Energy Station Research Articles

Solar-assisted biogas system with air-source heat pump for the energy-saving of indoor heating in north China

Abstract To address the challenges associated with winter heating in high-latitude regions of China, solar-assisted biogas heating systems have emerged as the predominant focus of research due to their cost-effectiveness, accessibility, and environmentally friendly attributes. However, traditional solar-assisted biogas heating systems encounter issues of low efficiency and limited practicality resulting from unstable solar radiation and extreme ambient temperatures during the heating season. To improve the economy and stability of the system, this study proposed a novel operational control method for a small-scale energy station system in rural regions of North China, named Solar-Biomass-Air Source Heat Pump Hybrid Heating System (SBHP-HHS). The integration of solar energy, biogas energy, and air-source heat pump (ASHP) systems in this proposed work has shown to create effective complementarity and enhances the production efficiency of the existing system. Test and simulation studies have been carried out for this system. The layout of buildings and equipment within a university campus in Beijing is reconfigured and redesigned, incorporating an ASHP into the existing heat source configuration. To begin with, a mathematical model is established for the complementary heating system that incorporates solar energy, a biogas digester, and an ASHP. Subsequently, a dynamic simulation model is developed using the TRNSYS platform, and a corresponding operational control strategy for the multi-energy complementary heating system is proposed. Dynamic simulation and analysis of the newly implemented system are performed using the TRNSYS platform, focusing on energy flow and thermodynamic performance. Throughout the heating season, the solar-biogas integrated system achieves a remarkable assurance rate of up to 79%. Additionally, ASHP maintains a relatively high heating efficiency, coefficient of performance (COP) reaches 3.02. Finally, an economic evaluation of the multi-energy complementary system was conducted based on the annual cost method. This was compared with the clean approach of using only an ASHP unit. The results indicate that the SBHP-HHS is more economical when the anticipated useful life is 6 years or longer. The results indicate that the proposed can achieve significant energy-saving and carbon-reduction benefits in rural areas, catering to their heating needs.

Research on simulation accuracy of equivalent aggregate electromagnetic model optimization for large renewable energy stations

Abstract The traditional power grid is rapidly transforming into a new power system with high penetration of new energy. Simulation modeling plays an important role in supporting the cognition and research of the new power system. A high proportion of renewable energy is connected to the grid through power electronic components, which puts forward a higher demand for research on the accuracy and stability of power grid simulation modeling. Renewable energy plants usually have a large number of single units, and equivalent aggregation modeling is the usual way to simplify the modeling of renewable energy plants. However, the transmission line power multiplier equivalent module inevitably introduces series impedance into the network, resulting in errors between simulation and actual engineering. Therefore, the study of lossless multiplier elements based on Modified Nodal Analysis (MNA) can improve the simulation accuracy, and it is verified with the simulation results of detailed models in actual scenarios, proving that MNA lossless multiplier elements can optimize the accuracy and stability of equivalent aggregation modeling of large-scale multi-unit renewable energy stations.

Quantum model prediction for frequency regulation of novel power systems which includes a high proportion of energy storage

As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge–discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional–integral–derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy.

Renewable energy utilization and stability through dynamic grid connection strategy and AI-driven solution approach

The connection of renewable energy sources such as wind and solar power into the power grid can significantly reduce both costs and pollution emissions. However, the variability, volatility, and anti-peak regulation characteristics of renewable energy pose significant challenges for power system dispatch. This paper proposes a hybrid economic emission dispatch model (HDEED) for wind–solar–thermal-storage systems, with operational cost and pollution emission as objective functions. The study aims to develop optimal grid-connection strategies for clean energy by utilizing the energy-shifting capability of energy storage systems. This includes strategies based on optimal load fluctuation and optimal operation income for new energy stations. A generalized load fluctuation coefficient is proposed to assess load fluctuations after wind and solar energy integration, comparing and analyzing the performance of energy storage power stations with varying capacities. In terms of algorithm development, the paper proposes the Pelican optimization algorithm with a clustering strategy (POA-CS), specifically tailored to address the complexities of economic emission scheduling. The effectiveness of the proposed strategy and algorithm is validated using an enhanced IEEE-39 bus test system. Results indicate that the generalized load fluctuation coefficient under the optimal grid-connected strategy based on load fluctuation is 21% lower than that of direct grid connection of wind power and photovoltaic, leading to a significant reduction in net load fluctuation. Furthermore, under the optimal grid-connected strategy based on the operation income of new energy stations, the revenue of these plants increased by 22.40% compared to direct grid connections of wind power and photovoltaic systems. The POA-CS algorithm demonstrates superior performance, continuity, and smoothness in obtaining the Pareto optimal boundary under consistent testing conditions.

