Flexible Grid Research Articles

Data-Driven Day-Ahead Dispatch Method for Grid-Tied Distributed Batteries Considering Conflict Between Service Interests

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies.

Research on optimal configuration of mobile energy storage in distribution networks considering various energy utilization efficiencies

The increasing integration of renewable energy sources such as wind and solar into the distribution grid introduces new complexities and instabilities to traditional electrical grids. This study tackles these challenges by optimizing the configurations of Modular Mobile Battery Energy Storage (MMBES) in urban distribution grids, particularly focusing on capacity-limited areas. Our method investigates five core attributes of energy storage configurations and develops a model capable of adapting to the uncertainties presented by extreme scenarios. This approach not only enhances the adaptability of energy storage systems but also equips decision-makers with proactive and flexible tools for decision-making in complex environments. Empirical evidence from the study shows that modular mobile energy storage significantly improves distribution grid performance by effectively managing the challenges posed by renewable integration. Furthermore, the research confirms that optimizing decision-makers’ cognitive parameters to align with subjective preferences ensures economic viability and enhances grid resilience. This study offers a new perspective and methodology for configuring energy storage, contributing to more flexible and reliable grid operations amidst widespread renewable integration.

Transient Transformations: Memory and Identity Lessons from Kumbh Mela

The Kumbh Mela, held every twelve years at the confluence of the Ganga and Yamuna rivers in Prayagraj, India, is the largest religious gathering and ephemeral city in the world. This article explores the physical and social dimensions of the festival, drawing parallels with other temporary architectures and paying particular attention to the role of memory. The physical structure of the city, organized into a flexible grid, accommodates millions of pilgrims while preserving spaces for the community and its collective identity. The material memory of the site, layered with historical and spiritual significance, intersects with the intangible memory carried by pilgrims through traditions and cultural practices. This blend of tangible and intangible elements fosters a sense of continuity, even in a temporary setting. By examining the Kumbh Mela, the study highlights the importance of memory in creating cohesive, resilient communities within transient urban landscapes, and offers valuable lessons for the design and planning of other temporary settlements.

Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN

Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Fault Localization in Multi-Terminal DC Distribution Networks Based on PSO Algorithm

Flexible DC power grids are widely recognized as an important component of building smart grids. Compared with traditional AC power grids, flexible DC power grids have strong technical advantages in islanding power supplies, distributed power supplies, regional power supplies, and AC system interconnection. In multi-terminal flexible DC power grids containing renewable energy sources such as solar and wind power, due to the instability and intermittency of renewable energy, it is usually necessary to add energy storage units to pre-regulate the power of the multi-terminal flexible DC power grid in islanded operation. Aiming at the important problem of large current impact and serious consequences when the flexible DC distribution network fails, a combined location method combining an improved impedance method (series current-limiting reactors at both ends of the line to obtain a more accurate current differential value) and a particle swarm optimization algorithm is proposed. Initially, by establishing the enhanced impedance model, the differential variables under the conditions of inter-electrode short-circuit and single-pole grounding fault can be obtained. Then tailor-made fitness functions are designed for these two models to optimize parameter identification. Subsequently, the iterative parameters of the particle swarm optimization algorithm are fine-tuned, giving it dynamic sociality and self-learning ability in the iterative process, which significantly improves the convergence speed and successfully avoids local optimization. Finally, various fault types in a six-terminal DC distribution network are simulated and analyzed by MATLAB, and the results show that this method has good accuracy and robustness. This research provides strong theoretical and methodological support for improving the safety and reliability of DC distribution systems.

Machine learning-based uncertainty analysis in power system planning: Insights and pathways for decarbonization

This study performs an extensive uncertainty analysis of decarbonization pathways for the Canadian power system. By leveraging machine learning (ML) techniques, we represent complexities and nonlinear behaviors of the power system, which are challenging to observe using traditional modeling methods due to computational limitations. We further utilize K-means clustering and statistical methods to identify correlations, investigate the effects of uncertainty in key parameters (capital costs, demand, and carbon tax), and identify opportunities for diversification of the power generation mix. Key findings include the increased flexibility of gas combined cycle (generation mean 4.8 TWh, installed capacity mean 4.8 GW) over gas simple cycle (generation mean 22.3 GWh, 0 installed capacity) in response to carbon tax, highlighting its role in a low-carbon future. Variations in solar and wind deployment rates, influenced by capacity factors and location-specific costs, underscore the necessity of a flexible grid system. This analysis reveals important correlations between renewable deployment, and transmission expansion and gas combined cycle reliance. The results also highlight minimal sensitivity of hydroelectric power generation (∼ 43 % of scenarios) to changes in input parameters. The novelty of our work lies in proposing a comprehensive approach which uses ML techniques to go beyond the capabilities of existing models, specifically by performing extensive uncertainty analysis and provides key insights into system behavior, causative factors, and an expanded breadth of potential pathways. Taken together, this proposed approach lays a foundation for strategic planning and policy formulation.

