Search Results (47)

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency. Full article

In order to improve the trading efficiency of virtual power plants (VPPs) participating in the market of multi-type auxiliary services under the gaming environment, an initial trading price setting method based on the information of VPPs’ response characteristics and real-time supply and demand changes is proposed to accelerate the convergence speed of the game. Firstly, a master–slave game trading model is established based on the reference auxiliary service pricing, which consists of a tariff coefficient and a basic tariff. Secondly, the tariff coefficient model is constructed based on response information, including response rate, quality, and reliability. Again, the basic tariff model is constructed based on the real-time supply and demand situation and the real-time grid tariff. Finally, the effectiveness of the proposed method in accelerating the convergence speed of the game is verified by analyzing 12 VPPs under the three auxiliary service scenarios of peaking, frequency regulation, and reserve. Full article

A double-layer robust optimization method for capacity configuration of shared energy storage considering cluster leasing of wind farms in a market environment is proposed based on the autonomy and profitability of shared energy storage. The feasibility of the leasing model of shared energy storage in the current market environment in China is discussed, and a commercial operation model for shared energy storage to provide leasing services and participate in spot market transactions is proposed. A robust optimization model of a master-–slave game for the capacity configuration of shared energy storage is constructed, considering output uncertainties of wind-driven generators and spot prices at multiple time scales. The upper layer of the model aims to minimize the annual cost of shared energy storage and determines the leasing prices and capacity-planning schemes for each period of shared energy storage in the scenario of an interactive game of wind farm clusters. The lower level of the model aims to minimize the assessment cost of the wind farm cluster and updates the leasing capacity for each time period by utilizing the leasing prices and the leasing demand of the wind turbine output power in the worst scenario. By comparing and analyzing multiple scenarios, the master–slave-game-formed lease improves the shared-storage lease benefit by $1.46 million compared to the fixed tariff, and the multi-timescale uncertainty promotes the shared-storage cost-effectiveness to be reduced by 8.7%, while the configuration result is more robust, providing new ideas for optimizing the capacity configuration of shared energy storage in multiple application scenarios. Full article

Rapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key challenge to efficiently consume renewable energy sources and guarantee the reliable operation of the power system. In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. Based on the case study of wind power, photovoltaic, and wind–solar complementary data in Jilin Province, the results show that the strategy significantly improves the rate of renewable energy consumption (up to 97.88%) and reduces the cost of power purchase by EVA (an average saving of RMB 0.04/kWh), realizing a win–win situation for all parties. The study provides theoretical support for the synergistic optimization of the power system and renewable energy and is of great practical significance for the large-scale application of electric vehicles and new energy consumption. Full article

Against the backdrop of global climate change and sustainable development, carbon emissions within transnational closed-loop supply chains have become a critical area of research. This paper utilizes a Stackelberg game model to analyze the relationship between a single export manufacturer and an import retailer. The study investigates the optimal solutions of the supply chain model—wholesale price, retail price, sales volume, and profit—across three consumer preference scenarios: no obvious preference, pure green preference, and pure new preference. Furthermore, this paper examines the impact of carbon emissions per unit of product on supply chain decision-making under two scenarios: with and without carbon trading. Carbon trading, which significantly increases unit costs, exerts a profound influence on the strategic decisions of both manufacturers and retailers. In addition, this paper incorporates carbon tariffs and taxes into its analysis, providing a theoretical foundation for governments and policymakers to promote sustainable production and consumption practices. The validity of the model is confirmed through numerical simulations, which reveal that under pure green and pure new preference scenarios, original equipment manufacturers (OEMs) are more inclined to invest in emissions reduction to minimize tariff costs. In contrast, under no obvious preference scenarios, OEMs are more likely to adjust product portfolios to evade carbon tariffs. This research advances the understanding of low-carbon production strategies in transnational supply chains, offering both theoretical insights and practical guidance for balancing economic and environmental objectives. Full article

