A game theory approach for the construction of a green and sustainable power grid

The efficient management of the green power grid supply chain is of great significance in addressing global energy transformation and achieving sustainable development goals. However, traditional methods struggle to effectively cope with the complexity and dynamics of demand forecasting and the multi-objective optimization problems in material allocation. In response to this challenge, this paper proposes a machine-learning-based demand forecasting and allocation optimization method, aiming to improve the management efficiency of the supply chain and reduce environmental impacts. First, based on the whole-process data of power grid materials, a multi-model fusion strategy is adopted for demand forecasting. By combining machine learning models such as long short-term memory (LSTM), extreme gradient boosting (XGBoost), and random forest, the prediction accuracy and the generalization ability of the model are significantly improved. Moreover, a “distributed collaborative optimization algorithm” is proposed. By decomposing the power grid regions, this paper optimizes transportation routes and inventory management, and comprehensively reduces transportation, inventory, and environmental protection costs while taking into account the real-time requirements in a complex supply chain environment. Finally, an empirical analysis is carried out in combination with the optimized allocation plan, verifying the practical effectiveness of the proposed method. The results indicate that the optimized scheme significantly outperforms the traditional method in terms of total cost, transportation efficiency, and carbon emissions. Specifically, the optimized scheme achieves a 13% reduction in transportation costs, a 10% decrease in inventory costs, and a 25% cut in environmental protection expenses. Additionally, it decreases transportation-related carbon emissions by approximately 250 tons. The demand forecasting and allocation optimization method based on machine learning has obvious economic and environmental advantages in the green power grid material supply chain. By effectively integrating various algorithms, this paper enhances the accuracy and stability of material management while substantially reducing operating costs and carbon emissions. This is in line with the sustainable goals of green power grid development. The paper provides an optimized framework with practical value for managing the green supply chain in the power grid industry.

The global energy landscape is undergoing a profound transformation as the world shifts towards sustainable and renewable energy sources.This transition is driven by the urgent need to mitigate climate change, reduce greenhouse gas emissions, and ensure energy security for future generations [1], [2].Renewable energy sources, such as solar, wind, and hydroelectric power, have emerged as promising alternatives to fossil fuels, offering clean and potentially inexhaustible energy solutions [3].Nonetheless, incorporating these renewable sources into existing power grids introduces considerable challenges because of their inherent intermittency and variability [4].Due to their inherent variability, incorporating renewable energy sources into current power grids poses major challenges.This study aims to optimize renewable energy integration using Internet of Things (IoT) technology and machine learning (ML) algorithms.The study was conducted across 30 renewable energy sites in the United States over six months (April-September 2023), encompassing solar, wind, and hydroelectric installations.Three ML models (Random Forest, XGBoost, and Long Short-Term Memory networks) were developed and compared against a traditional persistence model for energy generation forecasting.The study also implemented a reinforcement learning-based grid optimization system.Results showed significant improvements in forecasting accuracy, with the LSTM model achieving a 59.1% reduction in Mean Absolute Percentage Error compared to the persistence model.Grid stability improved substantially, with a 64.2% reduction in supply-demand mismatches.Overall renewable energy utilization increased by 19.2%, with wind energy seeing the largest improvement (21.8%).The implemented system resulted in estimated monthly cost savings of $320,000.These findings demonstrate the potential of IoT-ML systems to enhance renewable energy integration, contributing to more efficient, reliable, and sustainable power grids.

