Future Electricity Networks Research Articles

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

Challenges in Integration of RES and Control Techniques in Microgrid: A Review

Microgrids have gained extensive attention over the prior 20 years and are alleged to be a substantial compound of impending power systems. The main objective is to essence the carbon footmark and to enhance the utilisation of Renewable Energy Sources (RES). Integrating distributedenergy resources to create a microgrid will betremendously vital. The development of modern and future electricity networks, like the smart grid, is influenced by MGs because they can provide a variety of benefits to the increasingly complex and growing power system, like better power quality, increased efficiency, enhanced system integration of energy sources that are renewable and clean, and improved network stability and reliability. Microgrid implementation has difficulties controlling, operating, and protecting since integrating RES into the system is more difficult. This paper comprehensively presents the different novelties in integrating RES, control, and optimization.

Modelling Public Intentions to Use Innovative EV Chargers Employing Hybrid Energy Storage Systems: A UK Case Study Based upon the Technology Acceptance Model

The current study investigates public intentions to use an innovative, off-grid renewably powered EV charging technology called FEVER (Future Electric Vehicle Energy networks supporting Renewables). We report the findings of a questionnaire-based survey (QBS) conducted at a zoo in the south of England, exploring the prospect of demonstrating FEVER. The QBS was designed around a context-specific technology acceptance model (TAM) and administered both face-to-face (n = 63) and online (n = 158) from April to May 2023. The results indicate that most participants were willing to pay to use FEVER, particularly where revenue would benefit the zoo. The participants agreed they intended to use the chargers, and that they would be useful and easy to use. The participants agreed that there would be normative pressure to use the chargers, but that their use would be enjoyable. Of greatest concern was that the chargers would be blocked by others. The participants were ambivalent about concerns over charging duration and charge sufficiency. Structural equation modelling confirmed that the context-specific TAM explained 58% of people’s use intentions. The core relationships of the TAM were confirmed, with ‘perceived usefulness’ additionally predicted by subjective norms and ‘perceived ease of use’ additionally predicted by anticipated enjoyment. Of the other variables, only concern that the chargers would be blocked was retained as a marginal predictor of ‘perceived ease of use’. The implications of these findings for the co-design and demonstration of FEVER are discussed.

Flexibility options in a 100% renewable grid for Australia

Flexibility options for operating technologies available in a power system can facilitate the large-scale integration of variable renewable sources such as wind and solar whilst maintaining firm generation. In prior work, contributions of different flexibility options in future net-zero electricity networks vary widely across regions and scenario assumptions. Here we simulate a competitive-bidding process for an energy-only electricity system at 1-hour intervals to assess multiple flexibility options for supply and storage – including biomass, hydropower, concentrating solar power (CSP), electrical energy storage (EES), pumped hydro energy storage (PHES) and hydrogen (H2). We examine systemic effects of competing flexibility options and compare their grid-integrated cost-effectiveness in a future Australian 100% renewable grid. Our simulations indicate a variety of constellations with coexisting technologies, including flexibility options at various penetrations depending on storage cost and design points. We reveal the potentially significant role of long-term storage technologies (with months-deep discharge durations) in Australia's future 100% renewable grid, enabling significant reductions in the installed capacity of wind and solar PV, and thus overall system cost.

Stochastic performances estimate of a universal and flexible power management system for the future European electricity network

This paper presents the final results of the activities carried out by the authors to estimate the availability performances of an innovative modular power conversion architecture, developed within the UNIFLEX-PM (Universal and Flexible Power Management) EU project. The performed study also addresses the analysis of the effects on system stochastic performances deriving from basic components wear out phenomena, and the ones deriving from the adoption of different redundancy strategies and maintenance policies. The UNIFLEX-PM architecture represents an innovative solution for power management in the distribution networks, thus the reliability and availability studies have been focused on obtaining basic figures useful to define the effectiveness of the proposed solution through comparison with a power converter architecture already used for building commercial products and assumed as the reference case.

Overcurrent and Directional Overcurrent Protection for Microgrid

Microgrids will become common in future electrical distribution networks. Numerous operating modes that occur in microgrids make the development of protection schemes and settings challenging. This paper aims to develop a simple protection scheme for the Microgrid IEC Benchmark without any expensive equipment such as communication apparatus. The scheme consists of directional and non-directional overcurrent relays with standard inverse characteristics. For the simulation tool, Digsilent powerfactory is employed. Four operating modes of the microgrid are applied to verify the relays’ coordination. The results show that the proposed scheme successfully protects the microgrid during short circuit faults with acceptable time margins.