To investigate the dynamic complexity of chain-to-chain output decisions in a closed-loop supply chain system of cross-border e-commerce (CBEC), this study decomposes the system into four product–market (PM) chains, based on the e-commerce platform’s information-sharing strategy and the manufacturer’s selected logistics mode (direct mail or bonded warehouse). By combining game theory with complex systems theory, discrete dynamic models for output competition among PM chains under four scenarios are constructed. The Nash equilibrium solution and stability conditions of the models are derived according to the principles of nonlinear dynamics. The stability of the model under the four scenarios, as well as the impacts of the initial output level and comprehensive tax rates on the stability and stability control of the system, are analyzed using numerical simulation methods. Our findings suggest that maintaining system stability requires controlling the initial output levels, the output adjustment speeds, and tariff rates to remain within specific thresholds. When these thresholds are exceeded, the entropy value of the model increases, and the system outputs decisions to enter a chaotic or uncontrollable state via period-doubling bifurcations. When the output adjustment speed of the four PM chains is high, the direct-mail logistics mode exhibits greater stability. Furthermore, under increased tariff rates for CBEC, the bonded warehouse mode has a stronger ability to maintain stability in system output decisions. Conversely, when the general import tax rate increases, the direct-mail mode demonstrates better stability. Regardless of the logistics mode, the information-sharing strategy can enhance the stability of system output decisions, while increased e-commerce platform commission rates tend to reduce stability. Interestingly, the use of a non-information-sharing strategy and the direct-mail logistics mode may be more conducive to increasing the profit levels of overseas manufacturers. Finally, the delayed feedback control method can effectively reduce the entropy value, suppress chaotic phenomena in the system, and restore stability to output decisions from a fluctuating state. Full article

In the trade environment of a globalized economy, tariffs play a crucial role in transnational supply chains. At the same time, the power structure of the supply chain also plays an important role in the decision making and income distribution of a transnational supply chain. Therefore, we construct game-theoretic models to analyze the impacts of tariffs and power structures on the decision making and revenue distribution of transnational supply chains. First, we consider a bilateral monopoly model consisting of a Chinese manufacturer and a U.S. retailer and analyze the effects of tariffs and power structures on decision making and revenue distributions in this supply chain. Then, we extend the model to a duopoly competition model consisting of two Chinese manufacturers and one American retailer, further analyzing the roles of tariffs and power structures. The results indicate that in the bilateral monopoly model, the impact of tariffs on the manufacturer’s profits is always greater than on the retailer’s profits under a manufacturer-led circumstance. However, in a competitive model, when the market size is large, the impact of tariffs on the manufacturer’s profits exceeds that of the retailer’s profits; conversely, when the market size is smaller, the impact of tariffs on the retailer’s profits is greater than on the manufacturer’s profits. Furthermore, we find that in the duopoly competition model, under the manufacturer-led circumstance, both the manufacturer and the retailer earn the highest profits. Full article

This paper investigates the impact of tariff escalation on multinational suppliers relocating their production capacity to tariff-preferential regions with unreliable supply caused by low-production technology. We build a game theory model to analyze this issue based on three decisions for supplier-capacity relocation: no relocation, partial relocation, and full relocation. Our analysis finds that when tariffs are low or the production technology of the base in a preferential tariff region is not advanced, the supplier tends to adopt a partial-relocation strategy, but this strategy may be hindered by a manufacturer’s order-allocation decision, leading to a no-relocation strategy as the supply chain’s equilibrium. This may result in greater losses for the supplier. When tariffs are high or the production technology of the base in the preferential tariff region is advanced, the equilibrium strategy for the supply chain shifts to a full-relocation strategy. Interestingly, in the partial-relocation strategy, the higher production technology in the preferential tariff region negatively impacts the manufacturer’s expected profits but benefits the supplier’s expected profits due to the increased double marginalization. Finally, we find that the supplier can reduce the impact of tariffs by relocating their production capacity, especially with the partial-relocation strategy, as the supplier is always motivated to improve the production technology of the base in the preferential tariff region, with a potential purpose of transferring tariff costs to the manufacturer and consumers. Full article

