Intermittent Renewable Energy Sources Research Articles

Fundamental benchmarking of the discharge properties of negative electrodes in lead acid batteries

The unprecedented scale of energy storage deployment needed to allow high penetration of intermittent renewable energy sources into the electrical grid places significant economic cost and performance targets on battery technologies. Lead batteries, owing to their highly abundant and inexpensive raw materials, can be considered an important solution to grid storage as long as they achieve high material utilization, fast recharge rates, and long cycle life. To address the limitations in material utilization, we need to revisit the electrochemical processes during the discharge step and answer the following question: what are the fundamental limits of the discharge reaction? In this work we discuss how well-defined lead metal surfaces with nanometer scale roughness allow us to resolve the accessible discharge capacity for both faradaic and non-faradaic processes as a function of electrolyte concentration and in the presence of traditional additives. The direct connection between PbSO4 layer morphology and discharge rates gives us important insights on the mechanism of discharge as related to the dissolution and passivation events. This work sets the stage for reimagining the design of lead batteries with implications to grid storage applications.

Comprehensive analysis of integrating smart grids with renewable energy sources: Technological advancements, economic impacts, and policy frameworks

This study presents a comprehensive analysis of integrating smart grids with renewable energy sources, focusing on technological advancements, economic impacts, and policy frameworks. The primary objective is to explore how smart grid technologies can efficiently incorporate renewable energy sources, thereby enhancing grid reliability, efficiency, and sustainability. Utilizing a multidisciplinary approach, the study examines successful case studies, pilot projects, and innovative practices that highlight the potential and challenges of this integration. Key findings reveal that advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain are crucial for the real-time monitoring, predictive maintenance, and optimized management of energy systems. These technologies address the inherent variability and intermittency of renewable energy sources like solar and wind power. Case studies, including the Brooklyn Microgrid and Germany’s Energiewende, demonstrate significant improvements in energy resilience, efficiency, and consumer empowerment through decentralized energy systems. Economic analysis underscores the dual impact of cost savings from operational efficiencies and the financial challenges posed by substantial upfront investments in smart grid infrastructure. Policy frameworks play a pivotal role, with recommendations for supportive regulatory policies, increased funding for research and development, and enhanced public-private partnerships to drive innovation and consumer engagement. The study concludes that overcoming the technical, economic, and regulatory barriers requires coordinated efforts among stakeholders. Recommendations include developing consistent regulatory frameworks, fostering public-private partnerships, and implementing educational programs to encourage consumer participation in renewable energy initiatives. By addressing these challenges, the integration of smart grids with renewable energy sources can pave the way for a more sustainable, resilient, and efficient energy future. Keywords: Smart Grids, Renewable Energy Integration, Technological Advancements, Economic Impacts, Policy Frameworks, Grid Management Innovations.

The influence of socio-technical variables on vehicle-to-grid technology

Vehicle-to-grid technology (V2G) is a novel large scale energy storage option to improve the grid integration of renewable energy sources (RES). Using electric vehicle (EV) batteries to store and provide electricity to the grid, the intermittency of RES can be reduced. However, a successful implementation of this technology depends on various social and technological factors. This study analyses the influence of social and technical factors such as V2G acceptance (percentage of EV users that allow V2G energy transfer), EV battery availability for V2G service (percentage of the EV battery), EV adoption level (number of EVs in EV fleet) and the charger power for the energy transfer between the storage system and grid.Using Germany for the case study, a simulation model is developed and employed for the study. The base simulation results show that Germany needs 190 GW of PV and 170 GW of wind turbine installation to meet 80 % of electricity generation from RES in 2030. Further results show that an increased V2G acceptance and EV battery availability reduces the V2G contribution from individual EVs. A V2G acceptance of only 30 %, using half of the battery capacity dedicated to V2G, can help to reach an hourly reliability of 86.6 %. Having 50 % V2G acceptance, the hourly reliability increases by 5.3 %–91.9 %. The final analysis on charger power and EV adoption level highlights that a normal charger power of 7 kW or 11 kW can successfully accommodate V2G service. The study's overall results indicate that V2G technology will be an effective storage solution for Germany in the future.

Carbon Structure Regulation Strategy for the Electrode of Vanadium Redox Flow Battery.

