Search Results (12)

With the intensification of climate change, deepening energy transition, and increasing social vulnerability, extreme power outage events pose escalating challenges to the governance capacity of modern power systems. Existing evaluation frameworks primarily focus on engineering reliability and economic loss estimation, lacking systematic quantification of the governance complexity arising from multidimensional interacting pressures behind outage events. This creates a blind spot in both theoretical research and governance practice, hindering differentiated resilience decision-making. To address this gap, this study develops a four-dimensional evaluation framework of power outage governance complexity encompassing event attributes, external environment, internal system, and social impacts. Based on county-level outage data and multi-source auxiliary data in the United States from 2015 to 2024 and employing the XGBoost–SHAP interpretable machine learning approach, we construct the Power Outage Complexity Index (POCI) for all U.S. counties and systematically analyze its spatiotemporal evolution and core driving factors. The results show that outage governance complexity in the U.S. power grid exhibits a significant upward trend during 2015–2024, with an average annual growth rate of 1.84%. Spatially, significant positive autocorrelation is observed, and 146 high-complexity hotspot counties are identified, mainly clustered along the East and West Coasts, the Gulf Coast, and the Southwest. Driver analysis reveals that social impact and event attribute dimensions together account for nearly 90% of the variance in complexity, with cumulative outage exposure burden, outage frequency, and large-scale event ratio being the most critical drivers. Theoretically, this study extends power resilience research from an engineering-physical paradigm to a socio-technical governance paradigm and provides a reproducible methodological framework for assessing governance complexity in critical infrastructure systems. Practically, the POCI can serve as a governance diagnostic tool for the power industry and regulators, supporting resilience investment prioritization, emergency resource optimization, and differentiated governance strategy formulation. It also provides empirical evidence for safeguarding energy security in highly vulnerable communities and promoting energy resilience equity. Full article

Amid China’s pursuit of the “dual carbon” goals, the development and large-scale integration of renewable energy have become a core pillar of the power system transition. However, the intermittency and uncontrollability of wind and photovoltaic (PV) power have intensified peak-regulation conflicts after large-scale grid integration. Traditional coal-fired units lack sufficient flexibility to accommodate renewable energy fluctuations, while their willingness to participate in deep peak shaving remains low due to high associated costs. Addressing these challenges requires both enhanced system-level peak-regulation flexibility and effective market incentives for thermal units. Motivated by the limitations of existing studies that often consider individual flexibility resources or deterministic market mechanisms in isolation, this study investigates a coordinated multi-resource peak-regulation framework combined with an optimized market-clearing mechanism for deep peak-shaving ancillary services. First, flexibility resources are classified, and the peak-regulation mechanisms of source–load–storage coordination and auxiliary service markets are analyzed. Second, a wind–PV–thermal–storage operation cost model is established, followed by a two-layer peak-regulation market-clearing model that explicitly accounts for wind–PV uncertainty. The upper-level model minimizes total system operating costs through the coordinated dispatch of demand response and energy storage, while the lower-level model minimizes power purchase costs under a unified marginal clearing price. In addition, an uncertainty modeling framework based on Information Gap Decision Theory (IGDT) is introduced to manage renewable generation uncertainty and support decision-making under different risk preferences. Case studies are conducted to verify the effectiveness of the proposed framework. The results show that: (1) synergistic peak shaving through energy storage and demand response reduces the system peak–valley difference from 460 MW to 387.87 MW and decreases wind–PV curtailment costs from 355,000 yuan to 15,700 yuan, thereby alleviating thermal unit pressure and improving renewable energy accommodation; (2) the unified marginal clearing price mechanism reduces total system operating costs by 41.07% and significantly lowers the frequency of deep peak shaving for thermal units, enhancing their participation willingness; and (3) the IGDT-based model effectively addresses wind–PV uncertainty by providing optimistic and pessimistic scheduling strategies under different deviation coefficients. These results confirm that the proposed framework offers an effective and flexible solution for coordinated peak shaving in power systems with high renewable energy penetration. Full article

