Renewable Energy Grid Integration Research Articles

Flexible heat and power load control of subcritical heating units based on energy demand-supply balance

To fulfill the requirement for enhancing the flexibility of heating units within the framework of renewable energy grid integration. In this study, a proposed optimal control strategy for heating units borrows the heat extraction output to perform rapid load changes; the boiler heat supply within the unit is clearly differentiated from the turbine energy demand, and formulate corresponding control signals. The coordinated control of the boiler and turbine based on the energy demand-supply balance of the unit as secondary control for the final load accuracy, ensuring its safe and stable operation. To further enhance the control performance, an improved sparrow optimization algorithm with butterfly search is designed to adjust the controller parameters. Finally, field tests on a 300MW subcritical heating unit reveal that the automatic generation control performance index is improved by 32.16% compared to the traditional coordinated control strategy, while taking less impact on the heat user. These results validate the exceptional performance and significant practical value of the optimized control strategy.

Synergistic frameworks for sensor fault isolation and accommodation in grid-side converters

The stable and reliable operation of grid-integrated renewable energy systems requires advanced control and coordination of grid-side converters (GSCs), utilizing the feedback measurements of voltage and current sensors from both the direct current (DC) and alternating current (AC) sides of the converter. However, the effective operation of the converter is susceptible to sensor failures or divergence from their proper operation. Although sensor fault detection algorithms are usually effective under abrupt faults, the fault propagation effect caused by the physical interconnection between the DC and AC sides of the converter may limit the performance of the sensor fault isolation process in revealing the exact location of a potential faulty sensor. Therefore, this work proposes a robust, model-based fault isolation and accommodation scheme. Specifically, a synergistic sensor fault isolation framework based on adaptive estimation schemes is proposed for both single and multiple faults in the DC voltage and AC current sensors, considering modeling uncertainty and measurement noise. The performance analysis in terms of stability, learning capability, and fault isolability is rigorously examined. An accommodation scheme based on a virtual sensor utilizing dynamic sensor fault estimation with real-time learning capabilities is applied to a GSC. Finally, the performance of the proposed fault isolation and accommodation scheme is evaluated through simulation analysis under several scenarios involving single and multiple sensor faults.

Reimagining energy infrastructure for justice: Power, politics, and institutional work in India’s 2.05 GW Pavagada solar park

India has positioned itself as a leader in transitioning its energy sector to renewable sources, with ambitious targets and policies in place. Large-scale grid-integrated renewable energy plants have been identified as the most efficient option. Scholarly arguments differ regarding the impacts of renewables-led transitions, with some emphasizing positive environmental, economic, and social outcomes, while others highlight the potential for reinforcing asymmetrical power relations and unjust outcomes. Viewing the energy transition as a socio-technical process, we utilize a recent conceptualisation of the relationship between institutional work and infrastructures to analyze the unfolding power dynamics and its influence on socially just outcomes. Theoretically, we draw on the ‘Triple-Re’ framework, which distinguishes three interrelated domains of institutional work in socio-technical transitions: reimagining, recoding, and reconfiguring of infrastructures. Through a process tracing approach, we study the planning and realization of India's Pavagada Solar Park to understand the interactions among actors, institutions, policies, and material contexts at various spatial and temporal scales. Field observations, interviews, and archival research reveal how discursive dynamics and the recoding of rules and policies have facilitated land and resource mobilization, resulting in changes to ownership models, local infrastructure, land use, ecosystems, and occupational structures. We argue that recognizing and understanding these outcomes are crucial for achieving socially just outcomes in the context of renewable energy infrastructure.

IoT based solar power forecasting using SSA-ELM technique

Abstract The optimizing of renewable energy use and grid integration relies on accurate solar power predictions. In order to predict the amount of power that solar photovoltaic (PV) systems would produce inside an IoT framework, this study suggests a new method that integrates Singular Spectrum Analysis (SSA) with Extreme Learning Machine technology. The SSA algorithm makes sense of solar power data by separating it into its component parts, such as trend, seasonality, and noise. The ELM model, a quick and effective feedforward neural network with a single hidden layer, takes these broken-down parts as input characteristics. In order to enhance the accuracy of solar power forecasts, the suggested strategy combines the decomposition skills of SSA with the predictive capability of ELM. Data acquired by solar PV sensors is input into the IoT-based forecasting model, which then undergoes preprocessing with SSA, feature extraction, model training with ELM, and performance evaluation. The SSA-ELM methodology has been successfully tested on real solar power data and has shown promising results in terms of accuracy measures such as low mean absolute error and mean absolute percentage error. By implementing the suggested method, accurate projections of solar output can be made, leading to better energy management, lower costs, and the smooth incorporation of renewables into smart grids. A dependable and computationally efficient method for solar forecasting in Internet of Things applications is provided by the combination of SSA and ELM.

