High altitude mining zones, remote from grid coverage, depend on a high proportion of renewable energy, necessitating enhanced flexibility in system control. Within demand side, particularly the mining load, is characterized by large capacity and controllable power. However, participation in demand response (DR) is limited by normal production needs and the lack of effective control strategies. To fully explore the potential of flexible demand side resources, this paper focuses on the load of a high altitude mining zone and proposes a DR strategy for the high altitude smart flexible mining zone (HASFMZ), integrating production, residential, and transportation. Firstly, the paper analyzes the ore production process and establishes a smart flexible mining zone control model based on power and mass constraints. Secondly, by coupling a mining zone model with a hydrogen refueling station model that integrates production, storage, and refueling and a residential model that integrates electricity, thermal, and oxygen supply, the HASFMZ is proposed. A load aggregator is introduced, and based on the relationship between the two, a master-slave game-theoretic framework is proposed to guide the DR for HASFMZ. Finally, a case study from a high altitude mining zone demonstrates the potential of HASFMZ for DR, renewable energy integration, and adaptation to various operational scenarios. Additionally, daily carbon reduction of 1,548.72 kg from ore transportation, providing a reference model for industrial load participation in DR.

Energy agents such as villages, ranchlands, and hydrogen refueling stations in high-altitude areas are located far from the main power grid and rely on renewable energy for their power supply. However, limited supply capacity and system flexibility challenge their sustainable operation. This paper proposes a dual-layer energy sharing framework to enhance load supply and local renewable energy utilization in these areas. First, neighboring villages and pastures are aggregated, and a composite energy storage provider is introduced to offer diversified shared energy storage services, constructing the inner-layer model of the framework and enhancing the energy utilization patterns of villages and pastures. Second, oxygen production equipment will be deployed at renewable energy stations to provide electricity and oxygen supply. Through peer-to-peer energy sharing, interconnection among renewable energy stations, hydrogen refueling stations, and villages/pastures will be achieved, further enhancing the flexibility of the framework. Finally, the framework will be optimized in a distributed manner using a bi-level alternating direction method of multipliers (ADMM), with a case study focusing on the Tibet region of China. Results indicate that the framework elevates the load supply rate in villages and pastures to above 95 % despite the constrained energy supply, with distributed renewable energy achieving 100 % absorption. Furthermore, it significantly improves the energy absorption rate at renewable energy stations from 90.97 % to 95.17 %, offering a viable solution for sustainable energy operations in highaltitude areas.

With the increasing integration of renewable energy sources into the power system, challenges such as wind curtailment and operational flexibility are becoming more prominent. Therefore, this paper proposes a low-carbon optimised strategy for integrated energy system (IES) that considers the efficient use of hydrogen energy and the flexible operation of carbon capture power plant (CCPP)–methane reactor (MR)–hydrogen-doped combined heat and power (HCHP) combination. First, a model for the efficient utilisation of hydrogen energy containing wind power to hydrogen, hydrogen to thermoelectricity, gas-mixed hydrogen and hydrogen to methane was established. Secondly, the co-ordination mechanism among CCPP, HCHP and MR is explored, and the flexibility improvement of CCPP and HCHP is introduced by the liquid storage tank (LST) and Kalina cycle, respectively, and the joint CCPP-MR-HCHP flexible operation model is constructed. Finally, the integrated demand response (IDR) of electricity and heat is introduced, and a novel low-carbon optimisation model of the IES is established by integrating low-carbon and economic considerations. The simulation part of the example set up different scenarios for comparison, and the results showed that the introduction of an efficient hydrogen energy utilisation model can effectively improve the level of wind power consumption and reduce the total system cost and carbon emissions by about 11.35% and 24.73%, respectively. In addition, the proposed CCPP-MR-HCHP model can significantly improve the operational flexibility of the system, reducing the total system cost and carbon emissions by approximately 8.51% and 11.06%, respectively, compared to traditional operating modes.