An Adaptive Reclosing Scheme for Flexible DC Ring Grid Based on Residual Voltage Waveform Offset Detection

An Adaptive Reclosing Scheme for Flexible DC Ring Grid Based on Residual Voltage Waveform Offset Detection

Optimal operation of flexible interconnected distribution grids based on improved virtual synchronous control techniques

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.

Role of smart charging of electric vehicles and vehicle-to-grid in integrated renewables-based energy systems on country level

The energy transition based on variable renewable energy sources raises concerns on satisfying the electricity demand at all hours of a year, which is amplified by the ongoing energy system electrification. Balancing variable electricity supply and inelastic demand requires additional sources of flexibility in the system: flexible electricity generation, energy storage, grids, flexible demand, and overall sector coupling. Electrification of transport increases the electricity demand; however, it offers the opportunity to use additional low-cost flexibility from electric vehicles batteries. The LUT Energy System Transition Model was modified to model the smart charging and vehicle-to-grid functionality of electric vehicles in integrated energy systems. The results show that, in countries with a large fleet of electric vehicles, smart charging and vehicle-to-grid allow for a substantial reduction of energy storage requirements, reducing the electricity and heat storage capacity by 35% and 25%, respectively and leading to 4% lower system cost. Flexible operation of electrolysers required for e-fuels synthesis have a similar effect and, in combination with smart charging and vehicle-to-grid, can lead to 8% lower system cost. The impact of vehicle-to-grid naturally depends on assigned operational cost; however, it contributes to system cost reduction even at high costs of 50 or 100 €/MWh.

Fault detection method for flexible DC grid based on CEEMDAN multiscale entropy and GA-SVM

Fault detection method for flexible DC grid based on CEEMDAN multiscale entropy and GA-SVM

Flexible Grid Graphene Electrothermal Films for Real-Time Monitoring Applications.

Flexible electrothermal films are crucial for protecting equipment and systems in cold weather, such as ice blockages in natural gas pipelines and icing on aircraft wings. Therefore, a flexible electric heater is one of the essential devices in industrial operations. One of the main challenges is to develop flexible electrothermal films with low operating voltage, high steady-state temperature, and good mechanical stability. In this study, a flexible electrothermal film based on graphene-patterned structures was manufactured by combining the laser induction method and the transfer printing process. The grid structure design provides accurate real-time monitoring for the application of electrothermal films and shows potential in solving problems related to deicing and clearing ice blockages in pipelines. The flexible electrothermal film can reach a high heating temperature of 165 °C at 15 V and exhibits sufficient heating stability. By employing a simple and efficient method to create a flexible, high-performance electrothermal film, we provide a reliable solution for deicing and monitoring applications.

Cluster and Representative Models for Generation Units of Flexible Grids with Small Modular Reactors

The analysis of existing mathematical models for power systems with large shares of electricity production by NPP and renewable energy sources generation was carried out. The low level of adequacy is shown for power system models used in the problems of long-term development planning for generating plants with nuclear fuel and renewable energy sources. In order to plan the development of power systems at the current stage of technological changes in the electric power industry, new models of load modes for NPP units and generating equipment with renewable energy sources are proposed.
 Dispatching models with a cyclic forecast period were used to ensure the adequacy unit commitment models for NPP units and energy storage systems. Representative models of solar and wind power plants are presented, which, together with cluster models of NPP units and energy storage systems, solve the issue on the adequacy of power system modeling in current conditions of their development. The results of computational experiments with the proposed models for generating equipment of the flexible grid are presented. The adequacy of cluster mathematical models of NPP units and energy storage systems, as well as representative models of solar and wind power plants generating equipment is experimentally confirmed.

Exploring better image captioning with grid features

Nowadays, Artificial Intelligence Generated Content (AIGC) has shown promising prospects in both computer vision and natural language processing communities. Meanwhile, as an essential aspect of AIGC, image to captions has received much more attention. Recent vision-language research is developing from the bulky region visual representations based on object detectors toward more convenient and flexible grid ones. However, this kind of research typically concentrates on image understanding tasks like image classification, with less attention paid to content generation tasks. In this paper, we explore how to capitalize on the expressive features embedded in the grid visual representations for better image captioning. To this end, we present a Transformer-based image captioning model, dubbed FeiM, with two straightforward yet effective designs. We first design the feature queries that consist of a limited set of learnable vectors, which act as the local signals to capture specific visual information from global grid features. Then, taking augmented global grid features and the local feature queries as inputs, we develop a feature interaction module to query relevant visual concepts from grid features, and to enable interaction between the local signal and overall context. Finally, the refined grid visual representations and the linguistic features pass through a Transformer architecture for multi-modal fusion. With the two novel and simple designs, FeiM can fully leverage meaningful visual knowledge to improve image captioning performance. Extensive experiments are performed on the competitive MSCOCO benchmark to confirm the effectiveness of the proposed approach, and the results show that FeiM yields more eminent results than existing advanced captioning models.

