Search

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data. [ABSTRACT FROM AUTHOR]

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

Variable renewable energy sources (VRESs) are essential for decarbonizing the energy sector, but they introduce significant uncertainty into power grids. This uncertainty necessitates increased flexibility to ensure reliable and efficient grid operations, impacting both short-term strategies and long-term planning. Effective management of VRESs is particularly challenging for distribution system operators (DSOs) due to limited grid measurement and other data, complicating state estimation. This paper proposes a tractable framework that operates under low-observability conditions. The framework uses conservative linear approximations (CLAs) to manage grid constraints efficiently, requiring only the ranges of power injections typically available to operators. The objectives are twofold: first, to determine the amount and location of flexibility needed to prevent voltage violations and, second, to establish voltage measurement thresholds to trigger flexibility activation. Key contributions include the formulation of optimization problems to mitigate voltage issues, the introduction of flexibility provision triggered by voltage thresholds, and novel algorithms for determining flexibility and trigger points. The framework's efficacy is demonstrated on IEEE 33-bus and UK 124-bus test systems, showing it can effectively mitigate grid voltage problems despite limited data. [ABSTRACT FROM AUTHOR]

With a substantial fraction of renewable energy integrated into the electrical grid, the new power system urgently requires grid planning scheme displaying adaptability to different energy types and their volatility. Considering the indeterminacy of renewable energy generation output and the different attitudes of decision-makers towards its risk, this paper proposes an adaptability assessment methodology for power grid planning schemes considering multiple decision psychology. First, an evaluation indicator framework is established based on the adaptive requirements of the grid planning for novel power system, and the weights of indicators are calculated based on an improved AHP-CRITIC combination weighting method. Second, improved cumulative prospect theory (ICPT) is adopted to improve to the calculation method of the distance between the evaluation program and the positive and negative ideal programs in the GRA and TOPSIS, which effectively characterize the different decision-making psychologies, and a combination evaluation model is constructed based on a cooperative game (CG), namely, an adaptability evaluation model of grid planning schemes for novel power systems based on GRA-TOPSIS integrating CG and ICPT. Finally, the proposed model serves to evaluate grid planning schemes of three regions in China's 14th Five-Year Plan. The evaluation results show that the adaptability of the schemes varies under different decision-making psychologies, and under the risk-aggressive and loss-sensitive decision-making psychologies, grid planning scheme of Region 1 with the greatest accommodation capacity of renewable energy is preferable. [ABSTRACT FROM AUTHOR]

This paper proposes a variable-power-tracking-operation-based frequency regulation scheme for a wind turbine generator (WTG) that employs a frequency-excursion-based control parameter to suppress frequency excursion under various wind speeds and disturbances. To this end, an improved system frequency response model is built, and then the frequency regulation characteristics of the WTG are investigated. The frequency-excursion-based control parameter is determined. Simulations based on the Matlab/Simulink simulator verify the effectiveness of the proposed variable-power-tracking-operation-based frequency regulation strategy. The simulation results clearly indicate that the suggested scheme is beneficial for reducing the maximum frequency deviation. The proposed frequency regulation scheme could enhance the networking capacity of wind farms and improve the capacity of the power grid to absorb renewable energy. [ABSTRACT FROM AUTHOR]

This paper introduces a new hybrid DC–DC converter with enhanced voltage gain and synchronized multiple output capabilities, specifically tailored for smart grid applications. The proposed converter is based on the integration of non-isolated Zeta and Mahafzah converters, comprising a single controlled switch, two diodes, three inductors, and two coupling capacitors. The primary objective of this novel hybrid converter is to improve voltage gain as compared to conventional Zeta and Mahafzah topologies. By achieving higher voltage gain at lower duty cycles, the converter effectively reduces voltage stress on semiconductor switches and output diodes, thereby enhancing overall performance and reliability. A comprehensive examination of the hybrid converter's operating principle is presented, along with detailed calculations of duty cycle and switching losses. The paper also explores the converter's application in smart grids, specifically in the context of renewable energy systems and electric vehicles. Two distinct scenarios are analyzed to evaluate the converter's efficacy. Firstly, the converter is assessed as a DC–DC converter for renewable energy systems, highlighting its relevance in sustainable energy applications. Secondly, the converter is evaluated as an electric vehicle adapter, showcasing its potential in the transportation sector. To validate the converter's performance, extensive simulations are carried out using MATLAB/SIMULINK with parameters set at 25 kW, 200 V, and 130 A. The simulation results demonstrate the converter's ability to efficiently supply multiple loads with opposing energy flows, making it a promising technology for optimized grid management and energy distribution. Moreover, the paper investigates the total harmonic distortion (THD) of the grid current, focusing on its impact in smart grid environments. Notably, the new hybrid converter topology achieves a THD of 21.11% for the grid current, indicating its ability to effectively mitigate harmonics and improve power quality. Overall, this research introduces a cutting-edge hybrid DC–DC converter that enhances voltage gain and synchronizes multiple outputs, specifically catering to the requirements of smart grid applications. The findings underscore the converter's potential to significantly contribute to the advancement of efficient and resilient power conversion technologies for smart grids, enabling seamless integration of renewable energy systems and electric vehicles into the grid. [ABSTRACT FROM AUTHOR]