In recent years, the increasingly serious global resource and climate problems require people to build a green and sustainable economic strategy. As a power grid enterprise that takes burning fossil fuels as its main energy source, improving the environmental protection level of the power grid is of great significance for building a green and sustainable economy. The key to the environmental protection performance of the power grid lies in the power cable, and the core to improving the environmental protection of cable lies in the cable insulation material. The thermodynamic and electrical properties of insulating materials will affect the service efficiency and environmental performance of cables. Therefore, this paper uses the finite element simulation method to study the operating temperature of different cables with XLPE, PE, and PP insulation materials and finds its temperature rise characteristics under the fluctuating load in summer and winter. The results show that the maximum temperature of XLPE cable, PE cable, and PP cable under winter load is 299.37 K、299.69 K, and 302.45 K respectively, and the maximum temperature under summer load is 327.64 K, 327.99 K, and 332.42 K respectively. It can be found from the results that under the same load, the temperature rise of PP cable is the highest, and the temperature rise of XLPE cable is the lowest. Therefore, considering the relationship between cable temperature rise and thermal conductivity of cable insulation materials, as well as the difference in environmental protection characteristics between thermoplastic insulation materials and thermosetting insulation materials, PP cable can be used as the basic material for environmental protection power cable insulation. The research content of this paper puts forward the selection basis of environment-friendly cables that can be applied to build a green and sustainable power grid, which can provide a reference for solving environmental protection problems in large-scale cable applications.

The separation and bundling of electricity certificates based on consumption responsibility is an effective strategy for promoting the use of renewable energy sources. In this context, this paper focuses on power generation enterprises, power grids and power sales enterprises as research subjects. It employs the Stackelberg game method to construct four modes and compares the optimal decision-making of renewable energy supply chains under the weight of consumption responsibility of each subject in the supply chain. Furthermore, it analyses the impacts of key parameters on their decision-making. The findings of the study indicate that when the cost coefficient for green power production is low, the retail price of green power increases in tandem with the price of excess consumption certificates. Conversely, when the cost coefficient for green power production is high, the retail price of green power decreases in conjunction with the price of excess consumption certificates. Additionally, the level of green power production effort, the total amount of electricity, and the profit of each subject in the supply chain decline as the responsibility weight for renewable energy consumption increases. Overall, the supply chain profit reaches its maximum in the HF mode(separate pricing model for green power and green certificates with cooperation between power producers and grid companies) and its minimum in the BK mode(bundled pricing model for green power and green certificates without cooperation between power producers and grid companies).

Climate change and the desire to cut carbon emissions have increased demand for renewable energy. Quantum computing's revolutionary potential and unmatched processing capability could benefit capacity grids and other businesses. This research examines how quantum computing could improve power network efficiency to achieve sustainable energy goals. Quantum computing can solve complex power grid optimization problems. Current conventional approaches to power grid improvement have limits, but quantum computing can assist overcome them. Power grid optimization incorporates quantum methods like Grover's algorithm and the quantum approximate optimization algorithm. Quantum-resistant cryptographic techniques and quantum key distribution could secure power grid communication and data transfer. Given that quantum computing is still developing, the research examines the present obstacles to implementing quantum power grid solutions. Finally, quantum computing could improve power grid operations, security, and renewable energy consumption, helping accomplish sustainable energy goals.

Power grids are networks of lines and equipment used for electrical power generation, transmission, distribution, and consumption. Power grids are undergoing a process of digital transformation based on emerging information and communication technologies, with the aim of optimising the operation of power systems, increasing efficiency, reliability, sustainability, security, and quality of service, while reducing CO2 emissions. In this process of digital transformation, Artificial Intelligence (AI) has become a strategic element within power grids. This paper provides an overview of AI applications in power grids and how they can improve the efficiency, reliability, and availability of their processes. It analyses the main challenges and prospects for the future application of AI in power grids. AI applications help process large volumes of data and generate insights, with a focus on two primary functions: supporting operations and aiding strategic decision-making. These factors highlight the significant potential for AI application in power grids and solidify AI as a strategic key to the development of smart grids.