With the transformation of the global energy structure and the proposal of low-carbon development goals, the development of a clean, economical and efficient energy supply system has become an important task in the energy field. Integrated Energy Systems (IES), as a system that is synergistically optimized through multiple energy forms, are gaining traction due to their potential to improve energy efficiency and reduce system operating costs. Especially at high altitudes, it is particularly important to promote the application of oxygen integrated energy systems due to the special needs and challenges of oxygen supply. This system can not only effectively meet the demand for oxygen in high-altitude areas, but also cope with the impact of new energy volatility. By promoting local energy sharing transactions between multiple energy entities, it can not only improve the utilization rate of new energy, but also reduce the overall operating cost of the system. In this paper, we propose an oxygen integrated energy system (OIES), in which oxygen is introduced as an important energy carrier to optimize its use, thereby improving energy conversion efficiency and resource utilization. The main contributions of this paper include the proposal and exploration of the Oxygen-Integrated Energy System (OIES), which effectively integrates multiple energy forms while specifically addressing the unique oxygen demands of high-altitude regions. By optimizing the processes of oxygen production, storage, and release, the paper demonstrates the advantages of OIES in improving energy efficiency, reducing operational costs, and mitigating the volatility of renewable energy sources. Through an economic comparison of water electrolysis and compressed air methods for oxygen production, the study reveals the benefits of each approach in different operational contexts, with water electrolysis being particularly well-suited to addressing energy fluctuations. Furthermore, the paper introduces a scheduling model based on the Improved Particle Swarm Optimization (IPSO) algorithm, which optimizes the oxygen production, storage, and release process, thus reducing costs and enhancing system flexibility. This system provides a practical and effective solution for the efficient management of oxygen in multi-energy systems.

To address the growing load management challenges posed by the widespread adoption of electric vehicles, this paper proposes a novel energy collaboration framework integrating Community Energy Storage and Photovoltaic Charging Station clusters. The framework aims to balance grid loads, improve energy utilization, and enhance power system stability. A Coordinated Peak-Shaving and Charging Optimization Strategy is developed to encourage off-peak EV charging, effectively reducing grid peak loads and improving user satisfaction. Additionally, a cooperative alliance model between Community Energy Storage and Photovoltaic Charging Station is established, leveraging Nash bargaining theory to decompose the game into cost minimization and benefit distribution sub-problems and used the ADMM algorithm for distributed solving. To ensure fair and efficient profit allocation, an optimized scheduling strategy based on asymmetric bar-gaining is proposed, addressing the limitations of traditional Nash models. Simulation results demonstrate that the proposed framework reduces system costs by 12.35 %, improves EV user satisfaction, highlighting its potential for practical applications in multi-entity energy systems.

With the continuous increase in the proportion of new energy sources connected to the power grid, the operational uncertainty of distribution networks has significantly increased. Reasonably configuring and dispatching energy storage systems has become a key approach to enhancing the flexibility, stability, and economic efficiency of the system. This paper takes a typical distribution network as the research object and constructs a joint economic dispatch model that includes battery energy storage and pumped - storage systems. It comprehensively considers factors such as the charging and discharging behaviors of the two types of energy storage, energy state updates, water level changes, and capacity investment, and establishes a joint objective function that includes operating costs and construction costs. To solve this model, an improved multi - particle swarm optimization algorithm is adopted, and simulation verification is carried out in the IEEE 33 - bus distribution network to optimize the dispatch scheme of the system under the typical daily load curve. The results show that the introduction of pumped storage significantly improves the utilization rate of wind power and the economic efficiency of the system. Reasonable configuration of hybrid energy storage can effectively improve the ability of the distribution network to absorb wind and solar energy and its operational stability.

The paper addresses the economic operation optimization problem of photovoltaic charging-swapping-storage integrated stations (PCSSIS) in high-penetration distribution networks. It proposes a dual-layer optimization scheduling model for PCSSIS clusters and distribution network systems. Firstly, a master-slave game model is constructed. The upper layer takes the high-penetration distribution network as the decision-making entity and aims to maximize its own revenue while considering the energy trading of PCSSIS. The lower layer takes PCSSIS as the decision-making entity, and PCSSIS adjusts energy flow and optimizes revenue based on the internal electricity price provided by the upper-layer distribution network. Secondly, the differential evolution algorithm and GUROBI solver are used to solve for the maximum revenue, internal electricity price, and electricity consumption of PCSSIS and the distribution network. Finally, the effectiveness of the proposed strategy is verified through case studies and simulations.

The distributed robust optimal allocation method for multi-microgrid interconnected systems usually involves a large number of variables and constraints, and the computational complexity is high in practical applications, which makes it difficult to solve the problem. Therefore, a distributed robust optimal allocation method for multi-microgrid interconnection systems based on multi-objective swarm algorithm is proposed. A distributed robust optimization configuration constraint index model for multi-microgrid interconnection system is established. Considering the influence of energy storage technology operation characteristics on its service life, a micro-grid hybrid energy storage capacity optimization configuration model with the minimum annual comprehensive energy storage cost as the objective function is established with charge and discharge power and residual power as the constraint conditions. The multi-objective swarm algorithm is used to realize the optimization model of distributed robust configuration microgrid interconnection system. By determining the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points, the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points are determined. The hybrid energy storage configuration model of multi-microgrid interconnection system is established with the minimum alternative operating cost as the objective function, so as to realize the distributed robust optimal configuration of multi-microgrid interconnection system. The simulation results show that the distributed configuration of multi-microgrid interconnection system with the proposed method has good robustness and strong optimization control ability.