This document discusses the challenges of achieving high-power quality in modern power grids with a large share of distributed power production, particularly due to the integration of renewable energy sources (RES). The variable nature of RES can lead to disturbances and power quality degradation. The document highlights the importance of voltage source converters and control systems in enabling the flexible operation of RES. It also mentions the concept of microgrids, which are closely connected to power quality and other factors such as the Internet of Things and cyber security. The document includes eight research papers covering various topics related to power quality analysis and control of renewable energy sources in power grids. These topics include power factor correction, supercapacitor parameters identification, pulse width modulation, disturbance analysis, vibration analysis of motors, control of converters, phasor measurement units, and optimization of controller parameters. The document concludes by expressing gratitude to the authors, staff, and reviewers involved in the research papers. [Extracted from the article]

The expansion of renewable electricity storage technologies, including green hydrogen storage, is spurred by the need to address the high costs associated with hydrogen storage and the imperative to increase storage capacity. The initial section of the paper examines the intricacies of storing electricity generated from renewable sources, particularly during peak periods, through green hydrogen. Two primary challenges arise: firstly, the complexity inherent in the storage technology and its adaptation for electricity reproduction; and secondly, the cost implications throughout the technological chain, resulting in a significant increase in the price of the reproduced energy. Electric energy storage emerges as a pivotal solution to accommodate the growing proportion of renewable energy within contemporary energy systems, which were previously characterized by high stability. During the transition to renewable-based energy systems, optimizing energy storage technology to manage power fluctuations is crucial, considering both initial capital investment and ongoing operational expenses. The economic analysis primarily focuses on scenarios where electricity generated from renewable sources is integrated into existing power grids. The subsequent part of this paper explores the possibility of localizing excess electricity storage within a specific system, illustrated by domestic hot water. [ABSTRACT FROM AUTHOR]

Power systems with a high proportion of inverter-based sources like photovoltaics require a substantial short-circuit current ratio to ensure strong voltage support capabilities. However, this also increases the system's short-circuit current capacity and levels, which may potentially affect the safe operation of system equipment and current-carrying conductors. To evaluate the operational risks, this paper proposes a quantitative calculation model for the critical integration proportion of grid-connected inverter-interfaced power sources based on short-circuit current over-limit probability. Firstly, according to the verification criterion about short-circuit current during the selection of the switching equipment and conductors in the power system, the short-circuit current over-limit probability evaluation system with five indices is established. Secondly, considering the impact of the increased grid integration proportion of inverter-based power sources on short-circuit currents, an evaluation process for operational risk probabilities is proposed. Based on this, the critical access proportion for inverter-based power source integration in the power grid can be calculated. Finally, the proposed model is used to assess the operational risk probability for a 220 kV system which includes a high proportion of inverter-based power sources in the Anhui area, China. Through the analysis of cumulative probability distribution curves, the critical grid integration proportion interval for the transition from normal to high proportion stages of inverter-based power sources is determined. [ABSTRACT FROM AUTHOR]

Microgrids are small power systems, often equipped with renewable energy sources, that are alternatives or supplementary to utility grids. Many studies have been conducted on the design and implementation of microgrids and their interconnects to utility grids, and investigations have been extended to the use of Internet of Things technology (IoT) to monitor and operate such power grids. However, the broad applications of the IoT technology itself also call for a green energy solution. This paper investigates how to power local IoT applications via an integration of a microgrid and the utility grid. Together, we call such a system an IoT microgrid. The goal of an IoT microgrid is to maintain the availability of IoT applications while saving energy costs, and this is achieved by sustaining IoT applications via local renewable energy from a microgrid and by mitigating the intermittent power supply using the utility grid. This paper characterizes the IoT microgrid and proposes a configurable cyber-physical testbed for its design and validation. The testbed incorporates the hardware-in-the-loop (HIL) approach, where real-time simulation is integrated with physical elements for quick prototyping of those components in an IoT microgrid. The paper concludes with an example implementation of the proposed testbed, which demonstrates its use for validating both an IoT microgrid and the IoT application it sustains. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology. [ABSTRACT FROM AUTHOR]

To cope with the energy crisis and environmental pollution, the future development of the power system has to change towards a clean, low-carbon, flexible, and diversified direction. This paper proposes a decentralized power dispatching model based on blockchain technology to address the problems of uncertainty, privacy, security, and reliability in power dispatching systems containing renewable energy and flexible loads. Considering the uncertainty of wind, photovoltaic, and flexible load integration into the power grid, the total generation costs of the system are established, and the smart contracts of the decentralized power dispatching are proposed. The proof of work (PoW) consensus mechanism is improved in this paper. The hash operation that must be repeated in the PoW algorithm is replaced by an optimized computation process using a blockchain-based genetic algorithm (BD-GA). The proof of work-load-genetic algorithm-based (PoW-GAD) consensus algorithm is proposed. The decentralized power dispatching model and improved consensus algorithms' effectiveness was confirmed by simulation. The power dispatching method in this paper reduces the system cost and increases wind and photovoltaic usage. The improved PoW-GAD algorithm, while inheriting the security features of the PoW algorithm, adapts to the blockchain-based decentralized dispatching structure and enhances system security. [ABSTRACT FROM AUTHOR]

Featured Application: Optimal operation for renewable energy integrated smart microgrid under uncertainty. Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park's peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology's business schemes are investigated. Furthermore, the challenges faced by the V2G technology's business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology's research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector. [ABSTRACT FROM AUTHOR]

This paper's research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car's charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time. [ABSTRACT FROM AUTHOR]

