where multiple service providers collaborate to complete service transactions, the equitable allocation of profits based on their respective contributions and risk-bearing capacities is paramount. This paper introduces an optimized profit allocation game model that integrates risk considerations into the Nash bargaining framework. Initially, the study established a service alliance transaction model that considered the interactions among multiple participants, providing a robust theoretical foundation for cooperation. Subsequently, the concept of marginal risk was introduced, and a unique calculation method based on the Shapley value was devised to quantify risk contributions. Finally, an improved Nash bargaining model was proposed, which introduced a risk adjustment factor, explicitly addressing the impact of each participant’s risk on profit allocation. Through computational cases and result analyses, this model demonstrated its ability to balance profit and risk and to optimize outcomes for all participants, and it validated the fairness and rationality of the proposed allocation method. Full article

Energy Communities (ECs) are aggregations of users that cooperate to achieve economic benefits by sharing energy instead of operating individually in the so-called “disagreement” case. As there is no unique notion of fairness for the cost/profit allocation of ECs, this paper aims to identify an allocation method that allows for an appropriate weighting of both the interests of an EC as a whole and those of all its members. The novelty is in comparing different optimization approaches and cooperative allocation criteria, satisfying different notions of fairness, to assess which one may be best suited for an EC. Thus, a cooperative model is used to optimize the operation of an EC that includes two consumers and two solar PV prosumers. The model is solved by the “Social Welfare” approach to maximizing the total “incremental” economic benefit (i.e., cost saving and/or profit increase) and by the “Nash Bargaining” approach to simultaneously maximize the total and individual incremental economic benefits, with respect to the “disagreement” case. Since the “Social Welfare” approach could lead to an unbalanced benefit distribution, the Shapley value and Nucleolus criteria are applied to re-distribute the total incremental economic benefit, leading to higher annual cost savings for consumers with lower electricity demand. Compared to “Social Welfare” without re-distribution, the Nash Bargaining distributes 39–49% and 9–17% higher annual cost savings to consumers with lower demand and to prosumers promoting the energy sharing within the EC, respectively. However, total annual cost savings drop by a maximum of 5.5%, which is the “Price of Fairness”. Full article

As important methods of ecofriendly transportation, the supply chain coordination of new energy vehicles (NEVs) is an important issue in the field of sustainability. This study constructs a Stackelberg game composed of a power battery supplier and an NEV manufacturer. To better describe the coordination relationship in the NEV supply chain, we introduce the Nash bargaining framework into the fairness concern preference utility function. Through a comprehensive discussion of shareholding ratios and external environment factors, we discover that the traditional shareholding strategy fails to coordinate the NEV supply chain effectively, as enterprises seek to avoid losing management control, which occurs when excessive shares are held by others. In this context, this study proposes a novel industry–university–research (IUR) shareholding strategy, which can more easily achieve supply chain coordination and improve social welfare. In particular, this study reveals the superiority of the novel strategy in eliminating the double-marginal effect caused by high fairness concern preference among NEV enterprises. Based on these facts, we provide enterprises with optimal strategies under different conditions and offer a government-optimal subsidy to maximize the social welfare function. Full article

In response to climate change, the proportion of renewable energy penetration is increasing daily. However, there is a lack of flexible energy transfer mechanisms. The optimization effect of low-carbon economic dispatch in a single park is limited. In the context of the sharing economy, this study proposes a research method for multi-park electricity sharing and benefit allocation based on carbon credit trading. Firstly, a framework for multi-park system operation is constructed, and an energy hub model is established for the electrical, cooling, and heating interconnections with various energy conversions. Secondly, a park carbon emission reduction trading model is established based on the carbon credit mechanism, further forming an optimal economic and environmental dispatch strategy for multi-park electricity sharing. Matlab+Gurobi is used for solving. Then, based on asymmetric Nash bargaining, the comprehensive contribution rate of each park is calculated by considering their energy contribution and carbon emission reduction contribution, thereby achieving a fair distribution of cooperation benefits among multiple parks. The results show that the proposed method can effectively reduce the overall operational cost of multiple parks and decrease carbon emissions, and the benefit allocation strategy used is fair and reasonable, effectively motivating the construction of new energy in parks and encouraging active participation in cooperative operations by all parks. Full article

The Global Malmquist Productivity Index (GMPI) stands as an evolution of the Malmquist Productivity Index (MPI), emphasizing global technology to incorporate all-time versions of Decision-Making Units (DMUs). This paper introduces a novel approach, integrating the Nash Bargaining Game model with GMPI to establish a Cross-Productivity Index. Our primary objective is to develop a comprehensive framework utilizing the Nash Bargaining Game model to derive equitable common weights for different time versions of DMUs. These weights serve as a fundamental component for cross-evaluation based on GMPI, facilitating a holistic assessment of DMU performance over varying time periods. The proposed index is designed with essential properties: feasibility, non-arbitrariness concerning the base time period, technological consistency across periods, and weight uniformity for GMPI calculations between two-time versions of a unit. This research amalgamates cross-evaluation and global technology while employing geometric averages to derive a conclusive cross-productivity index. The core motivation behind this methodology is to establish a reliable and fair means of evaluating DMU performance, integrating insights from Nash Bargaining Game principles and GMPI. This paper elucidates the rationale behind merging the Nash Bargaining Game model with GMPI and outlines the objectives to provide a comprehensive Cross-Productivity Index, aiming to enhance the robustness and reliability of productivity assessments across varied time frames. Full article