Smart grid construction is an important carrier and an effective way to promote the development of low-carbon economy. Demand response (DR) is commonly regarded as an important core technology in smart grid field, and it reflects the flexible and interactive features of the core business in smart electricity. It is the developing direction of automated demand response (ADR) technology, and its main features are the standardization of information exchange, together with the intelligence of decision-making and the automation of implementations. ADR technology can improve the efficiency of the whole power system and enhance the ability to accept new energy sources. This paper analyzes the role of demand response in improving efficiency and low-carbon energy saving power systems. The automated demand response system architecture is investigated, and the ADR roadmap of commercial/industrial and residential customer is proposed. The key technologies for ADR system are analyzed, including demand response strategy, information exchanging model, measurement and verification techniques, and multi-agent scheduling techniques. To ensure the interoperability between the grid side and the user side, the ADR business in smart grid user interface standards is concluded to support further demand side management project.

Industrial power has a large load base and considerable adjustment potential. Enterprises with a high degree of automation and adjustable potential can automatically adjust the production status according to the peak load, frequency of the power grid and the demand of new energy consumption, so as to realize automatic demand response. This paper analyzes the opportunities and challenges of industrial demand response under the integration of Industrial Internet and Energy Internet. At the same time, the development direction of industrial demand response under the new situation, such as comprehensive demand response, adjustable load resources and other technical and policy aspects are prospected.

This paper proposes a novel approach that unifies a demand response (DR) with a master plan of the model predictive control method focusing on scheduling maintenance and replacement for suboptimal equipment in real-time solar power plants. By leveraging DR mechanisms and MPC algorithms, our proposed framework starts with understanding the correlation between solar module temperature, surrounding temperature, and irradiation—essential for predicting and optimizing the performance of solar energy installations. It extends to evaluate the DC to AC conversion ratio, which is an indicator of the efficiency of the inverters. This integration enables proactive decisions for repair, maintenance, or replacement of equipment. Through exploratory data analysis using Python, we establish the efficiency and benefits of our anticipated approach in identifying the relationship between the factors that affect solar power generation.

Distributed power resources (DPRs) offer a transformative opportunity to improve the efficiency, sustainability, and reliability of modern power infrastructures through their integration. This work presents a novel method based on a mix of renewable energy sources, energy storage technologies, and conventional generators for the optimization of DPR operations under dynamic market settings. Maximizing economic gains is the major objective while preserving system resilience and stability. To handle the complexity of DPR interactions, we offer a strong, hierarchical control architecture encompassing main, secondary, and tertiary levels. System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. Results indicate cost reductions of approximately 54.7%, which were achieved by effectively prioritizing renewable sources and optimizing energy storage usage. This research contributes both theoretically and practically to accelerating the transition toward sustainable, resilient, and economically viable power systems.

This study develops an optimization-based scheduling framework for coordinating the energy-intensive operations of a steel enterprise with estimated solar power availability. Unlike prior approaches that focus primarily on process efficiency or carbon reduction in isolation, the proposed model integrates demand response with linear programming to improve solar utilization while respecting load priorities. The solar generation profile is derived from typical meteorological year (TMY) irradiance data, adjusted for panel efficiency and system parameters, thereby serving as an estimated input rather than measured data. Simulation results over a 31-day horizon show that coordinated scheduling can reduce grid dependence and increase solar energy utilization by up to 99% under the simulated conditions. While the findings demonstrate the potential of load scheduling for industrial decarbonization, they are based on estimated solar data and a simplified system representation. Future work should incorporate real-world solar measurements and stochastic models to address uncertainty and further validate industrial applicability.

The iron and steel sector, a major global emitter, requires innovative strategies to achieve decarbonization. This study introduces a novel multi-method framework designed to maximize solar energy integration within steel plants. The approach synergizes three core elements: a Bayesian model for probabilistic solar forecasting, a dynamic system for aligning flexible production loads with renewable generation, and a Stackelberg game model to optimize economic participation in energy markets. When applied to a representative facility, the framework achieved a 37.5% solar penetration rate, drastically cutting grid dependence and operational costs without disrupting production. The findings validate a viable pathway for heavy industry to harmonize economic and environmental goals, with future work poised to integrate energy storage and detailed carbon accounting.