Large-scale Renewable Energy Integration Research Articles

Feedforward Control Strategy of a DC-DC Converter for an Off-Grid Hydrogen Production System Based on a Linear Extended State Observer and Super-Twisting Sliding Mode Control

With the large-scale integration of renewable energy into off-grid DC systems, the stability issues caused by their fluctuations have become increasingly prominent. The dual active bridge (DAB) converter, as a DC-DC converter suitable for high power and high voltage level off-grid DC systems, plays a crucial role in maintaining and regulating grid stability through its control methods. However, the existing control methods for DAB are inadequate: linear control fails to meet dynamic response requirements, while nonlinear control relies on detailed model structures and parameters, making the control design complex and less accurate. To address this issue, this paper proposes a feedforward control strategy for a DC-DC converter in an off-grid hydrogen production system based on a linear extended state observer (LESO) and super-twisting sliding mode control (STSMC). Firstly, a reduced-order simplified model of the DAB was constructed through the structure of DAB. Then, based on the reduced-order simplified model, a feedforward control based on LESO and STSMC was designed, and its stability was analyzed. Finally, a simulation comparison of PI, LESO + terminal sliding mode control (TSMC), and LESO + STSMC control methods was conducted in a DC off-grid hydrogen production system. The results verified the proposed control method’s enhancement of the DAB’s rapid dynamic response capability and the system’s transient stability.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

How to choose mobile energy storage or fixed energy storage in high proportion renewable energy scenarios: Evidence in China

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system.

Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multi-receptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis.

Comprehensive analysis of the economic, environmental and social impacts of large-scale renewable energy integration

Comprehensive analysis of the economic, environmental and social impacts of large-scale renewable energy integration

Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration

Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.

Multi-Stage Coordinated Planning for Transmission and Energy Storage Considering Large-Scale Renewable Energy Integration

Due to the large-scale integration of renewable energy and the rapid growth of peak load demand, it is necessary to comprehensively consider the construction of various resources to increase the acceptance capacity of renewable energy and meet power balance conditions. However, traditional grid planning methods can only plan transmission lines, often resulting in low utilization rates of newly constructed lines. Additionally, static planning methods can only address single-target scenarios and cannot cope with dynamic growth in load and renewable energy. To address these issues, this paper proposes a multi-stage collaborative planning method for transmission networks and energy storage. This method considers the non-line substitution effect of energy storage resources and their characterization methods. It establishes the coupling relationship between resources across different planning stages to achieve coordinated multi-stage planning for transmission networks and energy storage. Based on the IEEE-24 node system and a case study in a northern province of China, the results show that the proposed method reduces investment costs by approximately 30% compared to static planning methods and by about 7.79% compared to conventional grid planning methods. Furthermore, this method can accommodate more renewable energy.

Experimental study on peak shaving during preheating combustion for different coal ranks: Thermal modification and variable load operation

With the large-scale integration of renewable energy into the power grid, coal-fired power plants shoulder an enormous burden of peak shaving. In this study, the variable load characteristics of different coal ranks were extensively investigated on a 40 kWth down-fired test platform. Moreover, the preheating modification of fuel at ultra-low loads was investigated. A comprehensive evaluation coefficient (Eph) was established to characterize the reactivity of different coal ranks at wide loads. The determination criterion of combustion stability for variable load processes was modified. The results show that the preheating modification of different coal ranks was significant, especially for lower-activity coals such as anthracite and lean coals. The minimum loads after modification were 25 %, 25 %, 35 % and 40 % for ARG (lignite), SM (bituminous), RG (lean coal) and SH (anthracite), respectively. Furthermore, the preheating modification had a positive effect on increasing the variable load rate, which was positively correlated with Eph for different coal ranks. The maximum variable load rates after modification for ARG, SM, RG and SH were 2.78 %/min, 3.13 %/min, 1.79 %/min and 1.15 %/min, which corresponded to the optimum load steps of 25–30 %. The response rate of NO and N2O with time in the low-rank coal is faster than that in the high-rank coal during the variable load process, which is consistent with the trend of the variable load rate. In addition, the conversion of nitrogen oxides decreases and then increases with load, and the inflection points corresponding to AGB, SM, RG and SH are 40 %, 38 %, 70 % and 80 %, respectively. The NOx emissions were less than 335 mg/m3 (@6 % O2) for the different coal ranks during load changes.