Vanadium redox flow battery (VRFB) is a type of energy storage device known for its large-scale capacity, long-term durability, and high-level safety. It serves as an effective solution to address the instability and intermittency of renewable energy sources. Carbon-based materials are widely used as VRFB electrodes due to cost-effectiveness and well-stability. However, pristine electrodes need proper modification to overcome original poor hydrophilicity and fewer reaction active sites. Adjusting the carbon structure is recognized as a viable method to boost the electrochemical activity of electrodes. This review delves into the advancements in research related to ordered and disordered carbon structure electrodes including the adjusting methods, structural characteristics, and catalytic properties. Ordered carbon structures are categorized into nanoscale and macroscale orderliness based on size, leading to improved conductivity and overall performance of the electrode. Disordered carbon structures encompass methods such as doping atoms, grafting functional groups, and creating engineered holes to enhance active sites and hydrophilicity. Based on the current research findings on carbon electrode structures, this work puts forth some promising prospects for future feasibility.

Integrating renewable energy with fuel synthesis: Conceptual framework and future directions

The integration of renewable energy sources with fuel synthesis represents a transformative approach to addressing the twin challenges of energy sustainability and climate change. This review presents a conceptual framework for coupling renewable energy technologies, such as solar, wind, and biomass, with fuel synthesis processes to produce sustainable, zero-carbon fuels. The framework emphasizes the use of renewable electricity for water splitting and carbon dioxide reduction, key steps in synthesizing hydrogen and other synthetic fuels. The conceptual framework highlights the importance of advanced catalysts in enhancing the efficiency and selectivity of these chemical reactions. Innovations in nanostructured catalysts, hybrid materials, and biomimetic approaches are discussed for their potential to significantly improve the performance and durability of catalytic processes. Additionally, the framework underscores the role of advanced characterization techniques and computational modeling in understanding and optimizing catalyst behavior. This integration necessitates addressing technical challenges associated with scaling up production processes from laboratory to industrial levels. These challenges include ensuring the stability and longevity of catalysts under operational conditions, managing the intermittency of renewable energy sources, and developing robust systems for capturing and utilizing carbon dioxide. Furthermore, the economic viability of these integrated systems is critical, requiring cost-effective solutions that leverage earth-abundant materials and optimize the overall energy efficiency. The environmental benefits of integrating renewable energy with fuel synthesis are substantial, offering a pathway to significantly reduce carbon emissions and reliance on fossil fuels. The production of synthetic fuels using renewable energy can lead to a closed carbon cycle, thereby mitigating the impact on climate change and contributing to a sustainable energy future. Future directions in this field involve interdisciplinary research to further enhance catalyst performance, develop new materials, and refine process integration strategies. A roadmap for future development includes prioritizing areas such as improved catalyst design, efficient CO2 capture technologies, and the integration of electrochemical and photochemical systems. This abstract concludes by underscoring the transformative potential of renewable energy-fuel synthesis integration, envisioning a future where sustainable energy practices become the cornerstone of global energy systems. Keywords: Integrating, Renewable Energy, Fuel Synthesis, Future Directions, Framework.

Alternating direction method of multipliers based distributed energy scheduling of grid connected microgrid by considering the demand response