The large-scale adoption of renewable energy sources, while environmentally beneficial, introduces significant frequency fluctuations due to the inherent variability of wind and solar output. Electric vehicle (EV) integration with substantial battery storage and bidirectional charging capabilities offers potential mitigation for these fluctuations. This study addresses load frequency regulation in multi-area interconnected power systems incorporating diverse generation resources: renewables (solar/wind), conventional plants (thermal/gas/hydro), and EV units. A hybrid controller combining the proportional–integral–derivative with second derivative (PIDD²) and tilted derivative (TD) structures is proposed, with parameters tuned using an innovative optimization method called the Tianji’s Horse Racing Optimization (THRO) technique. The THRO-optimized PIDD²-TD controller is evaluated under realistic conditions including system nonlinearities (generation rate constraints and governor deadband). Performance is benchmarked against various combination structures discussed in earlier research, such as PID-TID and PIDD²-PD. THRO’s superiority in optimization has also been proven against several recently published optimization approaches, such as the Dhole Optimization Algorithm (DOA) and Water Uptake and Transport in Plants (WUTPs). The simulation results show that the proposed controller delivers markedly better dynamic performance across load disturbances, system uncertainties, operational constraints, and high-renewable-penetration scenarios. The THRO-based PIDD²-TD controller achieves optimal overshoot, undershoot, and settling time metrics, reducing overshoot by 76%, undershoot by 34%, and settling time by 26% relative to other controllers, highlighting its robustness and effectiveness for modern hybrid grids. Full article

With the continuous increase in renewable energy penetration, traditional frequency regulation strategies in power grids struggle to maintain frequency stability under high renewable-share conditions. To address the shortcomings of the current deadband settings in regional grid frequency regulation, this paper proposes an optimized deadband-configuration scheme for renewable energy power plants and evaluates its effectiveness in enhancing the frequency regulation potential of renewable units. By developing frequency response models for thermal power, wind power, photovoltaic generation, and energy storage, the impact of different deadband widths on dynamic frequency response and steady-state deviation is analyzed. Three representative output scenarios for renewable units are constructed, and under each scenario the coordinated control performance of the proposed and the existing deadband configurations is compared. Simulation studies are then conducted based on a typical high renewable penetration scenario. The results show that, compared with the existing regional-grid deadband settings, the proposed configuration more fully exploits the regulation potential of renewable units, improves overall frequency-response capability, significantly reduces frequency deviations, and shortens recovery time. This research provides both theoretical foundations and practical guidance for frequency-support provision by renewable energy power plants under high penetration conditions. Full article

Virtual inertia is a measure of the capability of distributed sources and loads within power supply units to resist system frequency variations through additional control strategies applied to converters. The reasonable allocation of virtual inertia is beneficial for enhancing system stability. In response to the insufficient consideration of multi-regional coordination and difficulties in balancing frequency change rates in existing virtual inertia allocation methods, this paper proposes a neural network-based coordinated virtual inertia allocation method for multiple regions. First, a data-driven model is constructed based on the RBFNN neural networks to map the feasible region boundaries of virtual inertia for distributed resources under different disturbance scenarios. Second, a multi-area virtual inertia optimization allocation model is established, aiming to minimize both the inter-area frequency change rates and the differences between them, while considering the regulation capabilities of grid-forming PV systems and ESS. Following this, a genetic algorithm-based solving strategy is designed to achieve the global optimal allocation of virtual inertia. Finally, simulations verify the effectiveness of the coordinated allocation strategy in enhancing frequency stability across multiple autonomous regions. This optimization method reduces the frequency variation rate in both regions and maintains relative stability between the regions, thereby enhancing the system’s disturbance rejection capability. The results showed that after optimizing the virtual inertia allocation using the method proposed in this paper, the frequencies of the two regions increased by 0.11 Hz and 0.14 Hz, respectively, and the dynamic rate of frequency change decreased by 50.2% and 52.1%. Therefore, this study provides a foundational method and a feasible approach to multi-area virtual inertia optimization allocation in the new distribution system, contributing to frequency support via virtual inertia in distribution network optimization operation. Full article

With the increasing prevalence of renewable energy, microgrids play a crucial role in enhancing distributed energy efficiency and system flexibility. However, the intermittent and unpredictable nature of renewable energy generation presents significant challenges for microgrid restoration and stable operation. Black-start technology, a key method for autonomous power restoration, is essential for ensuring reliable microgrid operation. Grid-forming virtual synchronous generators (VSGs), with inherent inertia support and regulation capabilities, autonomously establish the voltage, meeting the power supply demands of black-start processes. However, during the pre-synchronization of multiple distributed energy resources in black-start scenarios, rapid phase-angle adjustments can cause frequency fluctuations due to the coupling between the frequency and phase angle. This coupling often leads to frequency overshoot and decreased system stability. To address this challenge, this paper proposes an enhanced parallel restoration strategy for a multi-source black start. Optimizing phase-angle control reduces the dependency on phase-locked loops (PLLs), mitigates phase-angle difference jumps, and accelerates the pre-synchronization process. Furthermore, a linear active disturbance rejection controller (LADRC) dynamically compensates for frequency fluctuations, effectively decoupling the frequency from the phase angle. This approach improves synchronization accuracy and enhances parallel reliability among multiple distributed energy resources (DERs). Simulation results show that the proposed method suppresses frequency overshoot and system disturbances during a multi-source black start, significantly enhancing microgrid restoration capability and operational stability. Full article