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity. Full article

The electrification of the transport sector has emerged as a game changer in addressing the issues of climate change caused by global warming. However, the unregulated expansion and simplistic approach to electric vehicle (EV) charging pose substantial risks to grid stability and efficiency. Intelligent charging techniques using Information and Communication Technology, known as smart charging, enable the transformation of the EV fleets from passive consumers to active participants within the grid ecosystem. This concept facilitates the EV fleet’s contribution to various grid services, enhancing grid functionality and resilience. This paper investigates the optimal infrastructure design for a smart charging system within the Monash microgrid (Clayton campus). We introduce a centralized Grid-to-Vehicle (G2V) algorithm and formulate three optimization problems utilizing linear and least-squares programming methods. These problems address tariff structures between the main grid and microgrid, aiming to maximize aggregator profits or minimize load fluctuations while meeting EV users’ charging needs. Additionally, our framework incorporates network-aware coordination via the Newton–Raphson method, leveraging EVs’ charging flexibility to mitigate congestion and node voltage issues. We evaluate the G2V algorithm’s performance under increasing EV user demand through simulation and analyze the net present value (NPV) over 15 years. The results highlight the effectiveness of our proposed framework in optimizing grid operation management. Moreover, our case study offers valuable insights into an efficient investment strategy for deploying the G2V system on campus. Full article

In this paper, an output dynamic game model of intertwined supply chains operating in two different countries is established. The Nash equilibrium point of the model and its stable region are obtained using nonlinear dynamic principles. The complex properties of the system, such as stability, period-doubling bifurcations, and chaos, are investigated using numerical simulations. Our results suggest that the level of output and the system’s profits undergo bifurcation and chaos with an increase in the output adjustment speed. An interesting phenomenon occurs in that higher tariffs lead to the expansion of the stable range of the supply chain in the product-exporting country. The chaotic behavior of the system is sensitive to the value of the initial level of output. In supply chain competition, each supply chain firm should make suitable adjustments to the speed of output. To maintain the stability of domestic markets, excessive tariffs should be avoided. It is essential that each supply chain firm evaluates the potential impacts of different initial output values when making initial decisions. Using the method of delayed feedback control, the chaotic behavior of the system can effectively be controlled. These findings offer valuable and novel insight into inter-chain competition in supply chain networks. Full article

In the context of national efforts to promote country-wide distributed photovoltaics (DPVs), the installation of distributed energy storage systems (DESSs) can solve the current problems of DPV consumption, peak shaving, and valley filling, as well as operation optimization faced by medium-voltage distribution networks (DN). In this paper, firstly, a price elasticity matrix based on the peak and valley tariff mechanism is introduced to establish a master–slave game framework for DN-DESSs under the DPV multi-point access environment. Secondly, the main model optimizes the pricing strategy of peak and valley tariffs with the objective of the lowest annual operating cost of the DN, and the slave model establishes a two-layer optimization model of DESSs with the objective of the maximum investment return of the DESSs and the lowest daily operating costs and call the CPLEX solver and particle swarm optimization algorithm for solving. Finally, the IEEE33 node system is used as a prototype for simulation verification. The results show that the proposed model can not only effectively reduce the operating cost of the distribution network but also play a role in improving the energy storage revenue and DPV consumption capacity, which has a certain degree of rationality and practicality. Full article

