Renewable Energy Power System Research Articles

Sub-synchronous oscillation suppression strategy for virtual synchronous generators based on dual-loop sliding mode control

The virtual synchronous generator (VSG) has synchronous voltage source characteristics that can effectively improve the stability of high-penetration renewable energy power systems. However, the output impedance of VSG using traditional voltage-current inner-loop control has lower damping in the sub-synchronous frequency range. Therefore, when the VSG is connected to a grid with series compensation for long-distance transmission of renewable energy, in the sub-synchronous frequency range, there is a potential interaction between the inductive output impedance of VSG and the capacitive impedance of the line, leading to the occurrence of sub-synchronous resonance (SSR). To address this challenge, the stability of a series-compensated grid-connected system employing voltage-current inner-loop control was analyzed using VSG sequence impedance modeling. A dual-loop sliding mode control strategy (SMC-DL) was proposed to suppress SSR occurrence. This control strategy can significantly enhance the damping of VSG’s output impedance in the sub-synchronous frequency range, and effectively suppress SSR. Compared to traditional control strategies, this control strategy does not require the addition of virtual impedance, avoids voltage offset issues, adapts to grids with different series compensation levels. Finally, the effectiveness of this approach was verified through simulations and experiments.

Interruption characteristics of self-powered hybrid DC circuit breaker with synergistic effect of step-down and divided current

The DC voltage level of photovoltaic and PEDF applications have continuously advanced, reaching up to DC3000V. This presents new requirements for DC circuit breakers operating at higher voltages (>DC1500V) to ensure effective fault protection in the DC side. Based on this, a self-powered hybrid DC circuit breaker with synergistic effect of step-down and divided current is proposed. This innovative design utilizes the voltage of main branch to trigger IGBT conduction, eliminating the need for an additional trigger circuit. The interruption characteristics is analyzed by simulation. The reactor and shunt resistance effectively reduce the peak currents of both main and transfer branches. This approach significantly mitigates the voltage stress on the mechanical switch, reduces the risk of post-arc reignition, and shortens the interruption time. The circuit breaker topology proposed enables the DC arc interruption at larger current and higher voltage, serving as the valuable guide for the implementation of hybrid DC circuit breakers in renewable energy power systems.

Evaluating the impact of off-design CHP performance on the optimal sizing and dispatch of hybrid renewable-CHP distributed energy resources

The maturation of distributed energy resources (DER) has prompted the exploration of their deployment in commercial building applications due to their potential to supply energy at lower costs and emissions rates compared to centralized generation. While several software tools exist for evaluating the techno-economic potential of integrated renewable energy and combined heat and power (CHP) systems for distributed generation applications, many suffer from poor accuracy in capturing off-design (part load and changes in ambient air temperature and pressure) performance characteristics of microturbines, combustion turbines, or internal combustion engines. Thus, this paper presents a methodology for integrating these off-design characteristics in the mixed-integer linear program within REopt, a hybrid DER screening tool. The economic impact of the CHP off-design performance is observed through several application studies of various hybrid system configurations in different climates. Each study indicates how CHP off-design performance influences optimal sizing and dispatch decisions and therefore overall system economic value. We observe through case studies that modeling without the off-design effects, depending on the CHP prime mover and site, can result in Net Present Value predictions of hybrid systems that can be overoptimistic in frequently hot climates (up to 52 %), too conservative in frequently cold climates (up to 11 %), or unaffected (＋/−1 %) in temperate climates. Cases also highlight several advantages of hybrid systems relative to non-hybrid systems such as total economic value and the systems’ ability to mitigate potentially negative consequences attributed to off-design performance.

Multi-scenario flexibility assessment of power systems considering renewable energy output uncertainty

The widespread adoption of renewable energy sources presents a significant challenge to the flexibility of power system. To assess the flexibility of the power system in scenarios with uncertain renewable energy output, it is crucial to quantify it quantitatively. This evaluation plays a vital role in planning flexible regulatory resources and dispatching resources for both the energy source and load. This study introduces a novel flexibility assessment model tailored for power grids with high renewable energy penetration, specifically addressing uncertainty associated with wind and PV. By analyzing the impact of wind and PV uncertainty on system flexibility, the paper proposes an improved cohesive hierarchical cluster analysis method, incorporating reliability considerations based on the Davies-Bouldin classification reliability index. Additionally, the study develops models for flexibility resources and demands within high renewable energy power systems, along with quantitative assessment indicators across three key aspects. Through a structured flexibility assessment process accounting for wind and PV uncertainty, the effectiveness of the proposed approach is validated using real-world data from a renewable energy power grid in Shandong province. A set of typical renewable energy output scenarios with uncertainty is constructed using the improved hierarchical cluster analysis method. The study then analyses the impact of different wind and PV penetration rates and the proportion of energy storage units on system flexibility by the flexibility assessment model to validate the proposed method's effectiveness.