Grasshoppers can significantly disrupt agricultural and livestock management because they reproduce and develop quickly in friendly environments. Xilingol League is the region most severely affected by grasshopper infestations. The region’s extensive grasslands are considered valuable, a critical component of the local ecosystem, a vital resource for the region’s key economic activity of livestock farming, and crucial for supporting diverse flora and fauna, carbon sequestration, and climate regulation. Oedaleus decorus asiaticus (O. d. asiaticus) is highly harmful in Xilingol League in the Inner Mongolia Autonomous Region of China. Therefore, early warning is crucial for projecting O. d. asiaticus’s regional spread and detecting the impacts of critical environmental elements. We systematically identified 26 major contributing elements by examining four categories of environmental factors—meteorology, vegetation, soil, and topography—encompassing the three growth phases of grasshoppers. Furthermore, the MaxEnt and frequency ratio (FR) approaches, coupled with multisource remote sensing data, were used to predict a potentially appropriate distribution (habitat suitability) of O. d. asiaticus in Xilingol League. The research found nine key habitat factors influencing O. d. asiaticus distribution: the mean specific humidity during the adult stage (ASH), vegetation type (VT), above-ground biomass during the nymph stage (NAB), soil sand content (SSAND), mean precipitation during the egg stage (EP), mean precipitation during the nymph stage (NP), soil bulk density (SBD), elevation, and soil type (ST). Additionally, our analysis revealed that the most suitable and moderately suitable habitats for O. d. asiaticus are predominantly located in the southern and eastern parts of Xilingol League, with significant concentrations in West Ujumqin, East Ujumqin, Xilinhot, Zhenglan, Zheng Xiangbai, Duolun, and Taipusi. Based on the suitable habitat results, policymakers may make judgments about future management actions to preserve the ecological security of grasslands and their sustainable growth. This study indicates that the Maxent approach exhibited superior accuracy (receiver operating characteristic) compared to the FR approach for assessing the habitat suitability for O. d. asiaticus in Xilingol League. Full article

Nowadays, renewable energy (RE) sources are heavily integrated into the power system due to the deregulation of the energy market along with environmental and economic benefits. The intermittent nature of RE and the stochastic behavior of loads create frequency aberrations in interconnected hybrid power systems (HPS). This paper attempts to develop an optimization technique to tune the controller optimally to regulate frequency. A hybrid Sparrow Search Algorithm-Grey Wolf Optimizer (SSAGWO) is proposed to optimize the gain values of the proportional integral derivative controller. The proposed algorithm helps to improve the original algorithms’ exploration and exploitation. The optimization technique is coded in MATLAB and applied for frequency regulation of a two-area HPS developed in Simulink. The efficacy of the proffered hybrid SSAGWO is first assessed on standard benchmark functions and then applied to the frequency control of the HPS model. The results obtained from the multi-area multi-source HPS demonstrate that the proposed hybrid SSAGWO optimized PID controller performs significantly by 53%, 60%, 20%, and 70% in terms of settling time, peak undershoot, control effort, and steady-state error values, respectively, than other state-of-the-art algorithms presented in the literature. The robustness of the proffered method is also evaluated under the random varying load, variation of HPS system parameters, and weather intermittency of RE resources in real-time conditions. Furthermore, the controller’s efficacy was also demonstrated by performing a sensitivity analysis of the proposed system with variations of 75% and 125% in the inertia constant and system loading, respectively, from the nominal values. The results show that the proposed technique damped out the transient oscillations with minimum settling time. Moreover, the stability of the system is analyzed in the frequency domain using Bode analysis. Full article

In the past few decades, the water resources in the Tang–Bai River Basin showed a declining trend, due to the human–driven alteration of surface water and groundwater management. There are potential risks to the sustainable utilization of future water resources in response to agricultural, industrial, and domestic water supply. In this work, we used the water allocation and regulation model based on SWAT (SWAT–WARM model) to quantify the characteristics of water resources response under human activities in this basin. The multi–source water supply module was modified to improve the applicability of the SWAT–WARM model in this basin. We validated our simulations against observed runoff, water consumption, and supply. The main results were as follows: (a) We used the percent bias, the correlation coefficient, and the Nash–Sutcliffe efficiency coefficient to measure the model validity and found that the modified model did not show obvious advantages in runoff simulations, whereas it reproduced water consumption and supply better than the original model. The modified model had more advantages in reflecting the process of water resources transformation and utilization in the basin driven by strong human activities. (b) By comparing the variation of watershed water circulation fluxes under natural and human disturbance conditions in the Tang–Bai River Basin from 1995 to 2016, we found that human activities increased evapotranspiration by 6.8% and surface runoff increased by 10.0%, while groundwater resources decreased by 0.23 million m3/yr. (c) There was water shortage in the basin at different flow frequencies, among which agricultural water shortage accounted for the largest proportion, >70%. The basin should further strengthen agricultural and industrial water saving, reduce water consumption fundamentally, and ensure the sustainable development of economy and society. Full article