Electric Field Analysis of HDPE/NR Biocomposite Due to Moisture Content Condition

It is critical to develop new insulating materials that can improve the performance of next-generation high-voltage cables used in the construction of future electrical networks. The high electric field reduces solid insulation resistance and causes partial discharge through imperfections in a dielectric, causing the dielectric to age and eventually fail. Moisture is one of the most serious factors the effect the high voltage insulation status of High-Density Polyethylene (HDPE) composites. The main goal of this project is to investigate the electric field intensity of HDPE due to moisture content condition when mixed with 10g, 20g, and 30g of various bio-fillers such as coconut coir fibre, pineapple leaves fibre, and oil palm empty fruit bunch. This can be accomplished by using the Finite Element Method Magnetics (FEMM) 4.2 software to create a two-dimensional (2D) axisymmetric electrostatic model. When compared to unfilled HDPE, the inclusion of bio-filler in HDPE increased the maximum electric field intensity due to moisture content condition. The intensity of the electric field varied with the different percentages of biocomposite loading and their permittivity due to moisture content condition. The results showed, due to moisture content condition, the maximum electric field intensity was significantly lower when HDPE was added with a 10% loading of the oil palm empty fruit bunch (EFB). As a result, EFB bio-filler was the best composition because it tends to improve dielectric properties by having a lower maximum electric field intensity which is 4.214Mv/m due to moisture content condition at the top sphere electrode when compared to other compositions.

Integration of hybrid renewable energy sources with the power system considering their economic complementarity

AbstractRenewable energy sources play an important role in providing clean energy for future electricity networks. As the penetration level of these resources grows, their integration with the grid will be more challenging. Each renewable energy source has different inherent characteristics that, if appropriately used in the generation mix, could complement each other and create many technical and economic benefits for the power system. Usually, renewable energy resource complementarity studies are carried out with the objectives of smooth effect, reducing the need for storage and load tracking. In this study, the economic complementarity approach is introduced with the help of a Mixed integer nonlinear programming (MINLP) model. This approach can integrate renewable and storage energy sources with the grid and determine the optimal capacity of these resources in complementary used mode. The results show that the proposed method always imposes the system's lowest annual operation and investment costs.

Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches

The complication of the structure, topology and composition of the future electrical networks is characterized by difficult-to-recognize circuit-mode situations and requires modern methods for analyzing information parameters. The growing trend of digitizing signals in substations and the use of the IEC 61850 standard results in a huge amount of new data available at the nodes of the electrical network. The development and analysis of new methods for detecting and recognizing the modes of electrical networks (normal and emergency) are topical research issues. The article explores a new approach to recognizing a faulted section of an electrical network with branches by concurrently analyzing several information features and applying machine learning methods: decision tree, random forest, and gradient boosting. The application of this approach for decision-making by relay protection has not been previously implemented. Simulation modeling and the Monte Carlo method are at the heart of obtaining training samples. The results of testing the studied methods under review showed the required flexibility, the ability to use a large number of information parameters, as well as the best results of fault recognition in comparison with the distance protection relay.

Electric Field Characteristics of HDPE-NR Biocomposite Under Breakdown Condition

It is critical to develop new insulating materials that can improve the performance of next generation high voltage cables for creating future electrical networks. The high electric field reduces the resistance of solid insulation and produces partial discharge through imperfections in a dielectric, causing the dielectric to age and eventually fail. Thus, this project seeks to analyse the electric field intensity of High Density Polyethylene (HDPE) in breakdown condition when added with 10g, 20g and 30g of different types of bio-filler such as coconut coir fibre, pineapple leaves fibre, and oil palm empty fruit bunch. This can be achieved by creating a two-dimensional (2D) axisymmetric electrostatic model by using the Finite Element Method Magnetics (FEMM) 4.2 software. The results showed that the unfilled HDPE biocomposites have a higher electric field intensity than 10g, 20g, and 30g biocomposite. This indicates that the maximum electric field intensity changes according to the permittivity and voltage of the bio-filler under breakdown conditions. As a result, the maximum electric field intensity was much lower for HDPE added with a 20g of the pineapple leaves fibre. Hence, pineapple leaves fibre was the best composition as it tends to improve the dielectricproperties since it has a lower electric field intensity at the top electrode as compared to other compositions.

