Comprehensive Energy System Research Articles

Low carbon scheduling method of mine integrated energy system based on two-stage P2G technology

Coal mines derive a large amount of various associated resources that can be used for production and living needs during the mining process. In order to realize the recycling of coal associated resources, a typical architecture of coal mine comprehensive energy system including the recycling of coal derived resources is proposed. Firstly, according to the coal mine resource endowment and its geological characteristics, the corresponding mine integrated energy system is constructed. Then, the refined two-stage P2G technology is combined with the mine integrated energy system to realize the complete consumption of wind and light and the efficient utilization of energy. Finally, the stepped carbon trading mechanism is used to further constrain the carbon emissions and realize the further reduction of carbon emissions in the mining area. The simulation model of the integrated mine energy system is established in MATLAB and the GUROBI solver is called to solve the results. The simulation results show that the method can effectively reduce carbon emissions, realize the complete consumption of wind and light and the efficient use of energy.

Comprehensive evaluation of rural regional integrated clean energy systems considering multi-subject interest coordination with pythagorean fuzzy information

In the development of a comprehensive clean energy system in rural areas, how to evaluate it scientifically and effectively is an urgent problem to be solved. Artificial intelligence has already been widely applied in the field of energy technology, but its application in the comprehensive benefit evaluation is still insufficient. The purpose of this study is to conduct a comprehensive evaluation of the rural, regional integrated clean energy system with coordinated multi-subject interests. Firstly, the interest relationship between the subjects of the participating rural regional comprehensive clean energy system is analyzed. And, the comprehensive benefit evaluation index system is established based on farmers, rural grid, new energy investment operators, government and interest subject reciprocity partners. Secondly, Sugeno-Weber aggregation operators are more flexible and used to acquire smooth aggregated outcomes from a group of information in a single set. Moreover, an algorithm of the multi-attribute decision-making process also reveals the supremacy and reliability of the research work. A case study is established to evaluate the rural integrated clean energy system with coordinated multi-subject interests. The results are as follows. The index system reflects the characteristics of the comprehensive clean energy system in rural areas, such as environmental characteristics, economic characteristics, safety characteristics and clean energy characteristics. The advantages and validity of developed research work are proved by comparing the findings of pioneered approaches with existing mathematical operators. The ranking of suitable energy sources is Solar power > Biomass energy > Geothermal energy > Wind and hydropower based integrated clean energy system.

Comprehensive Evaluation and Decision-Making of Park Level Comprehensive Energy System Planning Scheme Based on Object Attributes and Matter Element Extension

Comprehensive Evaluation and Decision-Making of Park Level Comprehensive Energy System Planning Scheme Based on Object Attributes and Matter Element Extension

Enhancing renewable energy utilization and energy management strategies for new energy yachts

Hydrogen energy, due to its clean and efficient nature, has shown great potential during the current transition period in the shipbuilding industry. However, the application of hydrogen energy in ship energy systems is influenced by variations in operational load and the integration of new energy sources during actual navigation. To address these issues, this paper focuses on optimizing and scheduling the operation of ships under various navigation conditions, considering the distributed nature of hydrogen energy. System simulations were conducted to model the photovoltaic (PV), proton exchange membrane fuel cells (PEMFCs), lithium batteries (LIBs), electrolytic cells (ECs), and energy storage modules of yacht energy systems. Component boundaries and objective functions were set, and two cases (excess photovoltaic state and constant power state) were designed to optimize and regulate the energy balance of hydrogen-powered yachts, enhancing their comprehensive utilization of renewable energy. By comparing the changes in ship energy under the two cases, it was concluded that case 1 ensures the maximum utilization of renewable energy. When photovoltaic power generation is insufficient, the PEMFC and LIB in the system provide the required power to achieve a supply-demand balance. Moreover, when PV power generation is sufficient, hydrogen energy is used to store renewable energy. The optimization method designed in this study can, to some extent, maximize the application of renewable energy in new energy yachts, ensuring the efficiency of the comprehensive energy system of new energy yachts, reducing emissions, and improving the sustainability and economic efficiency of the ships.

Research and application of low-carbon economic dispatch of urban building integrated energy system

Abstract Global warming is a serious threat common to all countries. The technological revolution and sustainable development of energy have become the international consensus. While ensuring economic benefits, promoting low carbon environmental protection of comprehensive energy system has become a top priority. Therefore, this paper designs a low-carbon economic scheduling system based on consistency theory. First, the importance of user demand response should be considered in the integrated energy system to ensure the economy of scheduling. Secondly, the improved step-step carbon trading mechanism is used in the algorithm to improve the low carbon performance of scheduling. Then, the distributed consistency algorithm is used to schedule the integrated energy system to realize the most energy output of each unit. In addition, a noise function is added to the algorithm for encryption and desensitization to prevent user privacy leakage. Finally, the effectiveness of the proposed economic scheduling method.

