High Renewable Energy Research Articles

Stability Analysis of DC Microgrids: Insights for Enhancing Renewable Energy Integration, Efficiency and Power Quality

In the current context of smart grids, microgrids have proven to be an effective solution to meet the energy needs of neighborhoods and collective buildings. This study investigates the voltage behavior and other critical parameters within a direct current (DC) microgrid to enhance system efficiency, stability, and reliability. The dynamic performance of a DC microgrid is analyzed under varying load and generation conditions, with particular emphasis on the voltage response and load-sharing mechanisms required to ensure stable operation. The findings indicate that specific control strategies, particularly droop methods, are effective in mitigating voltage fluctuations, enhancing power quality, and ensuring proper load distribution across multiple sources. This study also addresses significant challenges, including voltage regulation and fault resilience, to provide guidelines for designing robust and efficient DC microgrids. These insights are essential to inspire further advancements in control strategies and facilitate the practical deployment of DC microgrids as a sustainable solution for distributed energy systems, especially in scenarios prioritizing high DC load penetration and renewable energy integration.

Green Hydrogen Blending into the Tunisian Natural Gas Distributing System

It is likely that blending hydrogen into natural gas grids could contribute to economy-wide decarbonization while retaining some of the benefits that natural gas networks offer energy systems. Hydrogen injection into existing natural gas infrastructure is recognised as a key solution for energy storage during periods of low electricity demand or high variable renewable energy penetration. In this scenario, natural gas networks provide an energy vector parallel to the electricity grid, offering additional energy transmission capacity and inherent storage capabilities. By incorporating green hydrogen into the NG network, it becomes feasible to (i) address the current energy crisis, (ii) reduce the carbon intensity of the gas grid, and (iii) promote sector coupling through the utilisation of various renewable energy sources. This study gives an overview of various interchangeability indicators and investigates the permissible ratios for hydrogen blending with two types of natural gas distributed in Tunisia (ANG and MNG). Additionally, it examines the impact of hydrogen injection on energy content variation and various combustion parameters. It is confirmed by the data that ANG and MNG can withstand a maximum hydrogen blend of up to 20%. The article’s conclusion emphasises the significance of evaluating infrastructure and safety standards related to Tunisia’s natural gas network and suggests more experimental testing of the findings. This research marks a critical step towards unlocking the potential of green hydrogen in Tunisia.

Utilizing Soft Open Points for Effective Voltage Management in Multi-Microgrid Distribution Systems

To enhance stability and reliability, multi-microgrid systems have been developed as replacements for conventional distribution networks. Traditionally, switches have been used to interconnect these microgrids, but this approach often results in uncoordinated power sharing, leading to economic inefficiencies and technical challenges such as voltage fluctuations, delay in response, etc. This research, in turn, introduces a novel multi-microgrid system that utilizes advanced electronic devices known as soft open points (SOPs) to enable effective voltage management and controllable power sharing between microgrids while also providing reactive power support. To account for uncertainties in the system, the two-point estimate method (2PEM) is applied. Simulation results on an IEEE 33-bus network with high renewable energy penetration reveal that the proposed SOP-based system significantly outperforms the traditional switch-based method, with a minimum voltage level of 0.98 p.u., compared to 0.93 p.u. in the conventional approach. These findings demonstrate the advantages of using SOPs for voltage management in forming multi-microgrid systems.

Addressing under-voltage events in power systems with high renewable energy penetration

The increasing integration of renewable energy sources into power systems presents significant challenges for maintaining voltage stability, particularly in cases of under-voltage. Under-voltage events in power systems represents a significant risk to the grid's stability and efficiency. Such events commonly cause device malfunctions, a decrease in operating effectiveness, and, in severe situations, full system failure. This research investigates the fundamental causes of under-voltage scenarios and presents a novel methodology for addressing under-voltage issues by strategically integrating multiple sources of electrical energy. The objective of this technique is to solve instances of under-voltage and avoid the necessity for load shedding. The efficacy of the proposed strategies for maintaining grid stability and addressing under-voltage events is demonstrated through the presentation of case studies and simulations within an environment characterized by the presence of high renewable energy sources. The simulation results show that the strategic integration of renewable energy sources can effectively mitigate under-voltage conditions, enhancing voltage profiles and system stability. The present research demonstrates the potential of utilizing renewable energy source integration as a cost-effective and sustainable solution for under-voltage addressing in power grids with high renewable energy penetration, while also reducing the necessity for conventional voltage support infrastructure.