Review on the Microgrid Concept, Structures, Components, Communication Systems, and Control Methods

This paper provides a comprehensive overview of the microgrid (MG) concept, including its definitions, challenges, advantages, components, structures, communication systems, and control methods, focusing on low-bandwidth (LB), wireless (WL), and wired control approaches. Generally, an MG is a small-scale power grid comprising local/common loads, energy storage devices, and distributed energy resources (DERs), operating in both islanded and grid-tied modes. MGs are instrumental to current and future electricity network development, such as a smart grid, as they can offer numerous benefits, such as enhanced network stability and reliability, increased efficiency, an increased integration of clean and renewable energies into the system, enhanced power quality, and so forth, to the increasingly growing and complicated power systems. By considering several objectives in both islanded and grid-tied modes, the development of efficient control systems for different kinds of MGs has been investigated in recent years. Among these control methods, LB communication (LBcom)-based control methods have attracted much attention due to their low expenses, recent developments, and high stability. This paper aims to shed some light on different aspects, a literature review, and research gaps of MGs, especially in the field of their control layers, concentrating on LBcom-based control methods.

Recursive Estimation in the Moving Window: Efficient Detection of the Distortions in the Grids with Desired Accuracy

The development of fast convergent and computationally efficient algorithms for monitoring waveform distortions and harmonic emissions will be an important problem in future electrical networks due to the high penetration level of renewable energy systems, smart loads, new types of power electronics, and many others. Estimating the signal quantities in the moving window is the most accurate way of monitoring these distortions. Such estimation is usually associated with significant computational loads, which can be reduced by utilizing the recursion and information matrix properties. Rank two update representation of the information matrix allows the derivation of a new computationally efficient recursive form of the inverse of this matrix and recursive parameter update law. Newton-Schulz and Richardson correction algorithms are introduced in this paper to prevent error propagation and for accuracy maintenance. Extensive comparative analysis is performed on real data for proposed recursive algorithms and the Richardson algorithm with an optimally chosen preconditioner. Recursive algorithms show the best results in estimation with ill-conditioned information matrices.

Simultaneous Frequency and Amplitude Estimation for Grid Quality Monitoring: New Partitioning with Memory Based Newton-Schulz Corrections

High penetration level of renewable energy sources and introduction of controllable loads will result in significant harmonic emissions and sag and swell events in the future electricity networks. Development of the methods for high performance simultaneous estimation of the frequency and amplitude events is required for stable operation of the future electricity networks since zero crossing frequency detection method (which is widely used nowadays in industry) is not accurate enough and does not allow estimation of the amplitude events. The multiple model method which is suitable for simultaneous estimation of the frequency and amplitude is extended in this paper with introduction of a new decomposition technique based on stepwise partitioning, which allows simultaneous construction and accurate and computationally efficient inversion of the information matrix. Recursive calculations of the inverse introduce error accumulation and a new general high order memory based Newton-Schulz iteration is proposed in this paper for correction and reduction of the accumulated error. Moreover, parallel Richardson iterations which are based on partitioning method are proposed in this paper for reduction of the computational complexity. The methods are especially efficient for approximation of the signals with large number of harmonics. The approaches were tested for simultaneous estimation of the frequency and sag and swell signatures in the one-phase synchronized voltage waveform measured at the wall outlet. Simulation results show that the multiple model method provides more accurate frequency estimation in comparison to zero crossing method.In addition, the cascade multiple model method which is based on the multi-windowing technique (where the components of the signals are separated via a proper choice of the window sizes) is introduced in this paper for estimation of the significantly separated frequencies of the electrical signals. The approach was tested in the problem of frequency estimation using the measurement record from the electric vehicle with on-board charger connected to the supply voltage in the laboratory.

Interoperability of modular VSC topologies in Multi-converter multiterminal DC systems

The ongoing transformation of power systems driven by the need for more interconnected networks will eventually lead multiterminal dc (MTDC) grids to become more commonplace in future electricity networks. Addressing interoperability challenges associated with multiple converter topologies for future MTDC implementations is a paramount requirement. This paper introduces a multi-converter MTDC (MC-MTDC) system combining both state-of-the-art and emerging modular voltage source converter (VSC) topologies. The developed MC-MTDC system, (i) demonstrates operation of the modular multilevel converter (MMC) and the alternate arm converter (AAC) in different MTDC configurations, and (ii) sets the basis for further development of MTDC configurations with diverse modular VSC topologies. The developed MC-MTDC system will also facilitate accurate testing and validation of multi-converter interoperability using real-time electromagnetic transient (EMT) models. The design parameters of the AAC-based terminals are determined ensuring compliance with existing MMC-based dc power systems. Detailed analysis and a complete set of results based on real-time implementation for different operating scenarios including steady-state, transient, and contingencies, verify multi-converter interoperability and associated control functions in different MTDC configurations.