Impact of large-scale renewable energy integration on the grid voltage stability

Nowadays, the production of renewable energy is increasing rapidly due to its enormous potential and environmental advantages. Still, the addition of renewable energy to the grid has resulted in some volatility in the electrical system. In this work, the national grid of Ethiopia is used as an example to examine the impact of significant wind power integration on grid stability. In particular, issues with voltage stability in both steady-state and emergency scenarios are taken into account for the network. The dynamic modeling of the wind farm is taken into consideration for contingency-based analysis, and the whole nation's power network, including the largest wind farm in the nation, Adama-II, is modeled for base-case analysis using MATLAB's built-in PSAT software. The result indicates that the bus voltages are within an acceptable standard voltage range for the base-case system. Nevertheless, the wind farm will be disconnected from the network in compliance with the global manufacturer's guideline in the case of a three-phase fault at the network point of common coupling (PCC), where the voltage at PCC is equivalent to 0.045 per unit voltage. The results also demonstrate that the voltage profile throughout the whole nation power network is lower when there is a problem at PCC. As a result, as Ethiopia is building several huge wind farms, it is advised that fault detection and control methods be thoroughly designed before connecting the major wind farms to the grid.

An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management.

Topological Structure and Control Strategy of E-UPFC

This article proposes a unified power flow controller with energy (E-UPFC) installed at the renewable energy grid connection node of the transmission network. Compared with other unified power flow controllers (UPFCs), the proposed E-UPFC can not only regulate the power flow on its connected transmission lines, but also suppress the power fluctuations of grid-connected nodes injected with large-scale renewable energy. At the same time, with the installation position of E-UPFC transferred from the transmission line to the node, the original stiff node can be transformed into a flexible node that can regulate the injected power flexibly. First, the PV grid-connected system with E-UPFC is introduced, and its principle of power flow regulation is detailed. In addition, the topology, mathematical modeling, and control strategies of E-UPFC are discussed. Finally, the E-UPFC is applied to the IEEE 3-generator 9-bus system with large-scale renewable energy integration in Matlab/Simulink in order to verify the correctness and feasibility of E-UPFC and its control strategies.

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.

Static-Voltage-Stability Analysis of Renewable Energy-Integrated Distribution Power System Based on Impedance Model Index

Static-voltage stability has become one of the most significant risks faced by large-scale renewable energy integration. However, traditional methods for static-voltage-stability analysis are often overly complex. This paper constructs an equivalent impedance model for renewable energy-integrated distribution power systems, proposing a static-voltage analysis method for renewable energy-integrated distribution power systems based on an impedance model index. This method has been verified to be applicable not only to a renewable energy single-infeed system but also to a multi-infeed system. Furthermore, an analysis is conducted on the influence of the integration capacity, location of renewable energy, and the topology of networks on the impedance model index, indicating that a higher impedance model index corresponds to greater static-voltage-stability margins in the system. Hence, during the planning of renewable energy integration, the plan with the highest impedance model index should be selected. Finally, the accuracy of the analysis method and conclusions in this paper was validated based on the IEEE 14-node system.

Assessment of the synthetic inertial response of an actual solar PV power plant

As the large-scale integration of renewable energy grows, inertia in power systems is reduced, which causes the rate of change of frequency to increase and worsens the frequency nadir during disturbances. There is, therefore, an urgent need to accelerate the development of international standards and regulations that require solar PV systems and other renewable installations to also contribute to stability in the network by emulating inertia. Spain is a pioneer in this field: it is currently the only country that includes the synthetic inertia technical requirement in its grid code. In this context, the present paper analyzes the synthetic inertial response of a PV system model that forms part of an actual PV power plant in Spain under two scenarios, according to its grid code: i) when measurements are conducted at the power conversion PV system level and hence only its control algorithms are executed; and ii) when measurements are conducted at the power plant level and the power plant controller also influences the response of the entire installation. Among its aims, this paper seeks to thoroughly assess the response of an actual PV power conversion system that has the capability of actively contributing to frequency regulation in a network and to critically analyze the suitability of having a power plant controller when the PV facility contributes to power-frequency control by means of an inertial response. The results reveal, at the power conversion PV system level, that the inertia emulation module significantly reduces the response times of the installation to changes in frequency -by more than 68% in the worst case under frequency increases, and by 73% under frequency decreases, thus contributing to more rapid power-frequency regulation in the network. However, at the power plant level, the power plant controller disturbs the PV power conversion system’s behavior and causes the opposite effect: response times increase -up to more than 7% in the most unfavorable case when the inertia module is enabled.