Comprehensive energy system planning with a focus on electric-thermal load correlations

Abstract Deepening research on electrothermal integrated energy systems has heightened the coupling between electric power and thermal systems. Accurate electrothermal load scenario modeling and thorough consideration of their interdependencies are crucial for effective planning and scheduling. The traditional method of generating scenarios cannot fully reflect the full complexity of the original power load. To address this, our paper introduces an enhanced clustering approach. Employing the Frank-Copula function to express the correlation between electric and thermal loads, we optimize the clustering and scene reduction sequence, yielding correlated typical electric and thermal load datasets. These refined load profiles serve as the foundation for comprehensive planning and analysis of the integrated energy system.

Economic analysis of hydrogen energy systems: A global perspective

In the realm of renewable energy, the integration of wind power and hydrogen energy systems represents a promising avenue towards environmental sustainability. However, the development of cost-effective hydrogen energy storage solutions is crucial to fully realize the potential of hydrogen as a renewable energy source. By combining wind power generation with hydrogen storage, a comprehensive hydrogen energy system can be established. This study aims to devise a physiologically inspired optimization approach for designing a standalone wind power producer that incorporates a hydrogen energy system on a global scale. The optimization process considers both total cost and capacity loss to determine the optimal configuration for the system. The optimal setup for an off-grid solution involves the utilization of eight distinct types of compact horizontal-axis wind turbines. Additionally, a sensitivity analysis is conducted by varying component capital costs to assess their impact on overall cost and load loss. Simulation results indicate that at a 15 % loss, the cost of energy (COE) is $1.3772, while at 0 % loss, it stands at $1.6908. Capital expenses associated with wind turbines and hydrogen storage systems significantly contribute to the overall cost. Consequently, the wind turbine-hydrogen storage system emerges as the most cost-effective and reliable option due to its low cost of energy.

Optimization Effectiveness of Multi-Intelligence Consistent Energy System Based on Digital Advertising and Data Analysis

This paper introduces a novel approach to address the issue of overestimation of state-action values in reinforcement learning algorithms based on the Q-framework. It integrates a dual-layer Q-learning algorithm with a grey wolf intelligent optimization method, enabling rapid search for optimal allocations in unknown search spaces. This integration results in the development of a multi-agent collaborative Area-Grid Coordination (A-GC) strategy, termed the grey wolf double Q (GWDQ) strategy, tailored for multi-area energy interconnection scenarios. The proposed GWDQ strategy is evaluated through simulation experiments on comprehensive energy system models, including mixed gas turbine systems, Combined Cooling, Heating, and Power (CCHP) systems, and a multi-area energy-interconnected Northeast power grid model. A centralized architecture is established to analyze the optimization effects of digital advertising. The performance of the GWDQ strategy is compared with traditional reinforcement learning algorithms through simulation and empirical data validation. Results indicate that the GWDQ strategy exhibits stronger learning capabilities, improved stability, and enhanced control performance compared to traditional methods. It demonstrates superior optimization for digital advertising and enables swift acquisition of optimal coordination in A-GC processes across multiple regions. Additionally, the paper analyzes the environmental and economic impacts of the proposed strategy.

Optimization of rural integrated energy system considering carbon emissions

Abstract The country’s objective of achieving “carbon neutrality” has necessitated transforming and upgrading rural energy systems. Establishing a comprehensive rural energy system is crucial to accelerate the development of a clean energy system in rural areas. This paper addresses the context of China’s “carbon neutrality” goal and presents a model for a rural integrated energy system that incorporates electricity, biomass energy, and PV. The model aims to optimize the capacity of energy devices while considering economic and environmental factors. Practical case studies are conducted to validate the feasibility of the proposed model.