Feasibility study: Economic and technical analysis of optimal configuration and operation of a hybrid CSP/PV/wind power cogeneration system with energy storage

In this study, a hybrid photovoltaic-wind-concentrated solar power renewable energy system and two cogeneration models are proposed. Evaluation criteria are employed, including the levelized cost of energy (LCOE) and the loss of power supply probability (LPSP). The optimal configuration and dynamic dispatch strategy of the hybrid system are determined through multi-objective optimization using the natural resources of Xining City. The economic feasibility of the system is assessed through comparison and analysis. The findings reveal that both cogeneration modes of the system effectively meet the power output requirements and dynamic thermal load. In thermal energy storage tanks' heat production mode without a battery storage system, the system achieves a minimum LCOE of 0.0526$/kWh and a maximum LPSP of 6.86%. With a battery storage system in this mode, the system attains a minimum LPSP of 4.83% and a maximum LCOE of 0.160$/kWh. Additionally, in the turbine spent vapor recovery heat supply mode, the system exhibits an LCOE of 0.127$/kWh and an LPSP of 4.96%, the system has better flexibility and wind-solar complementarity in this mode. This study also analyzed the importance of affordable electric energy storage for stable energy supply in hybrid energy systems.

Low-Frequency and Sub-Synchronous Power Oscillation Suppression and Analysis of Hybrid Line Commutated Converter for HVDC Grid

Building high-proportion renewable energy grid requires economical and reliable High Voltage Direct Current (HVDC) converters. Line Commutated Converter (LCC) is widely used because of its long transmission distance, low power loss, low manufacturing cost, and large transmission capacity, which modular multilevel converter (MMC) cannot achieve, but has commutation failure (CF) problems. Low-frequency and sub-synchronous power oscillation caused by CF will affect the safety of bulk power systems. This paper theoretically analyzes the power oscillation during CF and CF recovery, and then proposes a novel hybrid line commutated converter (H-LCC) based on integrated gate commutated thyristor (IGCT), which can effectively mitigate CF and suppress power oscillation. The principle of H-LCC suppressing power oscillation is analyzed. On this basis, the performance of LCC, MMC and H-LCC under the same fault conditions is compared by a simulation study, which proves that H-LCC can improve the security and stability of high-proportion renewable energy power systems.

A Method to Improve Both Frequency Stability and Transient Stability of Virtual Synchronous Generators during Grid Faults

Transient stability and frequency stability of virtual synchronous generator (VSG)-controlled converters during grid faults are critical for the stable operation of high-share renewable energy power systems. However, the existing transient stability methods often overlook the consideration of frequency stability. This paper presents a method to improve both frequency stability and transient stability of VSGs during grid faults. The influence mechanism of inertia and damping on transient stability and frequency stability is comprehensively analyzed under two types of faults, with or without stable equilibrium points. To address the conflict between frequency stability and transient stability of VSGs with large inertia under the first type of fault, an improved VSG control with angular frequency deviation feedback is proposed. The transient response of this control method is analyzed using small-signal and large-signal models. Furthermore, to achieve optimal dynamic performance, this improved VSG control introduces the optimal damping ratio and presents a parameter design method. Finally, the correctness of the proposed method and its theoretical analysis is verified through experimental results.

Enhanced dynamic state estimation of regional new energy power system under different abnormal scenarios

AbstractThe high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short‐term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride‐through (LVRT), high voltage ride‐through (HVRT), continuous fault ride‐through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30‐node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios.