Optimal Scheduling of a Cascade Hydropower Energy Storage System for Solar and Wind Energy Accommodation

The massive grid integration of renewable energy necessitates frequent and rapid response of hydropower output, which has brought enormous challenges to the hydropower operation and new opportunities for hydropower development. To investigate feasible solutions for complementary systems to cope with the energy transition in the context of the constantly changing role of the hydropower plant and the rapid evolution of wind and solar power, the short-term coordinated scheduling model is developed for the wind–solar–hydro hybrid pumped storage (WSHPS) system with peak shaving operation. The effects of different reservoir inflow conditions, different wind and solar power forecast output, and installed capacity of pumping station on the performance of WSHPS system are analyzed. The results show that compared with the wind–solar–hydro hybrid (WSH) system, the total power generation of the WSHPS system in the dry, normal, and wet year increased by 10.69%, 11.40%, and 11.27% respectively. The solar curtailment decreased by 68.97%, 61.61%, and 48.43%, respectively, and the wind curtailment decreased by 76.14%, 58.48%, and 50.91%, respectively. The high proportion of wind and solar energy connected to the grid in summer leads to large net load fluctuations and serious energy curtailment. The increase in the installed capacity of the pumping station will promote the consumption of wind and solar energy in the WSHPS system. The model proposed in this paper can improve the operational flexibility of hydropower station and promote the consumption of wind and solar energy, which provides a reference for the research of cascade hydropower energy storage system.

Navigating legal pathways for accelerating urban energy transition: A comprehensive deep analysis of photovoltaic power prediction and policy instruments

This research proposes a navigation method that explores how to accelerate urban energy transformation through legal means from the perspective of energy law, in order to improve the accuracy of photovoltaic output power prediction in sustainable energy transformation. Firstly, a deep learning model based on adversarial autoencoder (AAE) architecture was introduced to predict photovoltaic output power according to clean environment policies. Secondly, in order to effectively fine tune the parameters of AAE, a heuristic optimization method based on the Imperial Competition Algorithm was proposed to optimize the AAE configuration to adapt to the unique characteristics of photovoltaic systems, thereby improving prediction accuracy. In addition, the study analyzed the challenges and opportunities faced by current urban energy transformation, and then proposed sustainable ways to accelerate urban energy transformation from the aspects of formulating transformation plans, promoting technological innovation, promoting green transportation, promoting building energy conservation, and developing circular economy. The synergistic effect of deep learning and heuristic optimization, combined with strict stability and sensitivity assessment, puts this research at the forefront of advancing photovoltaic output power prediction methods, which is of great significance for optimizing renewable energy utilization and grid integration.

Ampacity forecasting using neural networks

Ampacity techniques have been used by Distributor System Operators (DSO) and Transport System Operators (TSO) in order to increase the static rate of transport and distribution infrastructures, especially those who are used for the grid integration of renewable energy. One of the main drawbacks of this technique is related with the fact that DSO and TSO need to do some planning tasks in advance. In order to perform a previous planning it is compulsory to forecast the weather conditions in the short-time. This paper analyses the application of the neural network to the estimation of the ampacity in order to increase the amount of power produced by wind farms that can be integrated into the grid.

Indirect estimation of overhead line ampacity in overhead lines integrating wind farms

Ampacity techniques have been used by Distributor System Operators (DSO) and Transport System Operators (TSO) in order to increase the static rate of transport and distribution infrastructures, especially those who are used for the grid integration of renewable energy. In some cases, especially when the overhead lines are used to evacuate energy from wind farms, there is a direct correlation between the ampacity of the overhead lines and the active power produced by the wind farms. This papers analyses this correlation considering the most important weather parameters like wind, ambient temperature and solar irradiation.

Increasing Grid Integration of Wind Energy by using Ampacity Techniques

The grid integration of renewable energy, particularly in Spain, supposes an important problem to deal with for Distributor System Operators (DSO). Most of the times Wind Energy Farms are located in places that are far away from the transmission networks so they have to be integrated into distribution networks that are frequently operating close to their static rate. Current regulations make almost impossible to build new overhead lines so the increase of the capacity of the existing lines is a new target for the DSO. This paper is devoted to the analysis of a new methodology and monitoring system that have been developed to overcome the existing static rates by moving the operation point of the overhead lines close to their dynamic rate. This new rate is computed by using both the IEEE and CIGRÉ algorithms and a local weather forecast. The obtained results show that this approach can increase the capacity of the lines in a significant percentage.