Optimal planning for high renewable energy integration considering demand response, uncertainties, and operational performance flexibility

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties’ impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties.

The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities

Renewable Energy Communities (REC) can largely contribute to building decarbonization targets and provide flexibility through the adoption of advanced control strategies of the energy systems. This work investigates how the role of flexibility sources will be impacted by shifting towards advanced control strategies under a high penetration of variable Renewable Energy Sources, in the following years. A large residential area with diverse energy systems, building envelope configurations, and energy demand patterns is modeled with the simulation environment RECsim, a virtual testbed for the implementation of energy management strategies in REC. Photovoltaic (PV) panels, Battery Energy Storage and Thermal Energy Storage (TES) of different sizes for each household provide a realistic description of a REC which includes both consumers and prosumers.This study explores a scenario in which advanced controllers based on Deep Reinforcement Learning (DRL) replace existing Rule-Based Controllers in building energy systems across a significant number of buildings. These control policies are simulated under three different scenarios that consider consumers with different pricing schemes and TES penetration.Efficient control strategies, have demonstrated significant potential, regardless of the presence of thermal storage and ToU pricing schemes, in reducing energy demand by 12.6%, cutting energy costs by 20.8%, and enhancing self-sufficiency and self-consumption, with minimal impact on Shared Energy. Implementing a flat tariff scheme under DRL enables consumers to increase their energy demand during periods of PV generation, which is particularly advantageous in a REC. Also, this approach lowers overall energy demand by 12.6% and boosts self-sufficiency, and it also decreases electricity exports from the REC to the grid by 18.2% compared to a ToU tariff scheme. When using ToU tariffs, thermal storage can be used to achieve cost savings, but total Shared Energy decreases, as do self-sufficiency and self-consumption of the REC. The results indicate that in a REC with high variable renewable energy and decentralized control, consumers using TES and ToU tariffs with peak prices during high irradiance periods may not be beneficial for the grid compliance.In conclusion, the coupling between DRL and thermal storage should be supported by more innovative pricing schemes for RECs and/or coordinated energy management, although it requires advanced communication and monitoring infrastructure.

Study on the transient overvoltage suppression effect of the synchronous condenser of high percentage renewable energy in the sending-end system

Abstract This paper analyzes the impact of configuring a synchronous condenser on the characteristics of the Inner Mongolia power grid following the commissioning of the first grid-to-grid DC project. Simulation results show that, after the configuration of synchronous condenser, the multiple renewable energy stations short circuit ratio (MRSCR) rises, and the DC sending end of the grid is a strong system, which is conducive to the whole scale of renewable energy transmission of Inner Mongolia power grid. DC bipolar blocking faults and transient overvoltage problems at the sending end occur for different operating conditions, such as reduced power and bipolar operation. Then, the effect of different numbers of synchronous condensers on transient overvoltages is studied. The study shows that it is necessary to reasonably arrange the DC project supporting thermal power unit start-up mode and configure large-capacity synchronous condenser or a certain number of distributed regulators, which can help to alleviate the problem of transient overvoltage during faults.

Design of a Reverse Osmosis Desalination Plant Powered by Renewables for a Small Mediterranean Island

Abstract Water shortage is one of the main problems for small, isolated islands, especially in the Mediterranean Sea. Water supply in those areas relies on maritime transport through tanker ships, especially during periods of high demand. However, this solution is unsuitable for isolated islands due to the high costs and environmental impact. This study aims to assess the feasibility of powering a desalination facility in a remote location with renewable energy sources to assess the potential cost savings and reduction of greenhouse gas emissions compared to the current supply mechanisms. To this end, the Greek island of Tilos is selected as a case study due to its high unexploited renewable energy production during winter months. The study hypothesizes using a seawater reverse osmosis (SWRO) unit to increase the sustainability of the water supply and promote the island’s self-sufficiency. Two control strategies have been adopted, simulating a demand-driven and a renewable production-driven scenarios. Results show a levelized cost of water that ranges between 1 and 2 €/m3, which is consistent with the average cost for the existing desalination plants in Grece. The adoption of a SWRO facility coupled with water storage systems results always in a more cost-effective solution than maritime transport, leading also to a relevant reduction of the environmental impact.