Integrated Power System Planning of Multiple Resources with Natural Gas Network and Power Sources

In order to promote all types of energy utilization, interaction between supply and demand as well as efficient operation, a new generation of energy systems which treats the electricity grid as the core came into being. Unlike traditional power system, the electricity network and gas network in the new generation of energy systems are closely linked through gas-fired units, and influence each other. Therefore, to ensure the safe operation of the future electricity network and natural gas network, the integrated framework for generation, electricity network and gas network expansion planning need to be carried out. Under the premise of meeting the electricity network and gas network safe operation constraints, we propose a multi-stage planning model for the combined generation, electricity network and gas network, in which investment cost and operating cost of the combined system are minimized. Combined generation, electricity network and gas network expansion planning is a large-scale, high-dimensional, non-convex and nonlinear optimization problem, which is difficult to solve. To solve this problem, we propose an incremental piecewise linear method to convert the original model into a large-scale mixed-integer linear optimization problem, allowing the use of sophisticated mathematical optimization method to quickly obtain the optimal solution.

Reduction method for planning cross-energy carrier networks in the cellular approach applicable for stability assessment in low-voltage networks

Stability assessment for electrical networks is an essential research topic for sustainable energy generation and, therefore, future electrical networks. Renewable energy sources implemented in the grid in order to substitute fossil fuels as energy sources present a challenge for current network infrastructures since renewable energy sources are highly volatile and, therefore, not always predictable. Especially in low-voltage networks with high shares of PV penetration and extended network branches that supply customers with high consumption, power quality issues arise since these infrastructures were historically built to transport and distribute electrical energy from local substations to the consumers. Due to further developments and new consumer groups connected to the grid as well as generation in PV units, load flows may be reversed or enlarged leading to challenges in terms of overloads and voltage problems. In this work, a method for reducing electrical networks at the low-voltage level by applying the cellular approach is presented with special regard to power quality issues that may arise in low-voltage networks. Network reduction, in general, enables faster calculations of expanded networks with fine temporal resolution and can, therefore, be applied when handling large amounts of grid data. This methodology for network reduction within the cellular approach is implemented into a hybrid load flow calculation framework developed at the Chair of Energy Network Technology. The results obtained from the hybrid load flow calculation can then be used to show how hybrid flexibility options as well as storage units can help increase network stability (in this case regarding voltage stability).

Risk-constrained probabilistic optimal scheduling of FCPP-CHP based energy hub considering demand-side resources

Renewable energy sources (RES) with sharing a large percentage of future energy generation capacities play an essential role in the decarbonization of the future electricity and thermal networks as well as transportation sectors. However, the uncertainties in their outputs make some difficulties in making operational decisions. Hydrogen energy plays a considerable role in this concept. Besides, energy hubs (EHs) provide an efficient and reliable framework for gathering multi-type energy carriers.This paper optimally schedules the operating of the EH and decreases the emission cost, considering the electrical and thermal demand response (DR) program in a probabilistic environment. Besides plug-in electric vehicles (PEVs) and a complete model of hydrogen-based renewable energy sources are presented in the EH. Taking into account uncertainties of electrical/thermal energy markets real-time prices, customers' energy demand, and energy production of RESs into account, various scenarios are generated using the Monte Carlo simulation technique. Next, an efficient method is used to reduce the number of the scenario to make the optimization problem computable and fast. In order to reduce the risk of encountering high operating costs, the conditional value at risk (CVaR) technique is used to manage the associated risk. Simulation results show the efficiency of the proposed method in decreasing the operational cost and managing the risk of encountering unfavorable states.

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer’s level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user’s tricks.

Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation

The integration of energy storage technologies with renewable energy systems can significantly reduce the operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to solve the optimization problem and to determine the reference values for the power that should be drawn by the MG from the main grid using a 15 min sample time. The reference values are then fed to a lower control layer, which uses a 1 min sample time, to determine the settings for the BESS which then ensures that the MG accurately follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented to demonstrate the capability of the proposed EMS.

Dynamic Breakdown Characteristics of Pancake Coil Model for Resistive-Type Superconducting Fault Current Limiters

Toward the reliable and rational insulation design of resistive-type superconducting fault current limiters (RSFCL), we have been investigating not only the intrinsic breakdown (BD) characteristics of liquid nitrogen (LN2) without bubbles but also the dynamic BD characteristics of LN2 under the transient bubble disturbance at the quench of RSFCL coils. We have so far used a fundamental model such as sphere-plane electrode system with quasi-uniform electric field. However, when RSFCL will be introduced in a future electric power network, a stack of pancake coils with non-uniform electric field will be adopted for the practical development of RSFCL. In this paper, we designed and fabricated a pancake coil model for RSFCL, and investigated the intrinsic and dynamic BD characteristics of the pancake coil models in LN2. Experimental results are discussed in terms of the volume effect of intrinsic and dynamic BD strength under non-uniform electric field.