The large-scale integration of new energy generators into the power grid poses a potential threat to its stable operation due to broadband oscillations. The rapid and accurate localization of oscillation sources is fundamental for mitigating these risks. To enhance the interpretability and accuracy of broadband oscillation localization models, this paper proposes a broadband oscillation localization model based on deep learning, integrating TimesNet and Autoformer algorithms. This model utilizes transmission grid measurement sampling data as the input and employs a data-driven approach to establish the broadband oscillation localization model. TimesNet improves the model's accuracy significantly by decomposing the measurement data into intra- and inter-period variations using dimensional elevation, tensor transformation, and fast Fourier transform. Autoformer enhances the ability to capture oscillation features through the Auto-Correlation mechanism. A typical high-proportion renewable energy system was constructed using CloudPSS to create a sample dataset. Simulation examples validated the proposed method, demonstrating it as a highly accurate solution for broadband oscillation source localization. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal scheduling of the resources of a renewable energy community, whose coordination is aimed at providing flexibility services to the electrical distribution network. The available resources are renewable generation units, battery energy storage systems, dispatchable loads, and power-to-hydrogen systems. The main purposes behind the proposed strategy are enhancement of self-consumption and hydrogen production from local resources and the maximization of the economic benefits derived from both the selling of hydrogen and the subsidies given to the community for the shared energy. The proposed approach is formulated as an economic problem accounting for the perspectives of both community members and the distribution system operator. In more detail, a mixed-integer constrained non-linear optimization problem is formulated. Technical constraints related to the resources and the power flows in the electrical grid are considered. Numerical applications allow for verifying the effectiveness of the procedure. The results show that it is possible to increase self-consumption and the production of green hydrogen while providing flexibility services through the exploitation of community resources in terms of active and reactive power support. More specifically, the application of the proposed strategy to different case studies showed that daily revenues of up to EUR 1000 for each MW of renewable energy generation installed can be obtained. This value includes the benefit obtained thanks to the provision of flexibility services, which contribute about 58% of the total. [ABSTRACT FROM AUTHOR]

Actuators play a crucial role in modern distributed electric grids and renewable energy network architectures, implementing control actions based on sensor data to ensure optimal system performance and stability. This paper addresses the economic dispatch (ED) problem of distributed DC microgrids with renewable energy. In these systems, numerous sensors and actuators are integral for monitoring and controlling various parameters to ensure optimal performance. A new event-triggered distributed optimization algorithm in the discrete time domain is employed to ensure the minimum production cost of the power grid. This algorithm leverages data from sensors to make real-time adjustments through actuators, ensuring the maximum energy utilization rate of renewable generators (RGs) and the minimum cost of conventional generators (CGs). It realizes the optimal synergy between conventional energy and renewable energy. Compared to the continuous sampling optimization algorithm, the event-triggered control (ETC) optimization algorithm reduces the frequency of communication and current sampling, thus improving communication efficiency and extending the system's lifetime. The use of actuators in this context is crucial for implementing these adjustments effectively. Additionally, the convergence and stability of the DC microgrid are proven by the designed Lyapunov function. Finally, the effectiveness of the proposed optimization algorithm is validated through simulations of the DC microgrid. [ABSTRACT FROM AUTHOR]

As renewable energy becomes more widespread, the uncertainty of its output poses serious challenges for peak and frequency regulation of the power system. Evaluating a grid's capacity to integrate renewable energy sources can provide an early-warning and decision-making basis for grid operation and scheduling. This paper presents a method for evaluating the hosting capacity of renewable energy, considering frequency security constraints. Introducing the system frequency nadir constraint into a system ensures that the frequency does not drop to a dangerous level in the event of power disturbances. The analytical characterization relation equation for the system frequency nadir constraint is constructed based on polynomial chaos expansion (PCE) theory. Furthermore, with the goal of minimizing the reduction in renewable energy, considering multiple flexible resources, like demand response (DR), Combined Heat and Power (CHP), energy storage, and Power-to-Gas (P2G), a renewable energy hosting capacity evaluation model that considers frequency security and flexibility resources is established. Finally, based on the concept of the feasible region, the maximum hosting capacity of a system's renewable energy is visualized using the progressive vertex enumeration method. It identifies the safe operating region for renewable energy output that meets the safety constraints of power grid operations. The simulation results were validated using a modified IEEE 39 bus system. [ABSTRACT FROM AUTHOR]

Power grids are progressing, and the possibility of incorporating DC grids toward hybrid AC/DC grids is gaining increasing relevance, as several technologies available nowadays are operating natively in DC. This paper proposes a topology of a full-controlled bidirectional dual-stage interleaved converter for interfacing hybrid AC/DC grids. The topology is based on a dual-stage architecture, constituted by an AC/DC converter and by a DC/DC converter, both based on interleaved power converters. On the AC side, which is connected to the main AC power grid, the proposed dual-stage architecture operates with sinusoidal current in phase or phase opposition with the voltage, meaning a bidirectional operation. In addition, it has the possibility of interfacing with other AC loads, such as domestic electrical appliances, or with an AC microgrid. The DC link, formed by the AC/DC power stage, is interfaced with a DC power grid, which provides numerous advantages, e.g., for interfacing battery electric vehicles directly charged in DC, as well as other DC loads, such as renewable energy sources. The DC/DC power stage is considered for interfacing with an energy storage system, which is capable of bidirectional power exchange with the DC grid or with the AC grid through the AC/DC power stage. A complete laboratory prototype was designed and developed, with the unified control algorithms implemented on a digital signal processor. The experimental results validated the operation of the full-controlled bidirectional dual-stage interleaved converter based on the specifications for the hybrid AC/DC grid, such as bidirectional operation, synchronization with the AC power grid, predictive current control, interleaved operation on both AC/DC and DC/DC power stages, DC-link voltage control for the DC grid, as well as the operation with different power levels. [ABSTRACT FROM AUTHOR]