A comparison of different methodologies for rating definition in overhead lines

The grid integration of renewable energy supposes an important problem to deal with for Distributor System Operators (DSO). Distributor and transmission system operators have been using static rates for a long time to manage electric systems. Currently operators deal with one, annual, static rate or four, seasonal, static rates. This paper is devoted to the analysis of a real case of ampacity management in a 132 kV overhead line for the purpose of stablishing new static rates based on different temporal intervals.

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.

Risk-averse energy management of hydro/thermal/pumped storage complementarily operating with wind/solar: Balancing risk, cost and carbon emission

Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind-Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints; (2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%; (2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.

Fuzzy Controlled Power Management and Power Quality Enhancement for Grid Integrated Renewable Energy sources with EV Fed UPQC

Recently, the merging of Renewable Energy Sources (RES) and Electric Vehicles (EVs) has brought about significant changes in distribution systems. The main goal of this research is to effectively tackle power quality (PQ) concerns such as voltage fluctuations(sags and swells) and total harmonic distortion(THD) in a system connected to the grid .This system combines Solar Photovoltaic(SPV) and Wind Energy Systems(WES) and integrates a Battery Energy Storage System(BESS) and Electrical vehicles(EVs). To accomplish this, a versatile Unified Power Quality Conditioner (UPQC), referred to as U-SWEBEV, is employed. The control of power flow, encompassing the transfer from energy generation sources to consumer loads and among various sources, is efficiently managed through the application of a Fuzzy Interface System (FIS) regulation. This FIS system plays a key role in regulating power distribution. Additionally, a fuzzy Controller is implemented to enhance Maximum power Point Tracking (MPPT) from the SPV system. This combined approach , utilizing both Fuzzy Logic and the Unified power Quality Conditioner (UPQC), significantly contributes to the efficient and effective. utilization of power resources while successfully addressing power quality (PQ) concerns with different load combinations . The proposed method reduces the total harmonic distortion of load current and source voltage at linear nonlinear, nonlinear, Nonlinear and unbalanced linear loads with % THD 1.64 ,0.04,1.20,0.04 and 2.84,2.32 which are lower than the existing methods that are available in literature.

Power investment and transmission network expansion in China and its neighbouring countries towards carbon neutrality

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea.

Adaptive Active Inertia Control Strategy of MMC-HVDC Systems for Flexible Frequency Support

The Modular Multilevel Converter High Voltage Direct Current Transmission (MMC-HVDC) technology is considered to be the most feasible choice for high-voltage and high-power transmission systems, and its flexibility and high controllability provide a new solution for renewable energy grid integration. The MMC topology contains a large number of capacitors, which enables it to provide a certain active inertia support for the connected AC system. Different from a synchronous machine, the active inertia control of an MMC can flexibly adjust a system’s inertia-supporting power by changing the control parameters. By introducing a variation of the Sigmoid function with amplitude-limiting capability, this paper proposes an adaptive active inertia control strategy for the MMC-HVDC system. The proposed scheme adjusts the inertia constant adaptively according to the frequency change rate of the AC system, which can better respond to the frequency recovery performance. Finally, the MMC-HVDC simulation model is established in PSCAD/EMTDC to verify the effectiveness of the proposed control strategy.

Research Progress of Flexible Peak Shaving Technology for Coal-Fired Boilers

In the face of the pressing challenges of climate change and carbon emissions, China's energy and power sectors are actively working towards the strategic goal of establishing a new power system, where wind and solar energy will constitute a rapidly expanding portion of grid-connected power generation. Nevertheless, the inherent characteristics of these renewable energy sources, such as their randomness, intermittency, and volatility, pose significant challenges to ensuring the secure and stable operation of the power grid. Presently, China heavily relies on pulverized coal power plants, which lack the flexibility required to accommodate the fluctuating demands posed by renewable energy generation. Overcoming this technical obstacle and enabling efficient grid integration of renewable energy necessitates a thorough exploration of the potential of existing pulverized coal furnaces for flexible peaking operations, especially under extreme peak shaving conditions. To address this challenge, coal self-preheating combustion technology has emerged as a pioneering solution developed by the Institute of Engineering Thermophysics, Chinese Academy of Sciences. By implementing preheating modification activation, this innovative approach alters the traditional combustion reaction path of pulverized coal, significantly enhancing its reaction activity. This advancement holds immense promise for achieving efficient and stable combustion, as well as facilitating rapid load changes in pulverized coal boilers operating at low loads. This study primarily focuses on discussing prevalent means of peaking technology, including low-load stable combustion technology, rapid load regulation technology, coupled peaking technology, and the development status of peaking technology for coal power units. Through in-depth research and innovation, it is anticipated that the effectiveness and viability of peaking technology will be further enhanced, promoting the sustainable development of pulverized coal boiler generating units and optimizing the operation of energy systems. Furthermore, the self-preheating combustion technology will increasingly play a pivotal role in multiple sectors and directions, such as flexible and deep peaking of coal power, driving the advancement and adoption of clean and effective coal utilization technology.

