Search

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction. [ABSTRACT FROM AUTHOR]

With the modernization and intelligent development of agriculture, the energy demand in rural areas continues to increase, which leads to an increased operational burden on the existing rural distribution network. Integrated energy stations (IESs), in rural areas, use renewable energy sources such as biogas, wind power, and photovoltaic as energy inputs, which can fully improve energy efficiency and help reduce the operating load and peak valley difference of rural distribution networks. In this paper, a multistage planning model is proposed for rural distribution networks with IESs based on the robust optimization method. Firstly, a rural distribution network operation framework with IESs is proposed, and a mathematical model of rural IESs is built based on the energy hub (EH). Then, the multistage robust planning model of rural distribution net-works with IESs is developed and typical scenarios of stochastic source and load are generated based on improved k-means. An iterative solution method fora two-stage robust optimization method is proposed based on the nested column constraint generation (NC&CG) algorithm. Finally, the effectiveness of the presented model and solution approach is assessed through case studies on a modified IEEE 33-node distribution network and a real 152-node distribution network. [ABSTRACT FROM AUTHOR]

Large-scale application of new energies is a major approach to achieving carbon peaking and carbon neutralization. Conducting research on the safety risk prevention and control strategy of new energy application helps in the establishment of a monitoring, risk prevention, and guarantee system for new energy application and promotes the high-quality development of related industries. Currently, new energy application in China involves various types of emerging infrastructures and faces challenges such as complex causes of safety accidents, incomplete understanding of safety risks, inadequate safety prevention and control technology systems, and lagging construction of a safety regulatory system. This study analyzes the current status and safety situation of new energy application in China and delves into the safety risk prevention and control issues faced by new energy application infrastructures such as charging/swapping stations, energy storage stations, hydrogen fuelling stations, and integrated energy stations. It also summarizes the current situation and experiences abroad regarding safety risk prevention and control. To establish a comprehensive system for preventing and controlling safety accidents before, during, and after they occur, and to enhance the safety risk management and regulatory capabilities of all stakeholders, this study constructs a strategic framework for safety risk prevention and control for new energy application. It clarifies the key tasks in terms of management and technology development and proposes medium- to long-term engineering and technological support projects. Furthermore, we propose development suggestions in terms of top-level design, governance standards, regulatory platforms, technological research and development, talent cultivation, and ecological co-construction, thus to promote the safety research and application of new energies.

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the modernization and intelligent development of agriculture, the energy demand in rural areas continues to increase, which leads to an increased operational burden on the existing rural distribution network. Integrated energy stations (IESs), in rural areas, use renewable energy sources such as biogas, wind power, and photovoltaic as energy inputs, which can fully improve energy efficiency and help reduce the operating load and peak valley difference of rural distribution networks. In this paper, a multistage planning model is proposed for rural distribution networks with IESs based on the robust optimization method. Firstly, a rural distribution network operation framework with IESs is proposed, and a mathematical model of rural IESs is built based on the energy hub (EH). Then, the multistage robust planning model of rural distribution net-works with IESs is developed and typical scenarios of stochastic source and load are generated based on improved k-means. An iterative solution method for a two-stage robust optimization method is proposed based on the nested column constraint generation (NC&CG) algorithm. Finally, the effectiveness of the presented model and solution approach is assessed through case studies on a modified IEEE 33-node distribution network and a real 152-node distribution network.

In recent years, countries around the world have been placing increasing emphasis on energy saving and emission reduction. As part of the energy system, integrated energy stations play an important role in the low-carbon transition of the energy sector. However, most of the existing studies on carbon emissions from integrated energy stations focus on the operation phase or a single module within it and lack analysis and prediction of carbon emissions from all phases of the integrated energy station. In this paper, based on the perspective of life cycle assessment, system dynamics is used to establish a life cycle carbon emission model for integrated energy stations from four phases: production & materialization, construction, operation & maintenance, and disposal & recycling. This paper takes wind power integrated energy stations as an example, simulates the characteristics of the life cycle carbon emissions of integrated energy stations, and identifies the key factors affecting the carbon emissions. The simulation results show that the carbon emissions of the integrated energy station are mainly concentrated in the production and operation of the energy station, and the increase in the service life of the energy station will not have a significant impact on the total carbon emissions. This paper provides the theoretical guidance and decision-making basis for the formulation of low-carbon emission reduction measures for integrated energy stations.

Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions. [ABSTRACT FROM AUTHOR]

Regarding the need to decrease carbon emissions, the electric vehicle (EV) industry is growing rapidly in China; the charging needs of EVs require the number of EV charging stations to grow significantly. Therefore, many refueling stations have been modified to integrated energy stations, which contain photovoltaic systems. The key issue in current times is to figure out how to operate these integrated energy stations in an efficient way. Therefore, an effective scheduling model is needed to operate an integrated energy station. Photovoltaic (PV) and energy storage systems are integrated into EV charging stations to transform them into integrated energy stations (PE-IES). Considering the demand for EV charging during different time periods, the PV output, the loss rate of energy storage systems, the load status of regional grids, and the dynamic electricity prices, a multi-objective optimization scheduling model was established for operating integrated energy stations that are connected to a regional grid. The model aims to simultaneously maximize the daily profits of the PE-IES, minimize the daily loss rate of the energy storage system, and minimize the peak-to-valley difference of the load in the regional grid. To validate the effectiveness of the model, simulation experiments under three different scenarios for the PE-IES were conducted in this research. Each object weight was determined using the entropy weight method, and the optimal solution was selected from the Pareto solution set using an order-preference technique according to the similarity to an ideal solution (TOPSIS). The results demonstrate that, compared to traditional charging stations, the daily revenue of the PE-IES stations increases by 26.61%, and the peak-to-valley difference of the power load in the regional grid decreases by 30.54%, respectively. The effectiveness of PE-IES is therefore demonstrated. Furthermore, to solve the complex optimization problem for PE-IES, a novel multi-objective optimization algorithm based on multiple update strategies (MOMUS) was proposed in this paper. To evaluate the performance of the MOMUS, a detailed comparison with seven other algorithms was demonstrated. These results indicate that our algorithm exhibits an outstanding performance in solving this optimization problem, and that it is capable of generating high-quality optimal solutions.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Taking the minimization of annualized cost as the objective function, as well as introducing environmental penalty cost, a bi-level optimal configuration model of integrated energy station is established to realize the collaborative optimization in the planning and operation stages. • Two different programming methods, the column-and-constraint generation (C&CG) and particle swarm optimization coordination with mixed-linear integer programming (PSO-MILP), are proposed to solve the model. The comparison and analysis of the programming results are illustrated in solving the two-stage robust planning problem. • Three operation modes of self-adaption, FEL and FTL are comprehensively considered to optimize the configuration of integrated energy station. On this basis, the sensitivity of heat-to-electric ratio (HPR) of CHP units and electric storage to the planning results are analyzed. • The planning results of integrated energy station are evaluated based on system dynamics (SD), which has certain guidance for the actual project. Operation modes of combined heat and power (CHP) units are closely related to the economic benefits of energy application in integrated energy station. In this paper, a novel bi-level optimal configuration method is proposed considering different operation modes of CHP, and a life-cycle assessment system on economic and environmental benefits is constructed using system dynamics (SD) method. Firstly, the basic structure and operation issues of a typical integrated energy station employing CHP are addressed. Secondly, the bi-level collaborative optimization model of the integrated energy station is constructed to minimize the annualized cost considering three operating modes, namely following the thermal load (FEL), following the electric load (FTL) and switching adaptively between FEL and FTL. Then, the model is solved by the column-and-constraint generation and particle swarm optimization coordination with mixed-linear integer programming respectively. Subsequently, the sensitivity of CHP units heat-to-electric ratio and electric storage to the planning results are analyzed in detail. Finally, case studies are carried out based on an actual integrated energy station. The optimization results show that, compared with FEL and FTL operating modes of the CHP units, the annualized cost of the system under adaptive operation mode is reduced by 26.5% and 42.8%, respectively. Besides, the assessment of the optimized system indicates that the total profit and carbon dioxide emission reduction of the station during its life-cycle are expected to reach 28.53 million CNY and 25.33 ten thousand tons respectively, which provides a reference for decision-making and a basis for flexible operation. [ABSTRACT FROM AUTHOR]