To face the growing electricity demand, several countries have adopted the solution of clean energy and use renewable energy sources (e.g., wind and solar) to reinforce the stability of the power network, especially during peak demand periods. Forecasting wind power generation is one of the important tasks for the network regulator. This paper deals with the probabilistic forecasting of hourly wind speed time series. In this approach, instead of evaluating a single-point forecast, an intraday interval prediction is provided, which allows modeling the probability distribution of the wind speed process at any specific hour. Practically, the quantification of uncertainty might be of particular interest for risk management purposes associated with wind power generation. The definition of interval prediction is based on the notion of conditional quantiles. In this paper, we introduce a new statistical approach, which deals with the nonstationarity behavior of the wind speed process, to define the conditional quantile predictor. The proposed approach was applied and evaluated on hourly wind speed processes. The suggested methodology provides accurate single-point forecasts using the conditional median as the predictor. Furthermore, the obtained hourly interval predictions are small and well adapted to the shape of the daily wind speed curves, which confirms the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples. [ABSTRACT FROM AUTHOR]

In light of the recent announcement of the primary construction objectives of the modern energy system during the "14th Five-Year Plan", the renewable energy industry has experienced rapid growth. The accurate assessment of the effects of renewable energy accommodation driven by various factors under the synergistic influence of "Source-Grid-Load-Storage" is vital for guiding the scientific planning and rational arrangement of the future energy system. For this purpose, this paper comprehensively considers boundary conditions such as power demand, load characteristics, cross-regional transmission characteristics, renewable energy resources and output characteristics, as well as energy storage characteristics. Based on the principle of simulation of time series production, this paper establishes a model for evaluating renewable energy accommodation and introduces a continuous optimization solution method. Taking the renewable energy accommodation of the power grid in a Chinese region as a case, this paper constructs 16 representative scenes that satisfy the development plans of various factors in the region. In conjunction with the simulation results of these 16 scenes, this paper uses the Shapley value method to determine the increased accommodation capacity of renewable energy promoted by multiple factors under the synergistic effect. The analysis results show that the Shapley value method examines the entire development process from the current situation to the synergistic scenes. By comprehensively weighing all development scenes regarding the increased accommodation capacity of various factors, this paper quantifies the effects of each factor under the synergistic scenes. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources (RESs) in transmission and distribution systems presents several challenges for grid operators. In particular, the unpredictable behavior of RESs can disrupt the balance between energy production and load demand, potentially affecting the stability of the entire system. Grid-connected energy storage systems (ESSs) offer a possible solution to manage the uncertainty associated with RESs. In fact, ESSs exchange power with the grid through the adoption of suitable energy management strategies, which are typically implemented by power electronics-based grid interfaces. Unlike other current source converter (CSC) solutions described in the literature, which only interface with a single energy storage device, this paper introduces a novel topology for a three-phase delta-type current source converter (D-CSC), which is capable of integrating three independent ESSs using the same number of semiconductors as traditional CSC solutions. Thus, it considerably enhances the flexibility of a power conversion system (PCS) without increasing the number of converter components. In addition, an innovative energy management control strategy is also introduced. This strategy enables the D-CSC to compensate for energy imbalances arising between the three ESSs, which might be caused by several factors, such as different aging characteristics, converter component tolerances, operating conditions, and temperature drifts. Hence, the D-CSC-based interface is capable of proper grid operation even if the three ESSs have different characteristics, thus opening the possibility of employing this converter to integrate both first and second-life devices. First, the topology of the proposed D-CSC is introduced, followed by a detailed mathematical description of its control strategy. The proper grid operation of the D-CSC was tested under different scenarios, considering the grid integration of three independent superconducting magnetic energy storage systems in a marine vessel. The proposed D-CSC is compared to traditional CSC solutions, highlighting the superior performances of the novel converter topology in terms of efficiency, total harmonic distortion of the output currents, and overall cost reduction for the PCS. [ABSTRACT FROM AUTHOR]

With the rapid growth in the number of EVs, a huge number of EVs are connected to the power grid for charging, which places a great amount of pressure on the stable operation of the power grid. This paper focuses on the development of V2G applications, based on the current research status of V2G technology. Firstly, the standards on V2G applications and some pilot projects involving more representative V2G systems are introduced. Comparing V2G applications with ordered charging and unordered charging, the social and economic benefits of V2G applications are highlighted. Analysis of the social benefits of V2G applications concerns three points: the grid demand response, personalized charging, and the coordination of renewable energy sources. And analysis of the economic benefits of V2G applications is divided into three parties: the grid, the aggregator, and individuals. From the perspective of innovative EVs expanding the application scenarios through V2G technology, V2G applications for commercial EVs, emergency power applications, and vehicle-to-vehicle energy trading are introduced. The current challenges related to V2G applications are presented: users' willingness to participate in V2G applications, battery loss, charging and discharging tariffs, privacy and security, and power loss. Finally, some research recommendations for the development of V2G applications are given and the current state of research in regard to those recommendations is presented. [ABSTRACT FROM AUTHOR]

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia's grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia. [ABSTRACT FROM AUTHOR]