Optimization scheduling for low-carbon operation of building integrated energy systems considering source-load uncertainty and user comfort

Hydrogen, being a clean energy source, presents novel possibilities for the efficient exploitation of building energy. Nevertheless, the unpredictability in the production of renewable energy, the fluctuating demand from users, and the varying comfort needs within buildings provide substantial obstacles to the functioning of building energy systems. This work suggests an optimal technique for managing a comprehensive energy system based on hydrogen. The strategy takes into account the variability in energy sources and the need for user satisfaction. Firstly, a model of building integrated energy system (BIES) is created, which includes devices for utilizing hydrogen due to its adaptable qualities. Afterwards, the adaptable attributes of electrical, cooling, and heating loads within the building are carefully examined, resulting in the creation of a integrated demand response (IDR) model and a user-integrated comfort rating model. Moreover, a ladder-type carbon tax framework is implemented, employing robust optimization methods to tackle uncertainty in the production of renewable energy and demand. Finally, a model for optimizing the scheduling of energy system operation is formulated. A case analysis is performed utilizing a spacious office building located in the northern region as an instructive example. The findings indicate that the suggested approaches successfully control user involvement in demand response, decrease carbon emissions in the system, improve user energy comfort, and accomplish the economic, low-carbon, and comfortable functioning of BIES.

Security in advanced metering infrastructures: Lightweight cryptography

Abstract Smart grids are designed to revolutionize the energy sector by creating a smarter, more efficient and reliable power supply network. The rise of smart grids is a response to the need for a more comprehensive and sophisticated energy system that caters to the needs of homes and businesses. Key features of smart grids include the integration of renewable energy sources, decentralized generation and advanced distribution networks. At the heart of smart grids is a sophisticated metering system, consisting of intelligent electronic devices that measure energy consumption and enable two-way communication between the utility company and the consumer. The information exchanged via these devices is critical, as it includes sensitive data such as energy consumption patterns and billing information. With the rise of the Internet of Things (IoT) and Industry 4.0, the security of this information is of utmost importance. This paper delves into the security challenges faced by advanced metering infrastructure (AMI) and highlights the crucial role played by cryptography in ensuring the secure exchange of sensitive information. Cryptographic protocols such as encryption, authentication and digital signatures are essential components of a secure smart grid infrastructure. These protocols work together to ensure the confidentiality, integrity and authenticity of the information exchanged between the utility company and the consumer.

Optimal scheduling of regional integrated energy systems with hot dry rock enhanced geothermal system based on information gap decision theory

AbstractHot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions.

The Analysis of The Impossible Triangle in Hydrogen Production Technologies

In the current global context, the transformation of the world's energy structure is of utmost importance. Hydrogen energy, being a zero-carbon source, serves as a crucial catalyst for achieving green and low-carbon transition. Despite China being the largest producer of hydrogen energy worldwide, it still heavily relies on coal-based energy systems and consumption patterns. It is essential to acknowledge that an inherent attribute of any single form of energy is its impossibility to fulfill all three aspects simultaneously: economy, cleanliness, and security. By delving into the theoretical connotation of this "energy impossible triangle" and considering factors such as energy security, economic viability, and environmental sustainability associated with various hydrogen production methods; we can analyze a feasible pathway for transforming China's hydrogen energy structure. This transformation necessitates gradually integrating advantages from existing coal-based systems while tailoring production structures according to local conditions. The development of hydrogen energy should be guided by both resource allocation capabilities and actual demand for sustainable growth. Additionally, establishing a comprehensive energy system will greatly facilitate the transition toward more efficient hydrogen production structures.

Integration of Electric Vehicles and Renewable Energy in Indonesia’s Electrical Grid

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia’s grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia.

The primary frequency modulation control strategy based on fuzzy active disturbance rejection control for gas turbines

Gas turbines play a core role in clean energy supply and construction of comprehensive energy system, and the control performance of primary frequency modulation (PFM) of gas turbine has a great impact on the frequency control of the power grid. However, there are some control difficulties in the PFM control of gas turbines, such as the coupling effect of fuel control loop and speed control loop, slow tracking speed and large variation of operating conditions. To relieve the above difficulties, a control strategy based on the fuzzy-modified active disturbance rejection control (F-MADRC) proposed in this paper. Based on the analyses of the parameter stability region for active disturbance rejection control (ADRC), fuzzy tuning rules of parameters for F-MADRC are designed. Finally, the proposed F-MADRC is applied to the PFM system of MS6001B heavy-duty gas turbine. The simulation results show that the gas turbine unit with the proposed method can obtain the best control performance of the PFM with strong ability to deal with system uncertainties. The proposed method shows good engineering application potential.