The emission control technologies decisions of port and shipping enterprises under subsidy and customers’ low-carbon preferences are discussed from a supply chain perspective. The game models are established under three game models (port-leader Stackelberg game, ship-leader Stackelberg game, and Nash game). The obtained results show that the impact of subsidy and low-carbon preference on demand in its pricing always is considered by the leader in the port supply chain. The profits and emissions in the Nash game are higher than that in the Stackelberg game. When subsidies and customers’ low-carbon preferences are low, the supply chain’s overall profits of using low sulfur oil are higher than that of using shore power. But the supply chain’s carbon emissions of adopting low sulfur oil are also higher than that of adopting shore power. When subsidies and low-carbon preferences of customers are high, the supply chain’s overall profits of using shore power are higher than that of using low sulfur oil. But the supply chain’s carbon emissions of adopting shore power are also higher than that of adopting low sulfur oil. When subsidy and low-carbon preference of customers are in the appropriate(medial) range, the supply chain would choose shore power to reduce emissions from the perspective of profits, and the whole carbon emissions of using shore power are lower than that of using low sulfur oil, so the regulator(government) and enterprises can achieve a win-win situation. Hence for a regulator who has to balance emission control and enterprises’ profits, implementing moderate subsidy within the appropriate range is the better strategy.

With the gradual implementation of power grid energy conservation and emission reduction policies, the factors affecting the price compensation of green power grid are becoming more and more complex. The green power grid price compensation system is divided into technology research and development subsystem, design subsystem, production subsystem, construction subsystem and operation subsystem. Applying the theory of system dynamics to establish the system dynamics model of green power grid price compensation. Through the analysis of causality diagram, the interaction mechanism between various factors is clearly shown, and the price compensation mechanism of green power grid is established.

This research article presents an innovative approach to enhance sustainable power generation and grid support by integrating real-time modeling and optimization with Molten Salt Energy Storage (MSES) and a Supercritical Steam Cycle (s-SC). As renewable energy usage grows, intermittent resource availability challenges grid stability and reliable power supply. To address this, we develop a system that merges real-time modeling and optimization for precise control of MSES and s-SC components. This integration ensures uninterrupted energy generation, storage, and distribution, optimizing renewable energy use during high-demand periods. Mathematical models and simulations assess the system's dynamic behavior, performance, and economic viability. Rigorous techno-economic analysis highlights cost-effectiveness and environmental benefits. Findings reveal exceptional energy efficiency and grid support, making it a promising solution for sustainable power generation and grid stability amid renewable energy growth. Real-time modeling and optimization emerge as crucial components in modern energy systems. The Combined Heat and Power (CHP) system achieves 56% energy efficiency with off-design impacts considered and 63.61% without. Also, the overall system exergy efficiency decreased from 73.36% at design to approximately 63.55% under off-design scenarios. Regarding the economic aspect, the levelized cost of storage (LCOS) for the CHP system is estimated at 114.4 €/MWh with off-design conditions and 106.8 €/MWh without.

To address the challenges faced by modern power systems—such as efficiency, dynamics, reliability, control, stability, economy and planning—significant efforts have been made to develop advanced techniques, tools and scientific innovations across various disciplines. Among these, the ‘digital twin’ (DT) has emerged as one of the most reliable and rapidly evolving technologies, now widely integrated into diverse applications. The incorporation of DT technology into energy systems marks a paradigm shift in achieving sustainable, efficient and resilient modern power grids. Although considerable research has been conducted on DT applications in the power sector, comprehensive reviews of its role in transforming power grids to accommodate high levels of renewable energy sources (RESs), smart grid technologies, vehicle‐to‐grid (V2G) systems and energy storage solutions remain limited. This paper seeks to bridge that gap by exploring the critical role of DT technology in this transformation. It examines the historical evolution, fundamental components and diverse applications of DT technology across modern grid systems. Detailed analyses focus on DT's application in modernising power grids, particularly in RES integration, energy storage, transmission and distribution, smart grid advancements and V2G systems. Additionally, the paper reviews progress, investments, standards, regulations and the key stakeholders driving DT advancements in power grids. Finally, major challenges, limitations and future perspectives for DT applications in next‐generation power grids are discussed. Key findings reveal that while DT technology delivers significant benefits—such as improved operational efficiency, enhanced grid stability, greater reliability, cost reduction, cybersecurity and resilience through real‐time monitoring, predictive maintenance and optimised energy management—addressing existing limitations is crucial to maximising DT's potential in advancing and modernising sustainable power grids.

