High Penetration Of Renewable Energy Research Articles

Estimation of Quantitative Inertia Requirement Based on Effective Inertia Using Historical Operation Data of South Korea Power System

In low-inertia systems with a high penetration of renewable energy, the rotational kinetic energy and inertia constant are significant factors in determining frequency stability. The energy released owing to the frequency decrease during contingency represents a portion of the inertia that a synchronous machine possesses in the normal state. However, when securing inertia or planning additional resources to secure frequency stability, inertia in the normal state is analyzed as the standard rather than the amount of energy released during a fault. Therefore, in this paper, we define the actual energy emitted from a synchronous machine as Effective inertia. In order to evaluate Effective inertia in various operating conditions, we conducted a comprehensive review on approximately 24,627 cases from the years 2019, 2020, and 2021. As a result, in systems with low rotational kinetic energy, both low- and high-frequency nadirs were observed, indicating high uncertainty. However, Effective inertia presented a consistent trend regarding the energy release aligned with the minimum frequency. For instance, the rotational kinetic energy required to satisfy the frequency standard was 23 GWs, while the required Effective inertia was 858 MWs. We emphasize that securing inertia based on rotational kinetic energy includes additional imaginary energy that does not contribute to frequency, resulting in an energy requirement greater than that needed for Effective inertia. Therefore, in order to secure the frequency stability of the future system, the actual required energy amount based on Effective inertia will be presented and utilized in the inertia market and FFR (Fast Frequency Response) resource design.

Modelling of wind and solar power output uncertainty in power systems based on historical data: A characterisation through deterministic parameters

The inherent uncertainty associated with wind and solar energy poses challenges in ensuring the reliability of the power system with a high penetration of renewable energy. Therefore, incorporating uncertainty is vital during the planning of power systems. Traditional analytical methods often necessitate transforming the uncertainty of large-scale power systems into deterministic optimisation planning, while those approaches tend to be overly conservative, yielding unsolvable solutions, or rely on assumptions and data fitting, creating a significant gap with real-world scenarios. To address this, this study employs upper α-quantiles originating from abundant historical data to replace power output expectations to consider uncertainty during the planning. Additionally, random sampling based on historical data generates potential wind and solar output scenarios, and Monte Carlo simulations are used to validate the proposed method's reliability and assess system investment distribution. Using China's power system as a case study, 64 quantile choices are evaluated. Compared to planning through output expectations, using quantiles can reduce the load loss rate from 10−1 to 10−5, and both the cost expectations and the extreme cost risk decrease. The use of fossil fuels has also decreased by 10%. Results underscore the necessity of choosing upper α-quantiles with higher α, such as 85, to account for the long-tailed wind power distribution. However, even overly conservative wind power output α is still challenging to mitigate residual load loss. The combination of photovoltaics and intraday energy storage ensures that the α selection of photovoltaics should not be too low, due to the reason that there is a need to balance the gradual decrease of load loss rates and the steep ascent of expected investment costs in the power system.

Analyzing how the timing and magnitude of electricity consumption drive variations in household electricity-associated emissions on a high-VRE grid

Abstract Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO2 emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption.

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Impact of Impedances and Solar Inverter Grid Controls in Electric Distribution Line with Grid Voltage and Frequency Instability

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz.

System Modeling-Based Investigation of SMR Operations Using Variable T-avg Control for Cost-Effective Electricity Generation in a Grid

Given the importance of nuclear power as low carbon energy source, this study conducted techno-economic evaluation of flexible use (baseload as well as load-following) of small modular reactors (SMRs) in an electric grid. The load-following operation of SMR (SMART-100) was restricted to the use of variable T-avg control, a least disruptive and cost minimizing method of load follow control. A system model was developed to describe the reactivity feedback effects of coolant temperature variations due to electric demand-based turbine load changes. Using the system model, conservative output ranges and maximum ramp rates that allow load-following operation without violating axial offset limits were derived. Assuming the introduction of SMR in Jeju Island in South Korea, a semi-independent grid region, with highly dependency on fossil fuel energy and power imports along with significant renewable energy generation (15–20 %), as a test case, this study evaluated the economic contribution of SMR using LPM to minimize the grid electricity generation costs. The results indicate that in regions with a high penetration of renewable energy in their grid systems, flexible energy generation of SMR can make a significant contribution in reducing electricity generation cost and carbon emissions.