Energy big data abnormal cluster detection method based on redundant convolution codec

Due to the scattered distribution and poor clustering of abnormal clusters in energy big data, the ability to detect anomalies is poor. Therefore, a high-energy data anomaly clustering detection method based on redundant convolutional encoding is proposed. Quantitative analysis of the coupling characteristics of electrical thermal gas optical time series for multi energy users based on Copula function, and incorporating quantitative values into multi energy feature indicators to extract the energy consumption behavior characteristics of multi energy users. Utilize redundant convolutional codecs to recombine and structurally encode abnormal features of energy big data, and capture multi energy coupling time features using coupling time capsule layers. Then, coupling time features are synthesized through fully connected linear regression layers to generate anomalous clustering feature components, and the energy time series data is then transformed into feature values of the time series in three-dimensional space. Based on this, a comprehensive energy system and massive multi energy user energy big data anomaly clustering analysis are carried out to determine the optimal number of multi energy users. Then, based on linear layers, the electricity heat gas light load characteristic map of multi energy users is transformed into one-dimensional form, and an energy big data anomaly clustering detection model is constructed to complete anomaly detection. The simulation results show that the proposed method has excellent feature clustering performance, detection accuracy above 98.7%, fast convergence speed, and an error rate below 0.1, which has reliable application value.

Renewable fuel production and the impact of hydrogen infrastructure — A case study of the Nordics

Hard-to-electrify sectors will require renewable fuels to facilitate the green transition in the future. Therefore, it is crucial to identify promising production locations while taking into account the local biomass resources, variable renewable energy sources, and the synergies between sectors. In this study, investments and dispatch operations are optimised of a large catalogue of renewable fuel production technologies in the open-source software SpineOpt, and this is soft-linked to the comprehensive energy system model Balmorel. We analyse future production pathways by comparing various levels of hydrogen infrastructure, including large-scale hydrogen storage, and assess system impacts. The results indicate that methanol may provide synergies in its multipurpose use as an early (2030–2040) shipping fuel and later as an aviation fuel through further refining if ammonia becomes more competitive (2050). We furthermore show that a hydrogen infrastructure increases the competitiveness of non-flexible, hydrogen-based fuel production technologies. Offshore electrolysis hubs decrease energy system impacts in scenarios with 105 TWh of Nordic hydrogen export. However, hydrogen export scenarios are much costlier compared to scenarios with no export unless a high hydrogen price is received. Finally, we find that emission taxes in the range of 250–265 €/tCO2 will be necessary for renewable fuels to become competitive.

Flexible Load Optimization Operation Method for Integrated Energy Systems in Parks Considering Demand Response

The ongoing advancements in energy technology are progressively unveiling the high energy utilization benefits of comprehensive energy systems (CIES) in parks. Based on the concept of an energy hub, a CIES model including distributed power supply, gas turbine, and flexible loads is constructed by combining the characteristics of flexible loads in the park that can be leveled, shifted, and curtailed. In light of the demand response mechanism, a method for the low-carbon economic optimization operation of CIES has been developed, focusing on minimizing the total cost. This method utilizes the CPLEX solver to process the algorithm. The approach involves a comparative analysis of load distribution before and after engaging in demand response activities. Such analysis is crucial in understanding the influence of the demand fluctuations mechanism and in determining the most efficient method for adjusting flexible loads. This adjustment aims to further reduce the system costs of CIES. The findings indicate that, under the demand response mechanism, the involvement of flexible loads in CIES operations can significantly enhance the system’s economic efficiency.

Integration Device of Super-Capacitors with Dye-Sensitized Solar Cells

Integration Device of Super-Capacitors with Dye-Sensitized Solar Cells

Day-Ahead scheduling of rural integrated energy systems based on distributionally robust optimization theory

The integration of diverse industries in rural areas is an important step in building a smart countryside and achieving rural revitalization. Aiming at the problem of high energy consumption and pollution in agricultural production parks under the traditional energy structure, a new rural comprehensive energy park distribution robust scheduling model for water containing product breeding greenhouses is proposed in this paper. Firstly, a greenhouse model for aquaculture based on the principle of thermal flow is established. Secondly, considering energy production, consumption and storage, a rural integrated energy system model is established. In response to the uncertainty of residential electricity load, solar radiation, and environmental temperature within the park, a distributed robust optimization model is established using Kullback Leibler divergence. Finally, a numerical simulation is conducted using energy data from a real rural aquaculture park. The results show that compared to agricultural production parks under traditional energy models, the new rural comprehensive energy system model proposed in this paper fully integrates rural resources, reduces daily operating costs by 74.832 %, and reduces total carbon emissions by 33.75 %, providing theoretical support for rural industrial structure adjustment and energy transformation.