Analysis of Traveling Wavefront Information and Wavefront Protection for Power Electronic Device-Interfaced AC System

The power electronic device-interfaced ac system becomes an important scene of renewable energy power system. The response of the power-electronic devices confronting faults is restricted by their overcurrent withstand capability, showing weak-feed characteristic. Therefore, the sensitivity of protection can be ensured only when the fault can be located before fault ride through of power-electronic device. In this paper, the expression of fault traveling wavefront is derived first and it is pointed out that the expression contains two terms, which are power frequency sine term and exponential decay term. The index of the exponential decay term is only determined by fault distance, whereas the fault occurrence time, fault type, and fault impedance only influence amplitudes of the two terms. In this way, the fault distance and fault severity can be characterized by index and amplitude coefficients respectively and thereby be decoupled. On this basis, an algorithm is proposed to extract fault distance and fault severity information from wavefront, then a novel protection principle is proposed using fault distance information contained in wavefront. The proposed wavefront protection can fast locate faults and have higher sensitivity and reliability compared with the existing non-unit protections, which is verified by numerous simulations and experimental tests.

Enhancing envrionmental sustainability via interval optimization for low-carbon economic dispatch in renewable energy power systems: Leveraging the flexible cooperation of wind energy and carbon capture power plants

In response to pressing environmental challenges, the power system is gradually developing towards a more efficient, clean, and sustainable direction. Carbon capture power plants (CCPPs) and wind power generation will play a significant role in this eco-friendly transformation. However, the high energy consumption of CCPPs and the anti-peak regulation characteristics of wind power lead to contradictions in their coordinated operation during peak shaving, restricting the system's low-carbon economic development. Aiming at the above challenges, an interval optimization method is proposed to coordinate wind power and CCPPs. Firstly, a flexible operation model of CCPPs is established, and the coordinated scheduling mechanism of wind power joint carbon capture operation and load-side demand response is introduced to optimize the system carbon emission through low carbon scheduling on both sides of the source and load. Secondly, interval numbers are used to manage the uncertainty of wind power output, and the interval-optimized scheduling model of wind power combined CCPPs is established. To solve the proposed model, the improved interval possibility method is adopted to transform the uncertain model into a deterministic one, which is solved by the CPLEX solver. Finally, the simulation outcomes using the IEEE 30-bus system framework reveal that the proposed approach significantly enhances the coordination between CCPPs and wind power. Notably, when compared to scenarios that solely focus on CCPPs, the proposed method achieves a substantial reduction in total costs by 16.67 % and decreases carbon emissions by 26.53 %. This sets a significant reference for low-carbon economic dispatch within power systems, providing key insights and foundational guidelines for future implementations, as well as novel strategies and technological support for the sustainable transformation of the power system towards lower carbon emissions.

Economy-environment-energy performance evaluation of power to hydrogen and power to methane considering renewable energy generation and carbon capture

Abstract The integration of multienergy complementary systems has become increasingly prevalent to mitigate the waste of renewable energy. As power-to-gas (P2G) technology advances, converting electricity into gas presents a viable solution for bolstering the reliability of power supplies within integrated energy frameworks. P2G plays a pivotal role in harnessing unused renewable resources, such as wind and solar energy, to produce sustainable power. While numerous studies have explored the advancement of P2G, there remains a lack of comprehensive evaluation of its various component subsystems. This research aims to design a renewable energy power system, augmented by gas-fired generation, assessing the collective advantages of both the power-to-hydrogen (P-H) and power-to-methane (P-M) conversion processes within the P2G infrastructure. It is important to note that the economic potential of P-H conversion is currently constrained by cost factors within existing P2G systems. This study takes into account the dynamics of carbon trading, as well as the hydrogen and methane markets, to compare the economic, environmental and energy-specific benefits of P-H and P-M conversions under different power demand scenarios. The goal is to formulate enhanced strategies to optimize P2G deployment in these varied contexts.

A proposed PMU-based voltage stability and critical bus detection method using artificial neural network

Voltage stability detection is currently still becoming the main issue in the modern integrated renewable energy power systems. To assess the voltage stability, the classical methods based on continuation power flow (CPF) technique were used to show nose curve. However, the classical methods require complete model of power system and long computation time. Data driven analysis and synchronized real time measurement technologies currently are developing in power systems monitoring, including the stability detection. The detection method is built based on the historical event model and uses the real time measurement as an input. For that reason, the algorithm to detect the voltage instability and critical bus is proposed using the artificial neural network (ANN) technique to represent the historical event model using the PMU measurement data. The ANN model architecture for this application is developed by creating seven hidden layers consisting of one normalization, four rectifier linear unit, one softmax and one sigmoid layer. To warrant the accuracy, the k-fold cross-validation is used. The algorithm is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and Photovoltaic (PV) integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is > 97% and critical buses detection is > 96% for all scenarios. Moreover, the required computation time is between 16 and 18 s per detection which makes the scalability to the real time detection is reasonable.