Optimal scheduling for wind-solar-hydro hybrid generation system with cascade hydropower considering regulation energy storage requirements

Large-scale integration of renewable energy into the grid can lead to significant changes in the net load, peak-to-valley difference, peak and valley occurrence time of the power system. As a result, the power of hydropower plants must take a rapid adjustment response. Aiming at the coordinated operation of multiple energy sources, such as wind power, solar power, cascade hydropower station and energy storage pumping station, a coordinated scheduling model is proposed which can fully improve the consumption capacity of wind and solar power by aiming at the maximum power generation, minimum net load fluctuation and minimum wind and solar abandonment. Through the configuration of three different pumping station capacities, the influence of energy storage pumping station capacity on the complementary power generation system is analyzed. When the pumping station capacity is large enough, the output of the wind and solar can be completely consumed. The studies show that the cascade power station and pump energy storage regulation have a strong net load filling valley effect, which can effectively reduce the impact of wind and solar access on system operation, maintain the efficient and stable operation of the unit, and ensure the absorption rate of renewable energy.

Vehicle-to-Grid Power in Danish Electric Power Systems

The integration of renewable energy systems is often constrained by the variable nature of their output. This demands for the services of storing the electricity generated from most of the renewable energy sources. Vehicle-to-grid (V2G) power could use the inherent energy storage of electric vehicles and its quick response time to balance and stabilize a power system with fluctuating power. This paper outlines the use of battery electric vehicles in supporting large-scale integration of renewable energy in the Danish electric power systems. The reserve power requirements for a high renewable energy penetration could be met by an amount of V2G based electric vehicles less than 10% of the total vehicle need in Denmark. The participation of electric vehicle in ancillary services would earn significant revenues to the vehicle owner. The power balancing services of electric vehicles in an electricity network with a large variable renewable generation in Denmark seems feasible and realistic.

Network for the Large-Scale Integration of Renewable Energies in Electrical Systems (RIBIERSE-CYTED, 723RT0150): Results for 2023

The RIBIERSE-CYTED network, i.e., the network for the large-scale integration of renewable energies in electrical systems (723RT0150) (2023-2026) is a hub for researchers and technologists belonging to Ibero-American universities, companies, and local administrations. This network promotes cross-training, mobility between centers, and the dissemination and implementation of technical and training activities aimed at analyzing and developing opportunities for the maximal integration of renewable resources, seeking a more sustainable energy model while allowing for the increased use of renewable resources and electric mobility (e-mobility).

Dynamic characteristics and economic analysis of a coal-fired power plant integrated with molten salt thermal energy storage for improving peaking capacity

Improving the peaking capacity of coal-fired units is imperative to ensure the stability of the power grid, thus facilitating the grid integration and popularization of large-scale renewable energy. To address this issue, this paper introduces a new concept that combines molten salt energy storage with coal-fired power plants. The proposed design consists of extracting a portion of steam from the turbine side and adjusting the extracted steam mass flow rate by adjusting the valve opening to improve the dynamic characteristics of a coal-fired power plant in terms of both increasing the peaking depth and peaking speed. A coal-fired boiler with integrated thermal energy storage was dynamically modeled using Dymola and its accuracy was verified. The results show that the highest equivalent round-trip efficiency is achieved by extracting from the main steam and discharging into the feedwater during charging process and from reheater1 and discharging into the feedwater during discharging process; The peaking potential during charging and discharging is 12.83 % Pe and 6.86 % Pe respectively, with maximum peaking rates of 9.27 % Pe/min and 5.11 % Pe/min respectively, and the optimal peaking method is also proposed according to the different peaking situations. The total cost of equipment and materials to retrofit the conventional coal-fired units was 19,948,193 USD and the levelized cost of delivery was 151.29 USD/MWh.

Multi-Objective Short-Term Optimal Dispatching of Cascade Hydro–Wind–Solar–Thermal Hybrid Generation System with Pumped Storage Hydropower

Aiming to mitigate the impact of power fluctuation caused by large-scale renewable energy integration, coupled with a high rate of wind and solar power abandonment, the multi-objective optimal dispatching of a cascade hydro–wind–solar–thermal hybrid generation system with pumped storage hydropower (PSH) is proposed in this paper. Based on the proposed system, the scheduling operation strategy takes into account the complex restrictions of cascade hydropower as well as the flexibility of the PSH. According to various scenarios, the NSGA-II approach is adopted to address the optimization problem, minimizing the system’s residual load variation and operation cost. The Pareto solution sets are contrasted and evaluated, applying the TOPSIS with CRITIC weighting. Additionally, the scheduling output of thermal power, cascade hydropower, and PSH is given in terms of different scenarios. The results demonstrate that the allocation of PSH to a hybrid energy system can significantly reduce the operation cost and the fluctuation in the residual load.