Global warming, environmental degradation, clean energy production, intermittent, volatile, and unpredictable renewable energy sources (RES’s), occasional peak demand on the system necessitates energy management (EM). Demand response (DR) programs in the distribution network can be seen as one of the foundation stones in the future of EM. This article illustrates the need for EM using DR, its benefits, types of loads, clustering techniques, price-based demand response (PBDR) etc. To accomplish the EM goals and to attain the economic benefit, DR employs peak shifting, peak clipping, valley filling and load growth. However, the accumulation of large loads at low electricity prices creates local peaks, this phenomenon is referred to as payback or rebound effect (RE). The occurrence of RE at low price zone heightens the volatility of market clearing price (MCP) and the operational cost of the microgrid. Inherently, the scheduled inelastic consumers at low price zone suffer from increased MCP and therefore, the total consumer tariff (TCT). The occurrence of RE depends on the load curve, peak to average ratio, electricity price and the percentage of interruptible loads present in the system. Unclear pricing methods impede the participation of customers in DR events. Moreover, majority of techniques presented in literature are of centralized frameworks that needs complex communication technologies. To fill these glitches the proposed work uses a simple distributed scheduling approach based on alternating direction method of multipliers (ADMM) to alleviate the energy management using an IEEE-18 bus system. The load factor increases from 0.79 to 0.83. Using DR lowers the peak power demand on the MG from 82 to 78 kW without compromising customer comfort or satisfaction. The TCT was lowered from scenario 1 to scenario 4 from 3058 to 2254 euros. The system's average demand dropped from 65.54 kW to 64.8 kW. IEEE-33 bus system was considered to assess the impact of RE on the MCP and TCT. Additionally, the marginal generator provides 72.51 kW of electricity in sub case 3 and 166.26 kW of power in sub case 2. Due to a decrease in power dispatch from the marginal generator, TCT increased from sub case 2 to sun case 3 by 11,046.41 rupees to 12,912.75 rupees. In contrast, TOC decreased from 6495.45 rupees to 6150.75 rupees from sub case 2 to sun case 3.

Impact of temporal resolution on the design and reliability of residential energy systems

Future energy systems incorporating high shares of intermittent renewable energy sources are often designed using optimization-based, bottom-up energy system models. However, such models are generally limited to single years and hourly resolutions. This study quantifies the precision loss between hourly and sub-hourly-resolved data for the design and operation of a self-sufficient residential multi-energy system with respect to total costs, system design, and reliability using both averaging and sampling data methods. In this case study, the total annual cost is underestimated by 1.7% with the average hourly data relative to the fully-resolved minute resolution data, mainly due to the sizing of the photovoltaic inverter and battery. This is a result of the sub-hourly peaks in the supply and demand data that are evened out, significantly impacting the sub-electrical system. The results show up to 89 kWh of the annual lost load of the total electrical and thermal load, and a penalty cost of up to €894 (+24%) based on the value of the lost load. Another method, which employs regular sampling of the original time series, shows unpredictable behavior with respect to the tendency of either over- or underestimating system costs and components’ capacities depending on the selected samples. Both the sampling and averaging methods highlight that while hourly resolution may suffice for total system cost approximations, it falls short of sizing dynamically-operated components and meeting stringent reliability requirements. Future research may aim to enhance the temporal resolution of global intermittent renewable energy sources and reduce the computational expenses associated with minute-level resolutions.

Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources

Estimation of the power obtained from intermittent renewable energy sources (IRESs) is an important issue for the integration of these power plants into the power system. In this study, the expected power not served (EPNS) formula, a reliability criterion for power systems, is developed with a new method that takes into consideration the power generated from IRESs and the consumed power (CP) estimation errors. In the proposed method, CP, generated wind power (GWP), and generated solar power (GSP) predictions made with machine learning methods are included in the EPNS formulation. The most accurate prediction results were obtained with the Multi Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms used for prediction, and these results were compared. Using different forecasting methods, the relation between forecast accuracy, reserve requirement, and total cost was examined. Reliability, smart reserve planning (SRP), and total cost analysis for power systems were carried out with the CNN algorithm, which provides the most successful prediction result among the prediction algorithms used. The effect of increasing the limit EPNS value allowed by the power system operator, that is, reducing the system reliability, on the reserve requirement and total cost has been revealed. This study provides a useful proposal for the integration of IRESs, such as solar and wind power plants, into power systems.

Advances in Redox Flow Batteries – A Comprehensive Review on Inorganic and Organic Electrolytes and Engineering Perspectives

AbstractDevelopment and application of large‐scale energy storage systems are surging due to the increasing proportion of intermittent renewable energy sources in the global energy mix. Redox flow batteries are prime candidates for large‐scale energy storage due to their modular design and scalability, flexible operation, and ability to decouple energy and power. To date, several different redox couples are exploited in redox‐flow batteries; some are already commercialized. This battery technology is facing a lot of challenges in the science, engineering, and economic front. Issues plaguing flow batteries are low energy density, high overall cost, poor stability of electrolytes, shifting of solvent from anolyte to catholyte while using cation exchange membrane, reverse flow with anion exchange membrane, and corrosion of graphite felt in the catholyte side. Significant research efforts are ongoing to address these challenges. This comprehensive and critical review summarizes the recent progress in electrolyte technologies, including electrochemical performance and stability, strategies to enhance the energy and power densities and, in the end, the levelized and life‐cycle cost of these batteries analyzed. A comprehensive outlook on this technology with respect to practical energy storage applications is also provided.

Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems

Hybrid energy systems (HESs) integrate renewable sources, storage, and optionally conventional energies, offering a sustainable alternative to fossil fuels. Microgrids (MGs) bolster this integration, enhancing energy management, resilience, and reliability across different levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010 to 2023, analyzed using SciMAT v1.1.04 software. It segments the research into two periods, 2010–2019 and 2020–2023, revealing a surge in MOO focus, particularly in the latter period, with a 35% increase in MOO-related research. This indicates a shift toward comprehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of artificial intelligence (AI) in optimizing HMGS within the MOO framework signals a move toward more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

Charging the Future: Harnessing Nature's Designs for Bioinspired Molecular Electrodes.

The transition toward electric-powered devices is anticipated to play a pivotal role in advancing the global net-zero carbon emission agenda aimed at mitigating greenhouse effects. This shift necessitates a parallel focus on the development of energy storage materials capable of supporting intermittent renewable energy sources. While lithium-ion batteries, featuring inorganic electrode materials, exhibit desirable electrochemical characteristics for energy storage and transport, concerns about the toxicity and ethical implications associated with mining transition metals in their electrodes have prompted a search for environmentally safe alternatives. Organic electrodes have emerged as promising and sustainable alternatives for batteries. This review paper will delve into the recent advancements in nature-inspired electrode design aimed at addressing critical challenges such as capacity degradation due to dissolution, low operating voltages, and the intricate molecular-level processes governing macroscopic electrochemical properties.

COMPARING THE COSTS OF LONG-DISTANCE COMPRESSED AIR ENERGY TRANSMISSION VERSUS ELECTRICAL ENERGY TRANSMISSION

This paper conducts a comprehensive analysis of the construction cost associated with long-distance, large-scale Compressed Air Energy transmission via pipelines compared to electricity as a main energy carrier. The study indicates notable disparities in terms of compressed air energy transmission costs versus electrical transmission. The lower cost emphasizes the cost-effectiveness of Compressed Air Energy as a promising and economical method for energy transmission over varying distances. Another advantage of transmitting energy through compressed air pipelines is that it provides an opportunity to integrate the intermittent renewable energy sources such as solar and wind power more effectively. It puts a spotlight on Compressed Air Energy pipelines as a key topic for discussion and further investigation.

Evaluation of wavy wall configurations for accelerated heat recovery in triplex-tube energy storage units for building heating applications

Efficient thermal energy storage (TES) is crucial for integrating intermittent renewable energy sources and managing fluctuations in energy supply and demand. Among TES methods, latent heat TES (LHTES) using phase change materials (PCMs) is highly promising due to its high energy storage density and nearly isothermal operation during phase transitions. However, the inherently low thermal conductivity of PCMs hinders heat transfer rates, negatively impacting charging and discharging performance. This study investigates novel wavy channel geometries for the PCM container in a triplex-tube LHTES unit to enhance heat transfer and improve solidification rates during the discharge process. A comprehensive computational model based on the enthalpy-porosity method is developed to simulate the PCM (paraffin wax RT35) solidification inside wavy channels with sinusoidal, zigzag, and step-function profiles. The effects of channel geometry, heat transfer fluid (HTF) velocity, and inlet temperature on solidification rate and heat recovery are systematically evaluated. Results demonstrate that the step-function geometry significantly accelerates solidification compared to straight channels, reducing discharge time by 65.1 % and increasing heat recovery rate by 147.9 %. Decreasing waviness width from 15 mm to 5 mm further reduces solidification time by 70.1 %, while increasing waviness height from 5 mm to 15 mm leads to a 74.4 % reduction. Moreover, increasing HTF flow from Re = 250 to 1000 enhances heat recovery by 92.3 %, and lowering inlet temperature from 20 °C to 10 °C improves it by 76.9 %. The findings provide valuable insights for designing efficient LHTES units with wavy channel geometries for renewable energy integration and thermal management applications.