In comprehensive energy systems incorporating various forms of energy, coordinating and optimizing the operation of each system component to maximize economic efficiency while meeting energy demands is crucial. This paper investigates the operational optimization and scheduling problem of a green industrial park's integrated energy system based on hydrogen utilization. Building upon modeling of key equipment, it addresses the stochastic nature of wind and solar outputs and the fluctuations in energy loads, constructing a multi-timescale optimization scheduling model encompassing both day-ahead and intra-day periods. Two scenarios are primarily studied: firstly, to mitigate the impact of uncertainty in wind and solar outputs and load, the PSO-BP neural network method is employed for forecasting; secondly, with economic optimization as the objective, penalty terms for curtailed wind and solar power and carbon emissions costs are introduced, and the park's coordinated optimization scheduling is conducted based on this.

To tackle the scheduling challenges in industrial park integrated energy systems, this study incorporates diverse energy storage forms within an electric-thermal-hydrogen coupling framework and optimizes them across multiple time scales, from day-ahead to intraday. Initially, system and equipment modeling are performed for the integrated energy system of industrial parks, incorporating the coupling of electricity, heat, and hydrogen. Subsequently, a day-ahead to intraday multi-time scale optimization model is developed. Lastly, by incorporating additional energy storage forms, four distinct hydrogen production scenarios are created for simulation and validation. Experimental results demonstrate that equipping with multiple energy storage forms enhances both the economic efficiency and environmental sustainability of the system.

In traditional comprehensive energy system benefit assessment studies, it is common to rely on a single weighting method, considering only subjective or objective weights to determine the importance of assessment indicators. This approach often fails to adequately combine subjective and objective factors. To integrate the advantages of both methods, this paper proposes a comprehensive evaluation model that combines the Best-Worst Method (BWN) and an improved entropy method with the coefficient of variation. The model adds a hydrogen production process to the common integrated energy systems for self-use or external sale of hydrogen. By selecting the scenario of a green hydrogen industrial park project, the newly constructed comprehensive evaluation model is applied for benefit assessment and case analysis. Through case verification, this study’s method has been confirmed to be scientific and effective.

As the penetration of distributed renewable energy gradually increases, prosumer groups in high-altitude areas are being expanded. However, the operation of these prosumers in high-altitude areas faces numerous challenges. To address the issue, this paper proposes a double layer energy cooperation framework for high-altitude prosumer groups, considers peer aggregation and shared composite energy storage, aims to adapt to the trend of diversified energy interactions and improve the economic efficiency, maximize social welfare and ensure the continuous growth of the sharing market of prosumers operations in high altitude areas. To meet the diversified energy interaction needs of prosumers, a composite energy storage provider, an energy aggregator, and a two-stage profit clearing scheme are proposed. Furthermore, the overall benefits of the framework and the impact of profit clearing schemes on fairness under various scenarios are studied, and the comprehensive effects of the double layer energy framework are evaluated. The study results show that compared to independent operation by prosumers, under the guidance of shared electricity prices and composite energy storage providers, the economic benefits, evaluation indicators and energy storage utilization efficiency have all been improved. In addition, compared to other profit distribution modes, the two-stage profit clearing scheme proposed in this paper has higher fairness, which greatly encourages the participation of interested entities in energy cooperation. This study provides a feasible framework for energy cooperation among prosumers, and the framework's effectiveness is well-validated.

Accurately predicting the magnitude of non-smooth and non-linear loads is important for good network operation and maintenance, reasonable design of startup and shutdown schedules, and ensuring safe and stable operation of the power system. A load decomposition prediction method based on singular spectrum analysis and an improved neural network is proposed. Considering the cyclic characteristics of the medium-term load, the trend and period series of the load sequence are extracted by HP filtering and singular spectrum decomposition; further, each sub-series of the decomposition is predicted by the Elman neural network model optimized by the PSO algorithm; finally, the results of each sub-series are superimposed as the final load prediction result. The results of the prediction analysis combined with the data provided by a numerical modelling competition show that the method has a high prediction accuracy for medium-term load prediction.

With the ever-increasing penetration rate of distributed renewable energy in the smart grid, the role of consumers is shifted to prosumers, and shared energy storage can be a potential measure to improve the operating income of prosumers. Nevertheless, the energy cooperation strategies of high-altitude prosumers (HAPs) are rarely studied. This study proposes an energy cooperation framework for HAPs, aiming to improve the economic flexibility of HAP operations and promote the cooperation framework. Firstly, a combined oxygen supply model for HAPs is proposed to satisfy the electricity and oxygen supply demand in high-altitude areas. Secondly, a composite energy storage provider (CESP) is introduced to provide electricity‑oxygen‑hydrogen composite energy storage sharing services and to establish an energy cooperation framework between HAPs and CESPs. Moreover, an asymmetric profit distribution model with the contributions of multiple energy sharing is proposed, and a two-stage profit distribution is carried out for CESPs and HAPs. Finally, the multi-scenario analysis and comparison are conducted to assess the overall benefits of the framework. From the research results, the proposed framework outperforms the independent HAPs, and the economic benefits, primary energy saving rate, system independence, and self-sufficiency rate have increased by 1609.91 CNY, 15.00%, 10.75%, and 15.31%, respectively. This study provides a feasible framework for energy cooperation among HAPs, and the framework's effectiveness is well-validated.