Identification of the Key Issues and Technical Paths for Intelligent Operation of Water Source Heat Pump Energy Stations Applying Digital Twin Technology

To address the challenges posed by global climate change, developing green energy systems characterized by informatization, digitalization, and intelligence is crucial for achieving carbon neutrality. This article is a research report type paper on water source heat pump (WSHP) energy stations, aiming to use digital twin technology and other information technologies to resolve conflicts between clean energy development and efficient energy utilization. The primary objective of this study is to identify and analyze issues in traditional energy station operations and management systems. Based on this analysis, specific technical solutions are proposed, including pathways for technological research, methodologies, and content. The results provide a comprehensive theoretical framework for the intelligent transformation of energy station systems and essential technical support for the WSHP energy station project in the Hankou Binjiang International Business District. The findings have significant implications for the widespread adoption of WSHP energy stations and the achievement of national carbon neutrality goals.

Energy storage optimal configuration in new energy stations considering battery life cycle

Energy storage optimal configuration in new energy stations considering battery life cycle

Design and Analysis of an Islanded Green Energy Station for Highways Emergency Hospitals at Different Locations in Egypt

Design and Analysis of an Islanded Green Energy Station for Highways Emergency Hospitals at Different Locations in Egypt

Many-objective optimization based mutual feed scheduling for energy system of integrated energy station

The battery storage system of the integrated energy station contains electric vehicles as the distributed energy storage node of the regional power system can help the regional power system to achieve peak shaving and valley filling. This paper proposes a many-objective optimization based mutual feed model of the energy system of the integrated energy station. The model includes multiple modules of electric vehicle (EV), wind turbine (WT), photovoltaic (PV) and battery energy storage system (BESS). BESS was modeled according to the two behaviors of EVs charging and swapping. The model ensures the role of peak shaving and valley filling for regional power, reduces the loss of battery life in the battery storage system of the integrated energy station, and reduces the operating cost of the battery storage system. In the optimized results, the regional power system load peak-to-valley ratio by 28.72 %, and the daily battery charge/discharge life loss is minimized to 0.00036 %. In addition, for known many-objective optimization problems, this paper also proposes a new meta-heuristic many-objective optimization algorithm the non-dominated sorting hunter prey optimization (NSHPO for short) and compared with other four many-objective optimization algorithms by three indexes (Distribution range, Hypervolume index, and Running time). The simulation results show that compared with other algorithms, the HV metrics of NSHPO are lower by about 1.56 %-7.26 %, Spread metrics are better by about 0.39 %-2.73 %, and the proposed NSHPO algorithm is better in terms of convergence and distributivity.

How to better match predicted loads of district heating system: A novel control approach focused on coupling source and network by data-driven methods

Deconstructing the load forecast into appropriate control parameters is essential for effective intelligent heating control. Drawing from the predicted load, a novel heating control approach is proposed, emphasizing the integration of source and network. The heating regulation of the energy station is achieved through controlling the supply water temperature, while the thermal entrance of the heating network is regulated by adjusting the flow rate using valve control. The heat transfer mechanism influencing the regulation of District Heating Systems (DHSs) operation and the alignment of supply and demand are analyzed to deconstruct the four key factors: load, flow, load-flow ratio, and supply water temperature. Multiple Linear Regression (MLR) method is employed to formulate the parameter equations relating to supply water temperature and valve opening. Given the uncertainty interference in DHS in practice, the model undergoes periodic dynamic revisions. The model’s validity is confirmed by the empirical data from three different heating seasons. The average absolute deviation of the calculated supply water temperature and valve opening from the actual DHS is 0.42℃ and 1.12%, respectively. When implemented in an actual system, the approach effectively decreased gas consumption by 29.3% while effectively fulfilling heating requirements.

Pumped-storage hydropower and hydrogen storage for meeting water and energy demand through a hybrid renewable energy system

The majority of the Greek islands have autonomous energy stations, which use fossil fuels to produce electricity in order to meet electricity demand. Also, the water in the network is not fit for consumption. In this paper, the potential development of a hybrid renewable energy system is examined to address the issue of generating drinking water (desalination) and electricity while releasing zero pollutants into the atmosphere. Wind turbines supply wind energy, while an additional amount of energy is stored using pumped-storage hydropower and green hydrogen tanks. These two storage options are investigated for the purpose of storing and distributing clean wind energy in a controlled manner. Three scenarios are investigated. The first scenario only relies on the pumped-storage hydroelectricity technology (88% of the total annual power demand is covered), the second scenario investigates hydrogen storage technology (83% of the total annual electricity demand is covered), and the third scenario uses a hybrid storage solution consisting of pumped-storage hydropower and green hydrogen tanks (95% coverage).