Towards Energy Efficient Cloud: A Green and Intelligent Migration of Traditional Energy Sources

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly.

Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns

Optimal microgrid design is pivotal in planning active distribution networks (ADNs) with intermittent renewable energy sources (RESs) and battery energy storage systems (BESSs). This paper introduces an innovative approach to clustering existing ADN systems, incorporating RESs and BESSs into a set of microgrids (MGs) termed a multi-microgrid (MMG). The approach accommodates diverse load patterns, considering the dynamic nature of residential, commercial, and industrial loads. The proposed methodology employs an innovative k-means algorithm that utilizes end node characteristics based on the graph partitioning method and the Silhouette technique, transforming the existing system into an MMG framework. Furthermore, an iterative pareto-fuzzy (IPF) method is suggested to determine optimal RES and BESS sizes, considering reliability, total cost, and the unutilized surplus power function. The framework integrates total line losses into the generation adequacy assessment of energy management strategy (EMS) for each MG within the MMG system. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE 33-bus distribution system. The implementation of the independent operation index (IOI) and round-trip efficiency (RTE) metrics demonstrates the effectiveness of the proposed approach in constructing an MMG, with achieved values exceeding 90 percent for IOI and 77 percent for RTE.

Optimal scheduling of electric vehicle aggregators for frequency regulation and cost efficiency in renewable-powered grids

The growing integration of intermittent renewable energy sources (RESs), such as wind and solar, into power grids presents significant operational challenges, particularly in maintaining grid stability and managing generation forecasting errors. This study underscores the critical role of Electric Vehicles (EVs) as a flexible load in providing ancillary services, especially for secondary frequency regulation, to address these challenges. Moreover, a novel scheduling model for EV aggregators is introduced that aims to optimize frequency regulation and cost efficiency. This model strategically manages the State of Charge (SOC) to ensure EV owner satisfaction while minimizing operational costs. Employing Mixed-Integer Linear Programming (MILP), solved via CPLEX-GAMS in both deterministic and stochastic scenarios, the proposed approach evaluates various EV integration strategies, demonstrating potential reductions in charging costs and enhancing owner participation by considering parking constraints. This research highlights the importance of innovative grid management solutions in the era of renewable energy proliferation, offering significant insights into cost-effective and reliable energy practices. The results in the deterministic show that by considering the EV parking constraint, the total charging cost will be increased by about 20%. Also, by applying the proposed strategy to the down-regulation value of 18%, the total cost of EV charging is decreased to 11,400 $ which shows about a 5% decrement in comparison with the down-regulation value of 32%.

Incorporating operational constraints into long-term energy planning: The case of the Egyptian power system under high share of renewables

This study assesses the performance of Egypt's energy system on a short-term basis to ensure that it can handle a high share of renewable energy, as predicted in the long-term planning up to 2040. PLEXOS software is used for short-term analysis to simulate operational performance on an hourly basis while considering the constraints of Unit Commitment (UC) and Economic Dispatch (ED). The long-term energy planning model, which covered the years 2020–2040 with chosen representative years (2020 as the base year and 2025, 2030, 2035, and 2040), was validated by the short-term operational model. The results from the short-term analysis indicate that by the year 2040, 69.5 % of the electricity mix from renewable energy sources (excluding hydro) can be accommodated, representing a slightly lower capacity than the 70 % predicted by the long-term plan. Due to the high share of intermittent renewable energy sources, traditional generators will experience excessive cycling in 2040. The average weighted short-run marginal cost in 2040 is projected to be 7.8 USD/MWh, compared to 18.37 USD/MWh in 2020, due to the low operating costs of renewable energy plants and their high penetration in 2040. Various operational techniques have been suggested to reduce the curtailment of renewable energy to achieve only 1.2 % of the total energy generation of the system by 2040. Additionally, other adjustments to the system operation are being considered to maintain a balance in the power system on a short-term basis.