DC Bus Differential Protection Based on Fault Component Current in Flexible DC Grid

DC Bus Differential Protection Based on Fault Component Current in Flexible DC Grid

Auxiliary Branch for Hybrid DC Circuit Breaker in Flexible DC Grid to Reduce Fault Breaking Time and Arrester Capacity Demand

Auxiliary Branch for Hybrid DC Circuit Breaker in Flexible DC Grid to Reduce Fault Breaking Time and Arrester Capacity Demand

Stability Modeling and Parameter Analysis for Flexible DC Power Grids Under Asymmetric Conditions

Stability Modeling and Parameter Analysis for Flexible DC Power Grids Under Asymmetric Conditions

A Survey of Wireless Charging Methods and Optimization Techniques of Electric Vehicles

Since the 1990s, electric vehicles have been extensively utilized, with a focus on extending the life of storage systems, lowering costs, and providing flexible grid connectivity—a feature that is currently being studied. The substitution of alternative energy sources for conventional fuels has resulted in significant advancements in energy conservation and greenhouse gas emission reduction. Electric vehicle power sources are discovered in power plants. Traditionally, the power grid supplies the power plant with its electricity. A power plant that uses a hybrid solar-wind system can save more energy and reduce greenhouse gas emissions more significantly. An overview of the latest wireless charging methods and optimization techniques is given in this article. Firstly, the essential characteristics of an electric car (EV), wireless charging techniques, and the type of charging system that is dependent on the location of the vehicle charging have been provided in detailed descriptions. After that, a few of the most common optimization techniques to determine the location and size of EV charging stations are presented. With a focus on its explicit application in electric vehicles, the paper provided researchers and scholars with an in-depth knowledge of the fundamentals of WPT and its mechanism of operation.

Study on the Equivalent Conversion and Utilisation Model of Solar Energy and its Benefits

Relying on the energy local area network (LAN), we study the equivalent conversion relationship between different “energy qualities” in the process of solar energy conversion and utilisation and its benefit model. Considering the impact of load demand characteristics and current energy consumption price factors on solar energy utilization, a multi-objective optimization model of solar energy conversion and utilization is constructed with the goals of maximizing solar energy utilization and maximizing the comprehensive benefits of system operation; then, the particle swarm optimization algorithm with quantum behaviour is used to optimize and solve the model, and a comparative analysis is carried out with the traditional single-energy supply mode. The results show that the proposed model can effectively improve the comprehensive utilisation and consumption of solar energy by the energy consumption system, and achieve higher energy sales benefits. Therefore, with the help of energy storage technology, the fluctuation and intermittency of the solar PV system output can be suppressed, and flexible grid connection can be realised, so as to enhance the grid-connected capacity of solar energy; at the same time, higher economic benefits can be obtained with the help of optimisation, coordination and control system operation. Optimised control of solar PV power generation systems under different scenarios ensures the safety and stability of the system’s power output and improves the grid-connected capability of solar PV power generation systems. The validity and feasibility of the proposed model and its operation mode are verified, providing a new idea for the development and utilisation of large-scale solar energy.

Performance of sorting grids on catching elasmobranchs in a multispecies Mediterranean bottom trawl fishery

Elasmobranchs are among the most threatened long-lived marine species in the Mediterranean Sea. Incidental catch and intense fishing effort are considered the major threats to their conservation. To date, no Mediterranean countries has adopted specific technical measures within current conservation plans to mitigate potential impacts on vulnerable elasmobranchs. This study aimed to evaluate the effectiveness of new elasmobranch bycatch reducing devices (BRD) in a Mediterranean multispecies bottom trawl fishery (northern central Adriatic Sea, one of the most exploited basins in the Mediterranean). Specifically, two types of grids were tested: a novel shark excluder device (SED) and a flexible grid (Flexgrid). Effectiveness was evaluated in terms of bycatch and commercial catch reduction, relying on the different sizes of elasmobranchs and the main commercial species. Results showed that preliminary tests with SED were effective in excluding elasmobranch species. Catches made with this grid showed no significant difference for targeted crustacean (mantis shrimp, Squilla mantis), however, commercial losses were shown for some large bony fish. Flexgrid did not affect the catches of the main target species, such as the red mullet (Mullus barbatus), European hake (Merluccius merluccius) and mantis shrimp, but also retained small-spotted catshark (Scyliorhinus canicula). The effectiveness of the grids apparently was affected by the small size of the elasmobranchs captured. The study showed that SED and Flexgrid are valuable solutions for elasmobranchs if combined with other conservation measures (e.g. seasonal fishing closure in critical areas and good practices). Finally, strong cooperation between fishers, scientists, and managers is critical to identify the best management options in a regulatory framework where only a few elasmobranch species are formally protected. Interestingly, the study also yielded the identification of two potential aggregation-nursery areas for the small-spotted catshark (Scyliorhinus canicula) and the common smooth-hound (Mustelus mustelus).