Analysis of the Effect of Meteorological Elements on New Energy Power Prediction Based on Machine Learning

Background: With the gradual construction of new power systems, new energy sources, such as wind and photovoltaic power, will gradually dominate positions in the power supply structure, directly leading the new power system to rely heavily on accurate meteorological forecasts. High-precision and high-resolution meteorological forecasts are important technical methods to improve the safe, stable, and economic operation of the new power system. Objective: Since the analysis of meteorological elements is the basis of meteorological forecasting, in this paper, the effect of different meteorological elements including temperature, relative humidity, air pressure, wind speed, wind direction, and radiation on the performance of power forecasting, was analyzed by using 7 machine learning algorithms in 5 provinces in southern China. Methods: About 5 provinces in southern China were selected as the research objects, and 7 typical machine learning algorithms were applied and compared, including support vector machine (SVM), decision tree (DT), random forest (RFR), K-nearest neighbor (KNN), Linear Regression (LR), Ridge Regression (RR), and Lasso Regression (Lasso R). At the same time, the influence of different meteorological elements, such as temperature, relative humidity, air pressure, wind speed, wind direction, and radiation amount, on the prediction performance of wind power and photovoltaic power was considered. Then, the performance of different regression models was further investigated and analyzed. Results: Based on the data of 10 new energy stations in 5 regions, the research on the prediction performance of 7 machine learning methods shows that the performance of models in different regions varies greatly. Among the 10 selected new energy stations, the RFR model and KNR model have superior overall performance. Conclusion: This study shows how variable importance and prediction accuracy depend on regression methods and climatic variables, providing effective methods to assess the interdependence of meteorological variables and the importance of meteorological variables in predicting output power.

Research on station-network planning of electricity-thermal -cooling regional integrated energy system considering multiple-load clusters and network costs

A reasonable regional integrated energy system (RIES) structure is the key to achieving its economic and efficient operation. However, in actual engineering construction, only the maximum load is considered as a prerequisite for energy design, which leads to waste of energy allocation. In view of this, this article proposes an electricity-thermal-cooling RIES energy stations and networks planning model that coordinates the operation of multiple-user loads, energy networks, and energy stations based on the idea of load clustering. Firstly, an average peak valley difference rate covering electricity, thermal, and cooling loads was proposed to evaluate the load characteristics of the RIES. Secondly, based on the load cluster characteristics and network costs of RIES, a RIES planning optimization framework is proposed, which includes load cluster division, RIES site selection and network planning, and RIES capacity planning. Thirdly, based on the P-median model and load cluster characteristics, establish an RIES location and network layout model. Fourthly, establish an RIES capacity planning model with the goal of annual total cost and carbon emissions. Finally, Dijkstra, improved particle swarm optimization, and VIKOR were used to solve the model. The results showed that the proposed model reduced the annual total cost by 7.13 % and the annual carbon emissions by 3.72 %.

Integrated Two-Level New Energy Monitoring and Big Data Centers: A Comprehensive Approach for Urban Sustainability

With the help of the quick development of big data, the Internet of Things, and other related technologies, the construction of new energy monitoring and big data centres aims to enhance the control capability of urban new energy stations. Inefficient operation and maintenance of new energy stations, as well as chaotic production control, are the reasons behind this action. Given the limitations of the current stage of new energy big data platform construction, the construction of group-level new energy monitoring and big data centres to achieve a high degree of integration and sharing of resources and experience has become an inevitable development of the new energy industry. This project aims to achieve multi-level integration in decision-making from the bottom up by using the background of the construction of the Wenzhou New Energy Two-Level Centre as a guide, organising the business processes for new energy production and equipment control, designing the technical architecture of the platform for cloud-side collaboration, providing guidance for the heterogeneous data processing and analysis of new energy assets in the cloud, and acting as a model for the development of the group-level intelligent platform for the new energy industry.

Bifurcation Analysis and Criterion of Voltage Oscillation Induced by Low Voltage Ride Through Control in Renewable Energy Stations

Bifurcation Analysis and Criterion of Voltage Oscillation Induced by Low Voltage Ride Through Control in Renewable Energy Stations

Modeling and analysis method for carbon emission flow in integrated energy systems considering energy quality

AbstractWhile proposing dual carbon strategy goals, research on carbon reduction in integrated energy systems (IESs) has become a focus of attention. Traditional IES carbon emission flow is mostly based on energy flow, without considering the differences in energy quality. Exergy can be used as a reasonable measure of energy quality. Combining the theory of exergy flow and carbon emission flow, a modeling and analysis method for IES carbon emission flow considering energy quality is proposed. First, exergy flow models for electric and natural gas systems, equivalent exergy flow models for heat systems, and exergy hub models are introduced. Afterward, the exergy branch losses are allocated to the nodes at both ends. A networked modeling method for the carbon emission flow of energy stations is proposed. A unified matrix calculation model for IES carbon emission flow considering energy quality is established. On this basis, an IES carbon emission flow calculation method considering energy quality is proposed. Finally, this method is applied to the test IES, and the results show that the carbon emission flow calculation results reflect the distribution of carbon emissions borne by the effective energy supplied by the source and consumed by the load in the system, taking into account both the quantity and quality of energy. The reasonable allocation of new energy is conducive to synchronously improving the energy efficiency, exergy efficiency, and cleanliness of the system.