Due to their efficient renewable energy consumption performance, AC/DC hybrid microgrids have become an important development form for future power grids. However, the fault response will be more complex due to the interconnected structure of AC/DC hybrid microgrids, which may have a serious influence on the safe operation of the system. Based on an AC/DC hybrid microgrid with an integrated bidirectional power converter, research on the interaction impact of faults was carried out with the purpose of enhancing the safe operation capability of the microgrid. The typical fault types of the DC sub-grid were selected to analyze the transient processes of fault circuits. Then, AC current expressions under the consideration of system interconnection structure were derived and, on this basis, we obtained the response results of non-fault subnets under the fault process, in order to reveal the mechanism of DC fault propagation. Subsequently, a current limitation control strategy based on virtual impedance control is proposed to address the rapid increase in the DC fault current. On the basis of constant DC voltage control in AC/DC hybrid microgrids, a virtual impedance control link was added. The proposed control strategy only needs to activate the control based on the change rate of the DC current, without additional fault detection systems. During normal operations, virtual impedance has a relatively small impact on the steady-state characteristics of the system. In the case of a fault, the virtual impedance resistance value is automatically adjusted to limit the change rate and amplitude of the fault current. Finally, a DC fault model of the AC/DC hybrid microgrid was built on the RTDS platform. The simulation and experimental results show that the control strategy proposed in this paper can reduce the instantaneous change rate of the fault state current from 19.1 kA/s to 2.73 kA/s, and the error between the calculated results of equivalent modeling and simulation results was within 5%. The obtained results verify the accuracy of the mathematical equivalent model and the effectiveness of the proposed current limitation control strategy. [ABSTRACT FROM AUTHOR]

With the advancement of regulations regarding the reduction in carbon emissions, renewable energy communities have come into the picture. However, many implications come with the installation of these communities from a cybersecurity point of view. The software platforms responsible for managing and controlling them handle a lot of crucial information, and therefore, tampering with these data can lead to several impacts on the operation of these communities and, in turn, the power grid as well. This paper examines the plausible impacts that can be caused by altering certain parameters of the system that make it a potential cyber attack target. The analysis is done by observing how the grid responds to these manipulations for both low-voltage as well as medium-voltage systems. These systems are designed along with integrated energy communities and are implemented in MATLAB/Simulink R2022b software. The observations are made by plotting the grid voltage and power profiles in normal as well as attacked conditions. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the scale of power generated by grid-connected renewable energy, the form and operation characteristics of power grids have undergone significant changes, and the power electronic characteristics of power systems are prominent, resulting in the frequent occurrence of wide-band oscillation problems when renewable energy power generation equipment is connected to a power grid. Oscillation has the characteristics of nonlinearity and has an oscillation frequency ranging from a few hertz to several thousand hertz or more, which seriously threatens the stable operation of power grids. This paper summarizes the wide-band oscillation events that occur worldwide under the background of renewable energy access to power grids, classifies the wide-band oscillation events according to the distribution of oscillation frequency bands, and sorts out the characteristics of various oscillations. From the perspectives of the source side and network side, the existing oscillation suppression measures are classified and prospected. [ABSTRACT FROM AUTHOR]

With the continuous development of renewable energy technologies, both domestically and internationally, the focus of energy research has gradually shifted towards renewable energy directions such as distributed photovoltaics and wind power. The penetration rate of renewable energy generation is constantly increasing, at the same time, the elements in the grid are becoming increasingly complex, and large-scale energy storage, as well as a variety of electricity loads such as electric vehicle charging piles and data centers are gradually appearing. Therefore, traditional distribution methods of the power grid are difficult to ensure the stable operation of the power system and cannot achieve efficient integration of renewable energy. Consequently, some scholars have proposed the concept of an energy internet. Compared to traditional power grids, the energy internet employs more comprehensive power electronics and communication technologies, enabling the interconnection of various new and traditional energy sources, and effectively integrating renewable energy. As the core device in the energy internet, the energy router plays a role in energy transformation and distribution, facilitating multi-information interconnection and multi-energy exchange within the energy internet. At the level of distribution network, the energy router can realize the efficient access of various forms of energy and the flexible control and management, which is of great significance for the optimal operation of distribution network. Against this backdrop, this paper reviews the development and current research status of energy routers, systematically analyzes the typical topologies and related control technologies of multi-port energy routers and summarizes and forecasts key issues and future development trends, aiming to provide thoughts and reference for subsequent related research. [ABSTRACT FROM AUTHOR]

In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverter-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative in enhancing the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state-space model and eigenvalues analysis are presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained, considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small-signal stability of the entire system. And the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. A case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical analysis of the selected properties of HCNG fuel calculations and a literature review of the other fuels that allow the storage of ecologically produced hydrogen. Hydrogen has the most significant CO2 reduction potential of all known fuels. However, its transmission in pure form is still problematic, and its use as a component of fuels modified by it has now become an issue of interest for researchers. Many types of hydrogen-enriched fuels have been invented. However, this article will describe the reasons why HCNG may be the hydrogen-enriched fuel of the future and why internal combustion (IC) piston engines working on two types of fuel could be the future method of using it. CO2 emissions are currently a serious problem in protecting the Earth's natural climate. However, secondarily, power grid stabilization with a large share of electricity production from renewable energy sources must be stabilized with very flexible sources—as flexible as multi-fuel IC engines. Their use is becoming an essential element of the electricity power systems of Western countries, and there is a chance to use fuels with zero or close to zero CO2 emissions, like e-fuels and HCNG. Dual-fuel engines have become an effective way of using these types of fuels efficiently; therefore, in this article, the parameters of hydrogen-enriched fuel selected in terms of relevance to the use of IC engines are considered. Inaccuracies found in the literature analysis are discussed, and the essential properties of HCNG and its advantages over other hydrogen-rich fuels are summarized in terms of its use in dual-fuel (DF) IC engines. [ABSTRACT FROM AUTHOR]