Probabilistic Forecasting-Based Reserve Determination Considering Multi-Temporal Uncertainty of Renewable Energy Generation

Operating reserve, among the most important ancillary services, is a powerful prescription for mitigating increasing uncertainty of renewable generation. Traditional reserve determination neglects uncertainty of renewable generation variations within the dispatch interval, which cannot gurantee sufficient reserve deliverability in real-time operation. This paper proposes a probabilistic forecasting-based reserve determination method to efficiently deal with multi-temporal uncertainty of renewable energy in power systems. A novel probabilistic forecasting approach is proposed by integrating Dirichlet process Gaussian mixture model into bootstrap-based extreme learning machine. A unified uncertainty model is constructed for depicting both interval-averaged uncertainty and intra-interval uncertainty by combing the probabilistic forecasts and linearized Itô-process model. In current business practices of many independent system operators, the coordinated determination of two well-designed reserve products, including regulating reserve and ramp capability reserve, is formulated in a two-stage robust optimization framework. A Bernstein polynomial-based model reformulation approach is then employed to handle the existing heterogeneity in the sub-models of each stage. Consequently, the integrated reserve determination is embedded in a generic two-stage robust optimization problem, which can be efficiently solved by the adopted modified column-and-constraint generation method. Finally, numerical simulations are implemented to validate the effectiveness and profitability of the proposed approach.

Theoretical research on carbon emission factors of distributed smart distribution network under high proportion of renewable energy access

In the context of the “dual carbon” goal, a high proportion of renewable energy connected to the grid to replace traditional high-carbon units has become a general trend. Taking the high proportion of renewable energy power system as the research object, the medium and long term planning of the installed capacity of source, grid, load and storage of power system is carried out considering the two dimensions of investment and operation maintenance, and the optimization model of source, grid, load and storage of power system is established. A low-carbon dispatching model considering carbon emission and demand response of distribution network is also proposed. Firstly, considering the characteristics of distribution network receiving power from the main network, the carbon emission model of distribution network based on carbon emission flow theory is constructed, and the low-carbon index of distribution network is proposed. Secondly, the improved dynamic carbon emission factor of distribution network is proposed, which is used as a guide signal to provide reference for low-carbon electricity consumption of distribution network users. According to the characteristics of users in the distribution network, the industrial demand response model aiming at low dispatch cost and low carbon and the residential demand response model aiming at electricity consumption satisfaction and low carbon satisfaction are constructed respectively.

Research on Optimal Dispatching of Renewable Energy and Traditional Power System

This research seeks to determine the most efficient method of incorporating renewable energy into the traditional power system. By analyzing the challenges and obstacles faced in merging renewable energy with the traditional power system, this study outlines the major variations in energy sources, the volatility of renewable energy, the uncertainty caused by climate change and weather patterns, the impact of renewable energy on grid stability. The power system's capacity and dependability were also taken into account. This study presents recommended scheduling suggestions to tackle these issues, encompassing intelligent energy prediction and management, incorporation of different energy storage technologies, enhancements to the distributed energy system, and utilization of smart grid technology.

Robust planning for distributed energy storage systems considering location marginal prices of distribution networks

Energy storage plays an important role in integrating renewable energy sources and power systems, thus how to deploy growing distributed energy storage systems (DESSs) while meeting technical requirements of distribution networks is a challenging problem. This paper proposes an area-to-bus planning path with network constraints for DESSs under uncertainty. First, a distribution location marginal price (DLMP) formulation with maximum fluctuation boundaries of uncertainties is designed to select vulnerable areas exceeding voltage limits and higher line losses that occur in distribution networks. Different from simple multi-scenario power flow calculation and sensitivity analysis, DLMP with time and regional characteristics could be more intuitive to reflect line losses and voltage limits of distribution networks through price signals. After that, a two-stage stochastic robust optimization based planning method is developed to determine locations and capacities of DESSs in vulnerable areas. To make the uncertainty problem more tractable, stochastic scenarios are used to portray upper and lower boundaries of uncertainties, which avoids too-conservative decisions for robust optimization. Finally, numerical tests are implemented to testify the reasonability and validity of the proposed area-to-bus planning path under uncertainty. Compared with the DESSs planning framework without DLMP, the costs of DESSs are observably reduced with DLMP. With same budgets of uncertainty, investment costs of DESSs for the stochastic robust optimization with 30 and 50 scenarios are 3.91% and 4.45% lower than classical adaptive robust optimization (ARO).