To respond to the escalating contradiction between improving economy and reducing carbon emissions, a low-carbon optimal dispatch model of integrated energy station (IES) considering carbon capture and hydrogen demand is proposed. First, a wind-photovoltaic-hydrogen power (WPHP) integration model is built, giving an effective way of renewable energy accommodating and reliable hydrogen supplying of IES. The WPHP significantly improves system economy and decreases the renewable energy curtailment, especially with a high proportion integrated; Then, a coupling model of carbon capture system (CCS) and power-to-gas (P2G) is built. The model resolves the problem of carbon source of P2G and reduces the carbon net emissions, changing the carbon trading cost from expenditure to profit; Next, an integrated demand response (IDR) model is built covering electricity, gas, cooling, hydrogen load as well as the heating load with different heat grade requirements. The IDR further improves multi-energy users' satisfaction, the economy and stability of IES by decreasing the peak-valley difference and increasing load ratio of multi-energy loads. Finally, an optimal dispatch model of IES is built with the objective of minimizing the cost of economic, carbon emission trading and users' dissatisfaction, and the effectiveness of the model is verified by a numerical example. • A wind-photovoltaic-hydrogen power integration model is built. • A coupling model of carbon capture system and power to gas is built. • An integrated demand response model is constructed covering multi-energy loads. • An optimal dispatch model of integrated energy station is proposed considering carbon capture and hydrogen demand. [ABSTRACT FROM AUTHOR]

To achieve its carbon neutrality goal, China has invested broadly in energy infrastructure and the emerging integrated energy stations (IESs) projects will bring enormous opportunities. Accurate carbon emission accounting for IESs is challenging in view of the complexity of the manufacturing process and uncertainty in construction and operation processes. To overcome these challenges, this paper develops a novel quantitative carbon footprint analysis model for IESs from a lifecycle perspective, with production and materialization, construction, operation and maintenance, and disposal and recycling phases considered. The method is applied on a 110 kV wind power IES project in China, to analyze and calculate lifecycle carbon emissions, identify the key influence factors of carbon footprints and provide suggestions for carbon reduction. The findings can identify key influence factors and provide suggestions for carbon reduction for the development of IES projects.

• An integrated energy station optimal planning model is proposed. • Combined PtG and gas-fired unit equipped with CCS are considered in the planning model. • An improved EH formulation is proposed to describe the closed loops of energy flows and the energy output from the integrated energy station to external multi-energy networks. • The sensitive analysis are presented in case studies. To deal with the menace of global warming and energy crisis, low-carbon and sustainable energy consumption patterns are receiving more and more attention. Carbon capture and storage technologies, renewable energy sources and multi-energy systems are important measures to achieve low-carbon emission and sustainable energy development. In multi-energy systems, an integrated energy station is a key to realize the coupled and coordinated operation of multi-energy networks. This paper proposes an integrated energy station optimal planning model which considers the combined power-to-gas and gas-fired units equipped with carbon capture systems. To describe the closed loops of energy flows in the proposed integrated energy station, this paper presents an improved energy hub formulation based on the directed acyclic graph. Moreover, the output ports of the integrated energy station is connected to multi-energy networks to sell excess energy. The improved energy hub formulation is applied in the above-mentioned optimal planning model. The objective of the proposed optimal planning model is to minimize the total cost of integrated energy station, which consists of the investment cost, energy purchase cost, energy sales revenue and carbon cost. Finally, case studies are carried out to discuss and validate the effectiveness of the proposed optimal planning model and the improved energy hub formulation. [ABSTRACT FROM AUTHOR]

• A novel structure of the smart multi-energy system is proposed. • Transportation, natural gas, and active distribution networks are jointly considered. • Integrated energy stations are served to achieve the synergies of various energies. • A mixed user equilibrium is developed to obtain both routing and charging guidance. As the energy demand for multiple purposes increases, smart multi-energy systems have become the trend in the development of cities. In such an intelligent environment, it is possible and imperative to consider the traffic flow guidance for electric and non-electric vehicles in the urban transportation network. However, this has not yet been considered in the challenges of planning multi-energy systems. Based on these considerations, a novel optimal planning framework is proposed in the present study, that takes into account the coupling of transportation, natural gas and active distribution networks. Firstly, a novel structure of integrated energy stations is proposed, which serves as coupling components where various sources of energy can be converted, distributed, and stored. The model optimizes the investment and operational strategies of renewable energy generation, energy converters, storage devices and charging facilities in a coordinated way. To capture the impact of traffic flow, a mixed user equilibrium state was established. Thus, the route selections for non-electric vehicles and the navigations of electric vehicles to charging facilities could both be incorporated into the model. Since the proposed planning model is a mixed-integer nonlinear program problem, convex relaxation was conducted to transform the model formulations into a mixed-integer second order cone program version. Finally, four cases were designed and tested in a practical urban area. The quantitative comparison revealed that unguided traffic flow will increase the traffic costs by more than 23%. Moreover, when both charging and routing guidance are considered the energy losses will decrease by at least 5.6%. [ABSTRACT FROM AUTHOR]