Power systems face adequacy risks because of the high integration of renewable energy. It is urgent to develop efficient methods for power system operational reliability assessment. Conventional power system reliability assessment methods cannot achieve real-time assessment of system risk because of the high computational complexity and long calculation time. The high computational complexity is mainly caused by a large number of optimal power flow (OPF) calculations. To reduce the computational complexity, this paper transfers the optimal power flow model as a multiparameter linear programming model. Then, the optimal power flow can be obtained by linear calculations. Furthermore, this paper proposes a state reduction method considering the importance index of transmission lines for further improving the calculation efficiency. Case studies are carried out on IEEE standard systems and a provincial power grid in China. Compared with the conventional reliability assessment method, the reliability assessment efficiency of the proposed method increases by 10–40 times, and the assessment error is less than 1%. [ABSTRACT FROM AUTHOR]

Recent developments in electrical power grids have witnessed high utilization levels of renewable energy sources (RESs) and increased trends that benefit the batteries of electric vehicles (EVs). However, modern electrical power grids cause increased concerns due to their continuously reduced inertia resulting from RES characteristics. Therefore, this paper proposes an improved fractional-order frequency controller with a design optimization methodology. The proposed controller is represented by two cascaded control loops using the one-plus-proportional derivative (1 + PD) in the outer loop and a fractional-order proportional integral derivative (FOPID) in the inner loop, which form the proposed improved 1 + PD/FOPID. The main superior performance characteristics of the proposed 1 + PD/FOPID fractional-order frequency controller over existing methods include a faster response time with minimized overshoot/undershoot peaks, an ability for mitigating both high- and low-frequency disturbances, and coordination of EV participation in regulating electrical power grid frequency. Moreover, simultaneous determination of the proposed fractional-order frequency controller parameters is proposed using the recent manta ray foraging optimization (MRFO) algorithm. Performance comparisons of the proposed 1 + PD/FOPID fractional-order frequency controller with existing PID, FOPID, and PD/FOPID controllers are presented in the paper. The results show an improved response, and the disturbance mitigation is also obtained using the proposed MRFO-based 1 + PD/FOPID control and design optimization methodology. [ABSTRACT FROM AUTHOR]

Wind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power prediction can avoid the adverse effects of wind power, and is of great significance for power grid frequency regulation, peak shaving, and energy improvement. However, traditional wind power prediction methods can only achieve accurate predictions in the short term and perform poorly in medium- to long-term prediction tasks. To address this issue, a power prediction model based on a Gated Transformer is proposed in this paper. Firstly, it can extract features from different types of data sources, effectively capture their correlations, and achieve data fusion. Secondly, gating unit, dilated convolution unit, and multi-head attention mechanism are added to improve the Receptive field and generalization ability of the model. In addition, adding a decoder to guide data prediction further improves the accuracy of prediction. Finally, experiments are carried out with the data collected from typical wind farms. The results show that the proposed Gated Transformer achieves consistent state-of-the-art results in prediction tasks on different time scales. [ABSTRACT FROM AUTHOR]

Compared with the traditional grid-following photovoltaic grid-connected converter (GFL-PGC), the grid-forming photovoltaic grid-connected converter (GFM-PGC) can provide voltage and frequency support for power systems, which can effectively enhance the stability of power electronic power systems. Consequently, GFM-PGCs have attracted great attention in recent years. When an asymmetrical short-circuit fault occurs in the power grid, GFM-PGC systems may experience transient instability, which has been less studied so far. In this paper, a GFM-PGC system is investigated under asymmetrical short-circuit fault conditions. A novel Q-V droop control structure is proposed by improving the traditional droop control. The proposed control structure enables the system to accurately control the positive- and negative-sequence reactive current without switching the control strategy during the low-voltage ride-through (LVRT) period so that it can meet the requirements of the renewable energy grid code. In addition, a dual-loop control structure model of positive- and negative-sequence voltage and current is established for the GFM-PGC system under asymmetrical short-circuit fault conditions. Based on the symmetrical component method, the composite sequence network of the system is obtained under asymmetrical short-circuit fault conditions, and positive- and negative-sequence power-angle characteristic curves are analyzed. The influence law of system parameters on the transient synchronous stability of positive- and negative-sequence systems is quantitatively analyzed through the equal area criterion. Finally, the correctness of the theoretical analysis is verified by simulation and hardware-in-the-loop experiments. [ABSTRACT FROM AUTHOR]

As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers. [ABSTRACT FROM AUTHOR]

With the integration of large-scale renewable energy generation, some new problems and challenges are brought for the operation and planning of power systems with the aim of mitigating the adverse effects of integrating photovoltaic plants into the grid and safeguarding the interests of diverse stakeholders. In this paper, a methodology for allotting capacity is introduced, which takes into account the active involvement of multiple stakeholders in the energy storage system. The objective model for maximizing the financial proceeds of the PV plant, the system for the storage of energy, and a power grid company is studied. Then, in order to maximize the benefit of three stakeholders, a modified particle swarm optimization algorithm is devised, employing the prevailing typical allocation strategy. Finally, a case study is provided based on the modified IEEE 14-bus and the actual power grid from South Xinjiang, China. The simulation results and findings of the case study conclusively illustrate that the proposed methodology adeptly ensures the maximization of interests for the triad of stakeholders. [ABSTRACT FROM AUTHOR]