A Review on the Recent Advances in Battery Development and Energy Storage Technologies

Energy storage is a more sustainable choice to meet net-zero carbon foot print and decarbonization of the environment in the pursuit of an energy independent future, green energy transition, and uptake. The journey to reduced greenhouse gas emissions, increased grid stability and reliability, and improved green energy access and security are the result of innovation in energy storage systems. Renewable energy sources are fundamentally intermittent, which means they rely on the availability of natural resources like the sun and wind rather than continuously producing energy. Due to its ability to address the inherent intermittency of renewable energy sources, manage peak demand, enhance grid stability and reliability, and make it possible to integrate small-scale renewable energy systems into the grid, energy storage is essential for the continued development of renewable energy sources and the decentralization of energy generation. Accordingly, the development of an effective energy storage system has been prompted by the demand for unlimited supply of energy, primarily through harnessing of solar, chemical, and mechanical energy. Nonetheless, in order to achieve green energy transition and mitigate climate risks resulting from the use of fossil-based fuels, robust energy storage systems are necessary. Herein, the need for better, more effective energy storage devices such as batteries, supercapacitors, and bio-batteries is critically reviewed. Due to their low maintenance needs, supercapacitors are the devices of choice for energy storage in renewable energy producing facilities, most notably in harnessing wind energy. Moreover, supercapacitors possess robust charging and discharging cycles, high power density, low maintenance requirements, extended lifespan, and are environmentally friendly. On the other hand, combining aluminum with nonaqueous charge storage materials such as conductive polymers to make use of each material’s unique capabilities could be crucial for continued development of robust storage batteries. In general, energy density is a key component in battery development, and scientists are constantly developing new methods and technologies to make existing batteries more energy proficient and safe. This will make it possible to design energy storage devices that are more powerful and lighter for a range of applications. When there is an imbalance between supply and demand, energy storage systems (ESS) offer a way of increasing the effectiveness of electrical systems. They also play a central role in enhancing the reliability and excellence of electrical networks that can also be deployed in off-grid localities.

Hierarchical Coordinated Control Strategy for Active Power in Distribution Networks with Scaled Distributed Generation Access

Promoting scaled distributed wind turbines and photovoltaic, and other distributed generations access to the distribution network is an important measure for realize the goal of carbon peaking and carbon neutrality and to build new-type power systems. However, as the installed capacity of wind turbine, photovoltaic and other intermittent renewable energy sources climbs dramatically, the security and consumption problems of the distribution network are becoming increasingly prominent, and there is an urgent requirement to improve the active power control capability of distributed generation. To optimize the operation stability and consumption economy of the distribution network, a hierarchical coordinated control strategy for active power of scaled distributed generation is proposed based on the principle of unified control and hierarchical management, including peaking control, frequency control, mesh control and market trading control, to coordinate and optimize the system operation safety and consumption economy. The proposed strategy optimizes active power control instructions in the layer of distribution network layer, mesh layer, and functional area layer, thus fully expanding the depth and breadth of the consumption for distributed generation. The effectiveness of the proposed strategy is proved by the actual operation of Liaoning power grid.

The current state of transition metal-based electrocatalysts (oxides, alloys, POMs, and MOFs) for oxygen reduction, oxygen evolution, and hydrogen evolution reactions

Climate change is showing its impacts now more than ever. The intense use of fossil fuels and the resulting CO2 emissions are mainly to blame, accentuating the need to develop further the available energy conversion and storage technologies, which are regarded as effective solutions to maximize the use of intermittent renewable energy sources and reduce global CO2 emissions. This work comprehensively overviews the most recent progress and trends in the use of transition metal-based electrocatalysts for three crucial reactions in electrochemical energy conversion and storage, namely, the oxygen evolution (OER), oxygen reduction (ORR), and hydrogen evolution (HER) reactions. By analyzing the state-of-the-art polyoxometalates (POMs) and metal-organic frameworks (MOFs), the performance of these two promising types of materials for OER, ORR, and HER is compared to that of more traditional transition metal oxides and alloy-based electrocatalysts. Both catalytic activity and stability are highly influenced by the adsorption energies of the intermediate species formed in each reaction, which are very sensitive to changes in the microstructure and chemical microenvironment. POMs and MOFs allow these aspects to be easily modified to fine-tune the catalytic performances. Therefore, their chemical tunability and versatility make it possible to tailor such properties to obtain higher electrocatalytic activities, or even to obtain derived materials with more compelling properties towards these reactions.