Robust $\mathbb {H}_{\infty }$Based Virtual Synchronous Generators for low Inertial Microgrids Considering High Penetration of Renewable Energy

Robust $\mathbb {H}_{\infty }$Based Virtual Synchronous Generators for low Inertial Microgrids Considering High Penetration of Renewable Energy

Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks

This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant (VPP) networks using multi-agent reinforcement learning (MARL). As the energy landscape evolves towards greater decentralization and renewable integration, traditional optimization methods struggle to address the inherent complexities and uncertainties. Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator (DSO) and individual VPPs, optimizing economic efficiency while maintaining grid stability. We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay. Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. The MARL framework shows particular strength in scenarios with high renewable energy penetration, where it improves system performance by 11.95% compared with traditional methods. Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.

A Review of Reliability Research in Regional Integrated Energy System: Indicator, Modeling, and Assessment Methods

The increasing complexity of integrated energy systems has made reliability assessment a critical challenge. This paper presents a comprehensive review of reliability assessment in Regional Integrated Energy Systems (RIES), focusing on key aspects such as reliability indicators, modeling approaches, and evaluation techniques. This study highlights the role of renewable energy sources and examines the coupling relationships within RIES. Energy hub models and complex network theory are identified as significant in RIES modeling, while probabilistic load flow analysis shows promise in handling renewable energy uncertainties. This paper also explores the potential of machine learning methods and multi-objective optimization approaches in enhancing system reliability. By proposing an integrated assessment framework, this study addresses this research gap in reliability evaluation under high renewable energy penetration scenarios. The findings contribute to the advancement of reliability assessment methodologies for integrated energy systems, supporting the development of more resilient and sustainable energy infrastructures.

Dynamic control of grid-following inverters using DC bus controller and power-sharing participating factors for improved stability

Integrating Grid-Following Inverters (GFLs) into power systems presents significant stability challenges, particularly in systems with reduced strength and high renewable energy penetration. This study delves into the dynamic interactions between parallel synchronous generators (SGs) and GFLs, highlighting the impact of reduced system inertia and transient stability margins. A novel DC bus controller is proposed to enhance the inertia and stability of GFLs during grid disturbances by dynamically adjusting power references based on load demand. Through mathematical modelling, small signal analysis, and MATLAB simulations, the study evaluates the effects of inverter-based resources (IBRs) on power system stability. The proposed GFL configuration demonstrates improved stability control, achieving an 80 % increase in stability under weak grid conditions. This paper provides insights into optimized control strategies, emphasizing the importance of power-sharing participation factors (PSPF) for effective GFL integration in modern power systems with high renewable energy penetration.

Online Evaluation for the POI-Level Inertial Support to the Grid via Ambient Measurements

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5%.

Flexible design of renewable hydrogen production systems through identifying bottlenecks under uncertainty

Renewable hydrogen production technology within the energy sector stands as a vital avenue towards decarbonization in process industries. However, for systems characterized by high penetration of renewable energy, the challenges posed by uncertain parameters impacting stable production present significant obstacles to the large-scale application of hydrogen production from renewable energy. To address the issues, this work proposed a comprehensive four-step strategy for the bottleneck identification and optimization of the renewable hydrogen production systems (RHPS), which encompasses four models including system configuration optimization, flexibility analysis, bottleneck identification, and debottlenecking models. The effectiveness of the proposed strategy is substantiated through a case study. The results indicate that positive fluctuations on supply side and negative fluctuations on demand side contribute to bolstering the flexibility of RHPS. When the RHPS possesses sufficient flexibility, the critical fluctuation amplitude for photovoltaic power is −36 %, whereas for wind turbine power, it stands at −32 %. It highlights that when the photovoltaic power fluctuates, the ability of RHPS to maintain its own stability is stronger than when the wind turbine power fluctuates. Furthermore, when the fluctuations of supply side are positive, the rate of change in critical fluctuation amplitudes on the supply side is 4.63 times than that of the demand side. This underscores that the power decline in the supply side has a more pronounced detrimental effect on the stability of RHPS than an increase in the demand side. The proposed strategy for optimizing RHPS offers valuable theoretical insights for the system design and retrofitting under uncertainty.

Maintaining reliability in a 100% decarbonized power sector: The interrelated role of flexible resources

Reliability will remain a key requirement for decarbonized power sectors, but also a concern with the high penetration of variable renewable energy (VRE). The traditional approach for reliability is by producing enough energy and adapting rapidly to changes in load with the dispatch of fossil fuel power plants, especially natural gas power plants. However, this will have to evolve as decarbonization progresses. Can various flexibility resources such as transmission, storage and demand response provide the same reliability benefits? This paper uses a detailed, capacity expansion and hourly operation model to investigate the possibility and the cost of reaching different levels of planning reserve margins (PRM), in the multi-regional context of the North American Northeast. We find that while it is possible to entirely avoid natural gas power plants or nuclear ones to obtain high PRM, it would come at a very high cost and would require high levels of transmission, storage and demand response. We also detail why these three flexibility resources are needed in combination. The implications for energy policies are significant: in order for power system's cost to remain reasonable, reliability must count on some carbon-neutral natural gas or nuclear generation, along with interties with neighboring systems, storage and high levels of demand response.

Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems

With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system’s adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system’s adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system’s net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels.

A method for selecting the type of energy storage for power systems with high penetration of renewable energy with multi-application scenarios

Energy storage (ES) configurations effectively relieve regulatory pressure on power systems with a high penetration of renewable energy. However, it is difficult for a single ES type to satisfy the complex regulatory demands of a power system. There has been little research on the selection methods for multiple types of ES that meet the demands of multiple application scenarios of power systems. This study introduces a method for the selection of ES types for power systems with a high penetration of renewable energy to determine the optimal ES types for multi-application scenarios. First, a generation method for ES schemes is proposed based on K-means clustering and combination theory. Then, considering the demands of peak shaving, frequency regulation and reserve of power systems with high penetration of renewable energy, an optimisation model of the expected indices of the ES is constructed to meet the demands of multi-application scenarios. Finally, the comprehensive evaluation method for ES schemes based on the error mean and the selection method for optimal ES types based on projection technology are proposed. Numerical studies show that for the power system in the example whose demand coefficients for peak shaving, frequency regulation and reserve are 30.59 %, 26.29 % and 43.11 %, respectively, a combination of pumped storage and colloidal battery, lithium iron phosphate battery, or lithium titanate battery is the most suitable ES type for the power system. This study offers a reference for the selection of ES types when an ES is configured.

A Review on Power System Security Issues in the High Renewable Energy Penetration Environment

A Review on Power System Security Issues in the High Renewable Energy Penetration Environment

An Enhanced Continuation Power Flow Method Using Hybrid Parameterization

The rapid integration of renewable energy sources and the increasing complexity of modern power systems urge the development of advanced methods for ensuring power system stability. This paper presents a novel continuation power flow (CPF) method that combines two well-known parameterization techniques: natural parameterization and arc-length parameterization. The proposed hybrid approach significantly improves computational efficiency, reducing processing time by 32.76% compared to conventional methods while maintaining high accuracy. The method enables faster and more reliable stability assessments by efficiently managing the complexities and uncertainties, particularly in grids with high penetration of renewable energy.

Evaluating the feasibility and economics of hydrogen storage in large-scale renewable deployment for decarbonization

Renewable energy (RE) is pivotal for achieving a net-zero future, with energy storage systems essential for maximizing its utility. This study introduces a modeling framework that simulates large-scale RE deployment in Taiwan, emphasizing hydrogen as a primary storage medium. Utilizing hourly time steps, the model assesses various energy generation and storage configurations to demonstrate the technical feasibility of meeting Taiwan's 2050 net-zero target, which calls for a 60 % RE share primarily through solar and wind resources. However, achieving this target and the ambitious goal of 100 % RE penetration requires substantial enhancements in both generation capacity and storage solutions. The research evaluates the economic impacts by analyzing the levelized cost of energy (LCOE), revealing that optimal configurations, such as a wind capacity of 30 GW with a 5 % minimum capacity factor constraint on electrolyzers, significantly reduce LCOE to $0.176/kWh, underscoring the cost-effectiveness of hydrogen storage compared to battery alternatives in large-scale settings. The study underscores the necessity for ongoing investigations into replacing thermal power plants and maintaining grid stability amidst high RE penetration. To fully harness Taiwan's RE potential and contribute to global sustainability, effective deployment of hydrogen storage will require robust stakeholder support, continual technological advancements, and enabling policies. This framework, while tailored to Taiwan's power system, offers insights applicable to various global contexts.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.

Frequency Support Coordinated Control Strategy of Renewable Distributed Energy Resource Based on Digital Twins

The integration of high-penetration renewable energy sources is playing an increasingly important role in the frequency regulation of the power system. However, due to the varying output characteristics and response speeds of different renewable distributed energy resources (DERs), the overall response from the collective output of these distributed energy resources may not meet expected requirements and could have adverse effects on the grid. One drawback of traditional distributed coordinated control is its high communication requirements. This paper proposes using digital twin (DT) technology for the coordinated control of distributed energy resources, which can minimize the communication needs between various distributed energy resources while achieving coordinated control. Verification of the frequency support coordinated control strategy based on digital twin technology shows that it can effectively enhance the individual output characteristics and overall response of each distributed energy resource, providing effective support for grid frequency.