With the deepening of power market reform, further study on power trading mechanisms has become the core issue of power market study. The development stage of the industrial electricity market requires efficient and flexible pricing mechanisms. Currently available pricing strategies are inadequate for demand response management. Therefore, this paper provides an in-depth study of the pricing mechanism in the industrial electricity market in the context of electricity market reform. It proposes a demand–response-based dynamic pricing strategy for industrial parks. The method proposes a dynamic pricing strategy for demand-side response in industrial parks based on master–slave game by establishing an exogenous model of demand-side response and incentives. Compared with the existing strategies, the strategy is more efficient and flexible, and effectively improves the economic efficiency of power trading and load regulation. Firstly, an exogenous model of demand-side response and incentive is built to characterize the demand-side response cost. The method focuses more on describing the exogenous characteristics of user incentives and response quantities. It only needs to analyze the exogenous indicators and random errors in various typical scenarios. The description of user demand-side response is more efficient. Secondly, a master–slave-game-based dynamic pricing strategy for industrial parks with demand-side response is proposed. The strategy is composed of a two-stage optimization. The primary regulation of customers is achieved by day-ahead time-of-use tariffs. The secondary regulation of customers is achieved by means of the same-day regulation of demand and purchase regarding clean electricity. The proposed two-stage price formation mechanism is more economical, more effective in load regulation, and improves the flexibility of industrial pricing. Finally, a case study is conducted on an industrial power user in a park in Liaoning Province. The results show that the proposed method is significantly better than existing methods in terms of improving the economic efficiency and load control effectiveness of the pricing strategy. Full article

To address the negative impact of large-scale disorderly grid connection of EVs on the stable operation of the power grid, a cooperative game cooperative control strategy for EVs based on tariff leverage is proposed, taking the grid-side and user-side economy as the objective function, taking into account the EV load state constraint, distribution grid power constraint, bi-directional charging and discharging pile power constraint, dynamic tariff constraint, and cooperative game members’ revenue constraint. A dynamic cooperative game model based on bi-directional charging and discharging piles is established, and the weight of users in the game is increased. Based on the cooperative game model, an optimal real-time tariff is determined for both the electric power operators and the charging and discharging pile users and based on the real-time updated dynamic tariff and the EV power connected to the charging and discharging pile at the current moment, a genetic algorithm is used to solve the simulation based on the Receding Horizon Control principle. The simulation results show that this control strategy has a smoother load curve and better peak and valley reduction than the fixed tariff and the time-of-use tariff, and it reduces the operating cost of the electric power operators. In addition, it brings the best economic benefits to the users, with the overall revenue of the charging and discharging piles increasing by up to 6.3% under the dynamic tariff. Full article

Due to global warming and climate change, it is essential to produce power using renewable sources, such as solar, wind, fuel cells, etc. The traditional grid shifts towards the smart grid by infusing digital communication techniques and information technology. As the current power system is shifting towards a smart grid, the utility and prosumers participate in the energy trading process. Due to the distributed nature of the smart grid, providing a fair price among them is becoming a difficult task. The article introduces a model for energy trading in a smart grid by allowing participants to negotiate in multiple stages using a game-theory-based multi-stage Nash Bargaining Solution (NBS). The model’s application of game theory enables the participants to decide on a mutually acceptable price, thereby encouraging the utility, private parties and prosumers (those who are able to generate and consume energy) to participate in the trading process. Since all parties participate in the trading procedure, greenhouse gas emissions are reduced. The proposed model also balances the benefits of consumers and producers in the final agreed fixed price. To demonstrate the efficacy of the proposed work, we compare the analytical results with feed-in-tariff (FiT) techniques in terms of consumers’ energy bills and producers’ revenue. For experimental analysis, 20 participants are considered, where the percentage reduction in the bill of each consumer and the percentage increment of revenue of each producer are compared to FiT. On average, the overall bill of the consumer is reduced by $32.8\%$, and the producers’ revenue is increased by $64.83\%$ compared to FiT. It has been shown further that the proposed model shows better performance as compared to FiT with an increase in the number of participants. The analysis of carbon emission reduction in the proposed model has been analyzed, where, for 10 participants, the carbon emission reduction is approximately 28.48 kg/kWh, and for 100 participants is 342.397 kg/kWh. Full article