In the environment of high speed development of large user industries, in order to ensure safe and reliable access to the grid for industrial large users, this paper proposes an industrial large user access planning model based on access risk. First, the traditional economic and security indicators are replaced by risk indicators, and the risk indicator system is constructed by considering the uncertainty of large user access capacity. Then, the CRITIC method is used to assign weights to the indicators of comprehensive risk, and the artificial bee colony algorithm is used to find the optimal access node as the target, so as to complete the site selection and access work of industrial large users. Finally, the effectiveness of the proposed method is verified by using the IEEE-69 node system as an example.

近年来,我国新能源技术迅猛发展,其中风电并网发电量增长迅速.但风电的接入往往采取大规模集群式并网的方式,导致电压不稳定问题突出、风电并网点电压越限问题严重.自动电压控制(AVC)系统是电网无功电压管理的重要技术手段,对电网实现经济稳定运行有重要意义.基于目前电网中风电大规模接入的情况,提出了风电参与下的电网AVC系统改进控制策略,即基于分级式控制结构,建立三级式风电AVC系统协调控制策略,以风电汇集站作为中间协调层,在协调层进行控制决策,并通过罚函数处理的方式将多机组非线性无功优化问题变为无功分配极值问题,最后,结合提出的改进和声搜索算法寻求无功的最优解.该策略可以确保AVC系统控制下电网的经济稳定运行.

Firstly, this paper summarizes the current microgrid evaluation indicators and constructs an evaluation indicator system from four aspects including reliability, economy, technology and environmental protection, from which the comprehensive evaluation of the microgrid is carried out. The DEMATEL (Decision Making Trial and Evaluation Laboratory method) and AHP (Analytic Hierarchy Process) are combined to calculate the subjective weights of each indicator, and the CRITIC (Criteria Importance Though Intercrieria Correlation) and EWM (the Entropy Weight Method) are combined to calculate the objective weights of each indicator, and then the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) is used to determine the optimal program among the microgrid construction alternatives by comparing the group benefits and individual regrets of each program after introducing the personal preferences of decision makers. Finally, the feasibility of the method is verified in the actual arithmetic example, and the corresponding suggestions are provided for the planning and construction of green energy microgrid.

The increasing penetration of renewable energy resources into regional-integrated energy systems (RIES) holds higher standards on operational flexibility. To alleviate the negative effects of insufficient operational flexibility, this paper proposes a joint market equilibrium model to obtain flexible resources in microgrids (MGs) and district heating networks (DHNs) through economic incentives. Based on the incorporation of the flexiramp (FRP) into the energy markets, a joint market mechanism is first established. Through multi-energy synergy, the FRP provisions on the supply and demand sides in MGs are then introduced. Considering the flexibility exploitation of DHNs, a collaborative FRP supply method is developed to formulate the optimal bidding strategy for MGs. Moreover, a hierarchical model is designed to achieve joint market equilibrium, where MGs strategically bid in the external layer and the RIES operator optimally clears the market in the internal layer. The combined utilization of the Q-learning algorithm and path-tracking interior point method is applied to solve the hierarchical model efficiently. The performance of the proposed approach is verified by case studies. The results demonstrate the efficacy of the proposed approach for operational flexibility improvement through minimizing renewable energy curtailment, the overall cost, and the average energy prices.

To realize low-carbon energy systems, distributed energy storage systems and flexible loads have been integrated into power grids. System reliability, economy, and resilience, therefore, face significant challenges. This article presents modeling of a distributed energy micro-grid including wind turbines, micro gas turbines, waste heat recovery devices, electric boilers, direct-fired boilers, battery energy storage, interruptible loads, and transferable loads. At the same time, the optimal configuration of energy storage and the demand-side response modeling are studied, and the combined optimization control strategy of the two is demonstrated. The simulation results indicate that the proposed control strategy has better performance than the traditional operation. In addition, this article also clarifies the impact of control strategy on distribution system resilience. The results show that the control strategy proposed in this article can achieve the resource complementarity of demand-side response and energy storage, and realize the integrated coordination of source, network, load, and storage. The distributed energy micro-grid under this control strategy has the best overall economic benefit and the best capacity to accommodate load growth.