The complexity of modern power grids, caused by integrating renewable energy sources, especially inverter-based resources, presents a significant challenge to grid operation and planning, since linear models are unable to capture the complex nonlinear dynamics of power systems with coupled muti-scale dynamics, and it necessitate an alternative approach utilizing more advanced and data-driven algorithms to improve modeling accuracy and system optimization. This study employs the sparse identification of nonlinear dynamics method by leveraging compressed sensing and sparse modeling principles, offering robustness and the potential for generalization, allowing for identifying key dynamical features with relatively few measurements, and providing deeper theoretical understanding in the field of power system analysis. Taking advantage of the this method in recognizing the active terms (first and high order) in the system's governing equation, this paper also introduces the novel Volterra-based nonlinearity index to characterize system-level nonlinearity. The distinction of dynamics into first-order linearizable terms, second-order nonlinear dynamics, and third-order noise is adopted to clearly show the intricacy of power systems. The findings demonstrate a fundamental shift in system dynamics as power sources transit to inverter-based resources, revealing system-level (second-order) nonlinearity compared to module-level (first order) nonlinearity in conventional synchronous generators. The proposed index quantifies nonlinear-to-linear relationships, enriching our comprehension of power system behavior and offering a tool for distinguishing between different nonlinearities and visualizing their distinct patterns through the profile of the proposed index. [ABSTRACT FROM AUTHOR]

In recent years, a pronounced transition to the exploitation of renewable energy sources has be observed worldwide, driven by current climate concerns and the scarcity of conventional fuels. However, this paradigm shift is accompanied by new challenges for existing power systems. Therefore, the hosting capacity must be exhaustively assessed in order to maximize the penetration of distributed generation while mitigating any adverse impact on the electrical grid in terms of voltage and the operational boundaries of the equipment. In this regard, multiple aspects must be addressed in order to maintain the proper functioning of the system following the new installations' capacities. This paper introduces a sequential methodology designed to determine the maximum hosting capacity of a power system through the optimal allocation of both active and reactive power. To achieve this goal, an Improved Salp Swarm Algorithm is proposed, aiming to establish the appropriate operational planning of the power grid considering extensive distributed generation integration, while still ensuring a safe operation. The case study validates the relevance of the proposed model, demonstrating a successful enhancement of hosting capacity by 14.5% relative to standard models. [ABSTRACT FROM AUTHOR]

The integration of renewable rnergy sources (RESs) into the power grid involves operational challenges due to the inherent RES energy-production variability. Imbalances between actual power generation and scheduled production can lead to grid instability and revenue loss for RES operators and aggregators. To address this risk, in this paper, we introduce a mutually beneficial bilateral trading scheme between a RES and a DR aggregator to internally offset real-time energy imbalances before resorting to the flexibility market. We consider that the DR aggregator manages the energy demand of users, characterized by uncertainty in their participation in DR events and thus the actual provision of flexibility, subject to their offered monetary incentives. Given that the RES aggregator faces penalties according to dual pricing for positive or negative imbalances, we develop an optimization framework to achieve the required flexibility while addressing the trade-off between maximizing the profit of the RES and DR aggregators and appropriately incentivizing the users. By using appropriate parameterization of the solution, the achievable revenue for the imbalance offsetting can be shared between the RES and the DR aggregators while keeping users satisfied. Our analysis highlights the interdependencies of the demand–production energy imbalance on user characteristics and the RES and DR aggregator profits. Based on our results, we show that a win–win outcome (for the RES and DR aggregators and the users) is possible for a wide range of cases, and we provide guidelines so that such bilateral agreements between RES and DR aggregators could emerge in practical settings. [ABSTRACT FROM AUTHOR]

The integration of renewable resources in isolated systems can produce instability in the electrical grid due to its intermintency. In today's microgrids, which lack synchronous generation, physical inertia is substituted for inertia emulation. To date, the most effective approach remains the frequency derivative control technique. Nevertheless, within this method, the ability to provide virtual drooping is often disregarded in its design, potentially leading to inadequate development in systems featuring high renewable penetration and low damping. To address this issue, this paper introduces an innovative design and analysis of virtual inertia control to simultaneously mimic droop and inertia characteristics in microgrids. The dynamic frequency response without and with renewable energy sources penetration is comparatively analyzed by simulation. The proposed virtual inertia control employs a derivative technique to measure the rate of change of frequency slope during inertia emulation. Sensitivity mapping is conducted to scrutinize its impact on dynamic frequency response. Finally, the physical battery storage system of the University of Cuenca microgrid is used as a case study under operating conditions. [ABSTRACT FROM AUTHOR]

The large-scale centralized development of wind and PV power resources is the key to China's dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh. [ABSTRACT FROM AUTHOR]