Frequency support by wave farms in low inertia power systems

New ancillary services and additional requirements for the grid integration of variable renewable energy (VRE) are being defined worldwide, in response to the technical challenges caused by increasing levels of VRE utilization in electric power grids. Currently, the use of wave energy is still limited to a few applications or demonstrations, where the level of penetration of wave power into the grid is not significant. To anticipate the future requirements for wave power integration, and the possibilities for provision of services, this paper considers the lessons being learned through the challenges caused by high penetration levels of other VRE sources into the grid, particularly wind power. On this basis, this paper presents an overview of grid support services that wave power plants can be expected to provide in power systems dominated by converter-interfaced generation, i.e. low inertia systems. Specifically, the focus is on services that support the active power balance in the power system. Then, the current capabilities and future perspectives for the provision of frequency support by wave farms are discussed.

Review of Low Inertia in Power Systems Caused by High Proportion of Renewable Energy Grid Integration

With the power industry moving toward a green and low-carbon direction, renewable energy is occupying an increasingly larger share in the power system. However, compared with traditional thermal power generation, the instability of new energy generation is very prominent, which also leads to a decrease in the inertia of the power system after the grid integration of a high proportion of renewable energy. If no measures are taken, this may lead to frequency collapse accidents. Therefore, this paper first introduces two international major power outage accidents that have occurred in recent years, analyzing the causes, and then summarizes the inspiration obtained from the accidents. Subsequently, some research results on low inertia-related issues in the power system caused by the high proportion of new energy grid integration in recent years were summarized and analyzed from three aspects: inertia evaluation methods, optimal operation measures for the power system, and under frequency load-shedding (the abbreviation “ULFS” in the following text stands for it) schemes. Finally, suggestions were made for future research directions.

Reliability and economics evaluation for generation expansion planning incorporating variability in wind energy sources

Generation expansion planning is a strenuous process encompassing technical, economic, geographical, and climatic factors. Due to the fast growth of grid-integrated renewable energy sources, planning for operational measures and generation techniques needs to be more flexible. Unlike conventional energy sources, renewable energy sources are fluctuating in nature, and a suitable methodology has to be adopted for efficient generation expansion planning considering reliability and economic analysis. The multistate generation models for representing wind energy sources make the reliability evaluation more challenging. The present work incorporates the impact of wind power generation variability and wind turbine generator outages on system reliability, an essential aspect of generation planning. In this paper, the techno-economic feasibility assessment model has been incorporated to ensure a reliable power supply, strengthening the grid's economic feasibility and lowering the energy generation and interruption costs and annualized system costs. The proposed model for reliability evaluation involves Fourier domain analysis. The model has been validated to study the wind energy sources dynamics and economic behavior, i.e., annualized operational maintenance cost and annualized fuel cost on the revised IEEE RTS with wind power, for the sensitive assessments for generation expansion planning.

The economic impact of energy storage co-deployment on renewable energy in China

Given the pillar role of renewable energy in the low-carbon energy transition and the balancing role of energy storage, many supporting policies have been promulgated worldwide to promote their development. To achieve the ambitious goal of no less than 1200 GW of wind and solar by 2030, China has also introduced policies to encourage the deployment of energy storage for the grid integration of renewable energy. The national policy is conducive to enhancing system flexibility for renewable integration, but it will also add the costs of renewable energy, especially when counting the regional differences. This paper adopts an improved levelized cost of electricity model to examine the total costs of renewable power co-deployed with energy storage in different provinces of China. The results show that the nationally unified energy storage co-deployment requirement, namely, 15% capacity ratio of renewable installation and 4 h duration, will negatively affect the economics of renewable generation, leading to an average cost increase in 15% and 21% for wind and photovoltaic generation, respectively. The economics of co-deploying energy storage under current market mechanism is inferior, but it can be effectively improved when energy storage participates in ancillary services market. With the revenue of frequency regulation, the cost of renewable co-deployed with energy storage can be even less than that without co-deployment in most provinces, except for Hebei, Jiangxi, and Gansu. An independent market entity status is thus conducive to encourage the co-deployment. Our finding also implies for a province-specific energy storage co-deployment policy.