While EVs partially rely on electricity from the power grid, they have the potential to be used as generation resources to provide energy and ancillary services into the grid. But the existing EVs' charge and exchange system has significant room for improvement through automation and information management, condition monitoring, and battery management, especially on the grid-connected control system. Therefore, a novel EV's intelligent integrated station (IIS) including bidirectional converters, exchange system, cascaded system and dispatch center, is proposed in this paper to pave the way for a more sustainable power grid. In this paper, we explore both the battery management system (BMS) and control strategy of grid-connection based on the technique of multi-source information fusion, focusing first on batteries information in IIS and then on operational information of power grid from energy management system (EMS). Based on the information in control center received from BMS and EMS, an effort is made to develop a novel control strategy of grid-connection to well take into account of the operational state of power grid and battery energy level of IIS, and further to improve the efficiency of power/energy flow between IIS and power grid. Moreover, an algorithm for power flow estimation between IIS and power grid based on Kalman filtering theory is proposed to get accurate information. Actual operation results indicate that the proposed system is an ideal power interface for EVs and power networks.

A sustainable power grid should be able to provide both environmental and economical advantages in electricity energy generation, consumption, and transaction facilitations. The smart grid inter-operability structure should be able to sustain with future energy market designs, flexible energy transactions, and new energy technologies, such as electric cars, dispersed generation, and highly penetrated renewable energy resources. Smart grid will provide more visibility and operability for relatively small scale generation/load assets into the energy markets. From today's bulk power system point of view, immediate challenges faced today include operation model identification, control signal and control schema, as well as metering solutions, which are usually with tariff regulated options. Natural resource constraints could become more sensitive factors on power grid expansions and electricity demand changes. For example, the accumulated wind power resources far away from load centers could lose their economic advantages with high transmission upgrade cost. Further, in recent practices, we have seen that the constructability of transmission projects is facing more and more environmental constraints on right-of-way options. Power engineers are not only responsible for reliable integrations, but also under the pressure of maintaining sustainable grid expansions to meet future demand growth. Practical projects and field experiences will be reviewed along with framework discussions.

The advancement of green power grid technology, driven by the Tenth Five-Year Plan and the "Double Carbon" objective, is transforming the energy sector and promoting a greener economy. Understanding the link between green power grid development and the ecosystem is crucial. This study establishes an evaluation system to assess their coordination using a model. The analysis shows a progression towards coordinated development since 2006, with green initiatives playing a key role. This evolution highlights green development as a driving force behind power grid progress, rather than a constraint.

Power system computation software tools are traditionally designed as serial codes and optimized for single-processor computers. They are becoming inadequate in terms of computational efficiency for the ever increasing complexity of the power grid. The power grid has served us remarkably well but is likely to see more changes over the next decade than it has seen over the past century. In particular, the widespread deployment of renewable generation, smart-grid controls, energy storage, plug-in hybrids, and other emerging technologies will require fundamental changes in the operational concepts and principal components of the grid. The grid is in an unprecedented transition that poses significant challenges in power grid operation. Central to this transition, power system computation needs to evolve accordingly to provide fast results for power grid management.On the other hand, power system computation should and has to take advantage of ubiquitous parallel computers. To bring HPC to power grid applications is not simply putting more computing units against the problem. It requires careful design and coding to match an application with computing hardware. Sometimes, alternative or new algorithms need to be used to maximize the benefit of HPC.This chapter demonstrates the benefits of HPC for power grid applications with several examples such as state estimation, contingency analysis, and dynamic simulation. These examples represent the major categories of power grid applications. Each of the applications has its own problem structure and data dependency requirements. The approach to apply HPC to these problems has different challenges. The HPC-enhanced state estimation, contingency analysis, and dynamic simulation presented in this chapter are suitable for today’s power grid operation.KeywordsPower SystemGraphic Processing UnitPower GridPacific Northwest National LaboratoryDynamic Load BalanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.