In low-carbon energy systems, due to the high proportion of renewable energy access, the voltage regulation capacity of the system will decrease. Therefore, in the event of voltage violation, it is easy to cause large-scale renewable energy off-grid and power outages. In order to improve the voltage regulation ability of low-carbon energy system, this paper proposes a two-stage overvoltage suppression strategy for sending-end power grid. Firstly, the principle of overvoltage phenomenon in the sending end power grid of low-carbon energy system with high proportion of renewable energy access is studied, and an overvoltage control strategy composed of two stages of centralized control of rectifier station and flexible resource control of distributed power grid is proposed. Then, the PSO algorithm and consensus algorithm are used to solve the established control model. Finally, a simulation system is established based on actual operating power grid data to verify the proposed control strategy through simulation. The results indicate that the control strategy proposed in this paper can effectively suppress transient overvoltage of AC buses and improve the operational stability of the high proportion of renewable energy sending-end power grid under various operating conditions. In addition, during the daytime overvoltage regulation process, the potential of flexible regulation equipment can be fully utilized. Shortening the duration of voltage exceeding the limit and reducing the peak voltage exceeding the limit can help reduce the renewable energy waste rate of the power grid. [ABSTRACT FROM AUTHOR]

As the climate crisis worsens, power grids are gradually transforming into a more sustainable state through renewable energy sources (RESs), energy storage systems (ESSs), and smart loads. Virtual power plants (VPP) are an emerging concept that can flexibly integrate distributed energy resources (DERs), managing manage the power output of each DER unit, as well as the power consumption of loads, to balance electricity supply and demand in real time. VPPs can participate in energy markets, enable self-scheduling of RESs, facilitate energy trading and sharing, and provide demand-side frequency control ancillary services (D-FCAS) to enhance the stability of the system frequency. As a result, studies considering VPPs have become the focus of recent energy research, with the purpose of reducing the uncertainty resulting from RESs distributed in the power grid and improving technology related to energy management system (EMS). However, comprehensive reviews of VPPs considering their formation, control techniques, and D-FCAS are still lacking in the literature. Therefore, this paper aims to provide a thorough overview of state-of-the-art VPP technologies for building sustainable power grids in the future. The review mainly considers the development of VPPs, the information transmission and control methods among DERs and loads in VPPs, as well as the relevant technologies for providing D-FCAS from VPPs. This review paper describes the significant economic, social, and environmental benefits of VPPs, as well as the technological advancements, challenges, and possible future research directions in VPP research. [ABSTRACT FROM AUTHOR]

In response to the rapid changes in the international energy environment, developing renewable energy (RE)-based distributed generation (DG) and various smart micro-grid systems is crucial for creating a robust electric power grid and new energy industries. In this aspect, there is an urgent need to develop hybrid power systems suitable for coexistent AC and DC power grids, integrated by high-performance wide ban gap (WBG) semiconductor-based power conversion interfaces and advanced operating and control strategies. Due to the intrinsic feature of variation in RE-based power generation, the design and integration of energy storage devices, real-time regulation of power flow, and intelligent energy control schemes are key technologies for further promoting DG systems and micro-grids. This paper investigates an integrated control scheme for multiple GaN-based power converters in a small- to medium-capacity, grid-connected, and RE-based power system. This is the first time that a complete design case demonstrating three GaN-based power converters with different control functions integrated with a single digital signal processor (DSP) chip to achieve a reliable, flexible, cost effective, and multifunctional power interface for renewable power generation systems is presented. The system studied includes a photovoltaic (PV) generation unit, a battery energy storage unit, a grid-connected single-phase inverter, and a power grid. Based on system operation condition and the state of charge (SOC) of the energy storage unit, two typical operating modes and advanced power control functions are developed with a fully digital and coordinated control scheme. Hardware of the GaN-based power converters and digital controllers are designed and implemented. The feasibility and effectiveness of the designed controllers and overall performance of the proposed control scheme are verified with results from simulation and experimental tests on a 1-kVA small-scale hardware system. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RES) as distributed generation units to the power grid has been accelerated by the rising demand for energy and the growing concern over the environmental effects of conventional sources. At the same time, modern electric vehicles (EV) are demonstrating a promising ability to decrease the consumption of fossil fuels. The issues related to a combination of various renewable energy sources to fulfil the load requirements can be solved by the application of multi-input–output architecture that is properly designed. In order to increase the driving range of EVs, several energy sources, including ultracapacitors and fuel cells, should be connected and operate in combination with a battery storage system. To manage these energy sources with various voltage-current characteristics, the same concept can be applied. The current trends in the field of multiport DC-DC converters are identified and examined in this research. This paper proposes a review and analysis of the most significant aspects of multiport converters, such as types based on various characteristics, their topologies, the benefits and drawbacks, and areas of application. A thorough investigation of multiport converters' design guidelines and selection process for specific applications is presented. Based on their properties, multiport converters are categorized in this review. In comparison with other available review publications, this paper is more concise and mainly focused on the most general and important aspects of multiport technologies. Based on the provided information, the reader can discern modern trends and directions of the multiport converter development. [ABSTRACT FROM AUTHOR]

Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as well as the ability to schedule individual loads. There are proposed Smart Plugs presented in the literature capable of such tasks. The problem, however, is that these methods lack the ability to detect if a previously unseen electrical load is connected. Misclassification of such loads presents a problem for load estimation and scheduling. Open-set recognition methods solve this problem by providing a way to detect samples not belonging to any class used during the training of the classifier. This paper evaluates the novel application of open-set recognition methods to the problem of load classification. Two approaches were examined, and both offer promising results. A Support Vector Machine based approach was first evaluated. The second, more robust method used a modified OpenMax-based algorithm to detect unseen loads. [ABSTRACT FROM AUTHOR]