Distributionally Robust Management of Hybrid Energy Station Under Exogenous-Endogenous Uncertainties and Bounded Rationality

Distributionally Robust Management of Hybrid Energy Station Under Exogenous-Endogenous Uncertainties and Bounded Rationality

Environmental Awareness: Issues of Taoyuan Natural Gas Energy Station and Algal Reef Ecology

The conflict between energy and ecological issues in Taiwan was explored to find how the coexistence relationship among humans, ecology, and energy could be obtained. To raise more awareness of ecological issues, public furniture was chosen as a medium to review this concept. We took Taoyuan's algal reefs and a natural gas receiving station as representatives of ecological issues and researched the balanced relationship between humans, ecology, and energy. Through a questionnaire survey, the respondents' evaluation of using public furniture for environmental protection was investigated. The results showed that the public understood environmental advocacy and ecological issues, which showed the increasing society's attention to these matters.

Collaborative Control of Reactive Power and Voltage in a Coupled System Considering the Available Reactive Power Margin

This article proposes a hierarchical collaborative reactive power and voltage control method aimed at meeting the internal voltage requirements of a coupled system composed of centralized new energy stations such as wind and solar power and traditional thermal power units, with particular attention paid to the reactive power regulation ability of power units in new energy stations. This method is implemented in two layers: At the transmission layer, the overall reactive power compensation capacity of thermal power units and stations on the transmission lines within the system is determined according to the engineering requirements of different node voltages in the coupled system, the regulation voltages of the high-voltage grid-connected points and the point of common coupling (PCC) of the stations are found, and the actual regulation voltages of the PCCs of the stations are given as commands. At the station layer, based on determining reactive power distribution between power units and continuous reactive power compensation devices within each station, voltage optimization is carried out using the voltage command issued by the transmission layer as a reference. At the same time, consistency coordination between the transmission layer and the station layer is achieved based on the Analytical Target Cascading (ATC) method at different layers. Finally, simulation analysis was conducted on a regional coupled system in Dalian. The results showed that the method proposed in this paper can effectively control the voltage safety requirements of each region and PCC in the coupled system and, to some extent, improve the economic efficiency of system operation.

Multi-objective optimization study of regional integrated energy systems coupled with renewable energy, energy storage, and inter-station energy sharing

Globally, countries have established timelines and technological pathways towards achieving "carbon neutrality". Regional integrated energy systems, as an efficient and clean mode of energy provision, are particularly suitable for supplying various forms of energy to building users. However, due to the complexity of their structure and multiple energy flow couplings, regional integrated energy systems face challenges in system optimization, warranting further exploration in design optimization and benefit analysis. Therefore, a regional integrated energy system was established, integrating renewable energy, energy storage, and power/thermal sharing between stations. A multi-objective optimization model for the regional integrated energy system was established, targeting economic benefits, carbon reduction, and reliability. Overall benefits of the internal energy stations in the regional integrated energy system were meticulously analyzed, considering system benefits, inter-station energy sharing, and energy storage. Research findings indicate, the regional integrated energy system constructed in this study exhibited superior energy-saving, carbon reduction, and independence, compared to other integrated energy systems. For the proposed system, the annual total cost was 38.41 CNY/m2, primary energy consumption stood at 16.26 kWh/m2, carbon emission was recorded at 4.33 kg/m2, and the system interaction power measured 9.99 kWh/m2. Final, this study provides theoretical support for the application and promotion of regional integrated energy systems.

Study on overvoltage suppression method of phase modulation of hydroelectric generating units and its cooperative optimization in new energy grid

When a significant portion of renewable energy is transmitted to the power grid through high voltage direct current, when the monopolar blocking occurs, as a result of the converter station’s filter and reactive power compensation equipment’s influence, overvoltage will be generated, which will cause harm to the new energy station, and even lead to large-scale new energy grid disconnection. This paper initially examines the mechanism behind overvoltage in a new energy power grid and then puts forward a method to restrain overvoltage by using phase modulation operation of hydroelectric generating units. To improve the phase modulation capability of hydroelectric generating units and optimize the utilization of new energy sources, this paper proposes a particle swarm optimization to determine the optimal phase modulation capacity. Finally, this paper builds an example of the PSASP platform to verify the correctness and effectiveness of collaborative optimization.