The continuous stability of hybrid microgrids (MGs) has been recently proposed as a critical topic, due to the ever-increasing growth of renewable energy sources (RESs) in low-inertia power systems. However, the stochastic and intermittent nature of RESs poses serious challenges for the stability and frequency regulation of MGs. In this regard, frequency control ancillary services (FCAS) can be introduced to alleviate the transient effects during substantial variations in the operating point and the separation from main power grids. In this paper, an efficient scheme is introduced to create a coordination among distributed energy resources (DERs), including combined heat and power, diesel engine generator, wind turbine generators, and photovoltaic panels. In this scheme, the frequency regulation signal is assigned to DERs based on several distribution coefficients, which are calculated through conducting a multi-objective optimization problem in the MATLAB environment. A meta-heuristic approach, known as the artificial bee colony algorithm, is deployed to determine optimal solutions. To prove the efficiency of the proposed scheme, the design is implemented on a hybrid MG. Various operational conditions which render the system prone to experience frequency fluctuation, including switching operation, load disturbance, and reduction in the total inertia of hybrid microgrids, are studied in PSCAD software. Simulation results demonstrate that this optimal control scheme can yield a more satisfactory performance in the presence of grid-following and grid-forming resources during different operational conditions. Full article

Energy storage systems can improve the uncertainty and variability related to renewable energy sources such as wind and solar create in power systems. Aside from applications such as frequency regulation, time-based arbitrage, or the provision of the reserve, where the placement of storage devices is not particularly significant, distributed storage could also be used to improve congestions in the distribution networks. In such cases, the optimal placement of this distributed storage is vital for making a cost-effective investment. Furthermore, the now reached massive spread of distributed renewable energy resources in distribution systems, intrinsically uncertain and non-programmable, together with the new trends in the electric demand, often unpredictable, require a paradigm change in grid planning for properly lead with the uncertainty sources and the distribution system operators (DSO) should learn to support such change. This paper considers the DSO perspective by proposing a methodology for energy storage placement in the distribution networks in which robust optimization accommodates system uncertainty. The proposed method calls for the use of a multi-period convex AC-optimal power flow (AC-OPF), ensuring a reliable planning solution. Wind, photovoltaic (PV), and load uncertainties are modeled as symmetric and bounded variables with the flexibility to modulate the robustness of the model. A case study based on real distribution network information allows the illustration and discussion of the properties of the model. An important observation is that the method enables the system operator to integrate energy storage devices by fine-tuning the level of robustness it willing to consider, and that is incremental with the level of protection. However, the algorithm grows more complex as the system robustness increases and, thus, it requires higher computational effort. Full article

A significant integration of energy storage systems is taking place to offer flexibility to electrical networks and to mitigate side effects of a high penetration of distributed energy resources. To accommodate this, new processes are needed for the design, implementation, and proof-of-concept of emerging storage systems services, such as voltage and frequency regulation, and reduction of energy costs, among others. Nowadays, modern approaches are getting popular to support engineers during the design and development process of such multi-functional energy storage systems. Nevertheless, these approaches still lack flexibility needed to accommodate changing practices and requirements from control engineers and along the development process. With that in mind, this paper shows how a modern development approach for rapid prototyping of multi-functional battery energy storage system applications can be extended to provide this needed flexibility. For this, an expert user is introduced, which has the sole purpose of adapting the existing engineering approach to fulfill any new requirements from the control engineers. To achieve this, the expert user combines concepts from model-driven engineering and ontologies to reach an adaptable engineering support framework. As a result, new engineering requirements, such as new information sources and target platforms, can be automatically included into the engineering approach by the expert user, providing the control engineer with further support during the development process. The usefulness of the proposed solution is shown with a selected use case related to the implementation of an application for a battery energy storage system. It demonstrates how the expert user can fully adapt an existing engineering approach to the control engineer’s needs and thus increase the effectiveness of the whole engineering process. Full article