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%.

Hybrid modeling with data enhanced driven learning algorithm for smart generation control in multi-area integrated energy systems with high proportion renewable energy

Multi-area integrated energy systems (MA-IES) with a high proportion of renewable energy have become a trend in social development. The access to a large number of distributed energy sources makes the safe, stable, and economic operation of MA-IES suffer serious challenges. To reduce the frequency deviation and area control error (ACE) of MA-IES and to improve the economy of operation, this study proposed a hybrid modeling with data enhanced driven learning algorithm, which combines model-driven and data-driven algorithms, named combined proportion-integral-derivative and deep reinforcement learning (CPIDDRL). Firstly, the frequency deviation signals are collected and judged, and the smaller frequency deviation signals, which are sent to the model-driven part, are regulated by proportion-integral-derivative (PID) controllers. Then, the ACE and large frequency deviation signals are collected and transmitted to the data-driven part, and the Transformer deep learning network is applied to predict the multi-feature timeseries, which are applied to strategy selection for state-action-reward-state-action reinforcement learning. Finally, the model-driven part and data-driven part work together to generate adjustment commands every 4 s. The control effects of CPIDDRL, PID, Q-learning, and sliding model control algorithms are compared in two and four areas integrated energy systems. The results show that, compared to the comparison algorithms, the CPIDDRL reduces the mean values of frequency deviation and ACEs by at least 46.78% and 6.83%, respectively, and the total generation cost by at least 8.20%. CPIDDRL has practical significance in improving system stability, enhancing renewable energy integration capabilities, and promoting the development of smart grids.

Review on reactive power optimization of high proportion renewable energy power system

Review on reactive power optimization of high proportion renewable energy power system

Rolling planning model for high proportion renewable energy generation power system considering frequency security constraints

In this paper, a rolling planning model for high proportion renewable energy generation power systems is proposed, considering frequency security constraints, to address the frequency stability challenges posed by increased integration of wind and solar energy into the power grid under the “double carbon” goal. First, this study establishes a frequency response model for a thermal generation unit (TGU) and analyzes the impact of the high proportion of renewable energy on system frequency stability. Subsequently, a dynamic frequency security constraint is formulated, which can be used in generation planning to address the frequency issues. Second, a bi-level rolling planning model for high proportion renewable energy generation power systems considering dynamic frequency security constraints is established. The upper level focuses on investment decisions. The lower level incorporates frequency security constraints into the operational simulation to guarantee the frequency stability of the power system when making decisions on the size of newly built TGUs annually. Finally, case studies are conducted to demonstrate the validity and effectiveness of the proposed rolling planning model.

Towards decarbonizing large-scale industries: A decision support framework for optimizing grid-connected PV-battery energy systems planning – Case study of an OCP mining site, Morocco, North Africa

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

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 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

Assessment of the Renewable Energy Consumption Capacity of Power Systems Considering the Uncertainty of Renewables and Symmetry of Active Power

The rapid growth of renewable energy presents significant challenges for power grid operation, making the efficient integration of renewable energy crucial. This paper proposes a method to evaluate the power system’s capacity to accommodate renewable energy based on the Gaussian mixture model (GMM) from a symmetry perspective, underscoring the symmetrical interplay between load and renewable energy sources and highlighting the balance necessary for enhancing grid stability. First, a 10th-order GMM is identified as the optimal model for analyzing power system load and wind power data, balancing accuracy with computational efficiency. The Metropolis–Hastings (M-H) algorithm is used to generate sample spaces, which are integrated into power flow calculations to determine the maximum renewable energy integration capacity while ensuring system stability. Short-circuit ratio calculations and N-1 fault simulations validate system robustness under high renewable energy integration. The consistency between the results from the M-H algorithm, Gibbs sampling, and Monte Carlo simulation (MCS) confirms the approach’s accuracy.

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.