Optimization of alkaline electrolyzer operation in renewable energy power systems: A universal modeling approach for enhanced hydrogen production efficiency and cost-effectiveness

This study presents a comprehensive analysis of an Alkaline Water Electrolysis System (AWES) for hydrogen production, focusing on thermal balance, operating conditions, efficiency, and lifecycle cost. A thermal balance model was developed and further refined using machine learning techniques based on experimental data, to accurately calculate electrolysis power, cooling requirements, and temperature maintenance, enabling cost-effective operation. This study provides insights for optimizing AWES performance and economic feasibility in hydrogen production. Selecting appropriate operating conditions considering current density, inlet temperature, and electricity price is crucial. Implementing these optimizations improves energy efficiency, reduces lifecycle hydrogen production costs, and enables integration with renewable energy systems.

What influences industrial enterprises’ willingness of demand response: A survey in Qinghai, China

Although demand response (DR) is an important means for solving the stability and reliability challenges of renewable energy (RE) power systems, it remains unclear how DR can be widely applied in industrial enterprises, particularly in areas with a high proportional RE system and industrial energy consumption that do not apply any DR programs. The objective of this study is to understand the influencing factors of industrial enterprises' willingness to participate (WTP) in DR in these areas, including one economic factor and three noneconomic factors. This study performs a questionnaire survey in Qinghai, and a total of 556 industrial enterprises completed valid questionnaires, these data are empirically analyzed through Structural Equation Model. The results demonstrate enterprises' DR awareness degree is the most significant predictors of the WTP in DR. Meanwhile, enterprises' electricity consumption level also had a significant impact on the WTP in DR rather than enterprises' potential DR capabilities and the expected minimum compensation. Finally, the study suggests feasible recommendations, such as increasing enterprises' ability of analyzing potential DR, directly encouraging enterprises to participate in DR, designing compensation standards and adopting differentiated DR according to enterprises’ actual DR potential.

Cascade hydropower stations optimal dispatch considering flexible margin in renewable energy power system

Power system flexibility refers to the ability to respond to changing net loads within a predefined timeframe. The main causes of flexible demand are resource and load uncertainties. The joint interaction of resource and load uncertainty makes flexible demand more complex. Accurately assessing the power system flexibility and promoting flexible supply are important for maintaining grid safety and stability. In this study, a joint probability distribution for intermittent renewable energy sources (IRES) output and load forecast deviation is proposed. Subsequently, an optimal dispatch model that considers flexible demand and hydropower flexible supply is built to improve the operation scheme of flexible resources. A hydropower dispatch model that does not consider the probability of IRES output and load forecast deviation is used as the contrast model. A comparison of the results shows that the optimal dispatch model is more beneficial for maintaining grid safety and stability. The results show that the maximum upward adjustment flexible (UAF) margin of the optimal dispatch model of the Yalong River Downstream is 376 MW, whereas the maximum downward adjustment flexible (DAF) margin is 197 MW. This implies that a greater UAF supply is required in the case study to satisfy the power system flexible demand.

Dispatch of highly renewable energy power system considering its utilization via a data-driven Bayesian assisted optimization algorithm

In recent years, renewable energy (RE) has been widely deployed, and the power system with high penetration RE is gradually formed. However, the high proportion of RE may threaten transmission security of power systems, which in turn limits its utilization. What is more, the interaction between RE penetration and power system transmission security has not been comprehensively investigated so far. To this end, we develop a bi-objective stochastic dispatch model to investigate the relationship between RE utilization and transmission security. It aims to solve the optimal power system dispatch (OPSD) problem with high penetration RE, in which the RE curtailment and the capacity margin of transmission lines are considered as two objectives of the dispatch problem and formulated in the probabilistic forms. With this, the proposed model is a complicated bi-objective stochastic optimization problem, which is difficult to be solved for traditional optimization algorithms. Therefore, we propose a data-driven Bayesian assisted optimization (DBAO) algorithm, based on Bayesian evolutionary optimization and estimation of distribution algorithm to improve the searching efficiency for the proposed model. Case studies on a modified Midwestern US power system verify the effectiveness of our proposed dispatch model and the optimization algorithm of DBAO.