Sustainable Energy Grids & Networks最新文献

The increased uptake of distributed renewable energy in port areas is facilitating the electrification and net zero transition of marine ports. Effective operation that considers unique characteristics of the port is critical to minimize the operating cost in the port microgrid (PMG). In this paper, we propose a joint scheduling method that considers the impact of tidal patterns on the period and intensity of port operations. The method takes advantage of the strong correlations between renewable energy (solar, wind and tidal) and multi-class load to support the PMG operator in determining the most cost-effective scheduling of energy supply and flexible loads during port activities. Additionally, the traditional centralized operation is vulnerable to local failures, and distributed operation for hundreds of energy units will result in significant computational burden, neither of which is suitable for the PMG operation. Our work decouples the PMG system based on the port functions and thus decomposes the PMG operation into a few subproblems. Then, we hierarchically solve the primal and dual problems by a distributed algorithm. Simulation results illustrate the benefits of tidal energy in the renewable generation mix. Furthermore, the proposed method achieves cost reductions of 12.4% and 21.7% under two different tidal patterns.

With the deployment of renewable energy, the load curve is expected to follow the renewable energy output curve to minimize the fluctuation of thermal power output in the source-load coordinated dispatching. The traditional indicators for the load curve are no longer enough to describe the load characteristics. A new load indicator called the source-load similarity distance is proposed by improving the similarity measurement method of the time series and calculating the similarity distance between the renewable energy output curve and the load curve. By combining the Euclidean distance with the improved dynamic time warping, the source-load similarity distance is obtained and the data distribution and morphological fluctuation characteristics can be simultaneously considered. The source-load coordinated dispatching model is also established to minimize the source-load similarity distance. The simulation results show that the source-load similarity distance can effectively describe the similarity characteristics of the renewable energy output curve and the load curve. Increasing the source-load similarity distance can reduce the thermal power operation cost by 56.2 % and the cost of demand response by 25.3 %, and increase the utilization rate of wind power by 4.6 % compared to the dispatching model with the standard deviation indicator.

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

This paper presents an exploration of the application of control theory, particularly utilizing a gradient-based algorithm, to automate and optimize the operation of photovoltaic panels and refrigeration systems in warehouse environments. The study emphasizes achieving coordination between energy generation and consumption, specifically harnessing surplus solar energy for efficient refrigeration. The complex interplay between fluctuating solar irradiance, thermal dynamics of the warehouse, and refrigeration needs underscores the significance of control theory in designing algorithms to dynamically adjust PV panel output and refrigeration system operation. The paper discusses foundational control theory principles, proposes a tailored framework for warehouse operations, and highlights the potential for sustainable energy practices. This paper explores the use of data-driven approaches based on NeuralODEs vs classical ones using physics equations.

The decarbonization of the energy sector represents a challenge that requires new tools and approaches of analysis. This paper aims to demonstrate the fundamental role that geographical distributed real-time co-simulations (GD-RTDS) can play in this regard. To this end, three different case studies have been analyzed with GD-RTDS, covering a wide range of applications for the energy sector decarbonization: (a) implementation of Renewable Energy Communities for supporting the share increase of Renewable Energy Sources, (b) the integration and management of Onshore Power Supply, and (c) the integration of a forecasting tool for the management of the Electric Vehicle charging. The performed experiments included fully simulated components, together with (power) hardware-in-the-loop and software-in-the-loop elements. These components have been simulated in different laboratory facilities in Italy and Germany, all operating in a synchronized manner under the presented geographically-distributed setup. The results show that the proposed architecture is flexible enough to be used for modeling all the different case studies; moreover, they highlight the significant contribution that the GD-RTDS methodology can give in informing and driving energy transition policies and the fundamental role of power systems to spearhead the complete decarbonization of the energy sector.

Renewable generation utilizes inverter-based technology which is much different than the coal and nuclear synchronous machines it is replacing. The electrical network was designed around big synchronous machines providing constant dispatchable power and innate inertia to dampen frequency disturbances. The network protection system is based on high available fault current provided by the big generators. The renewable plants have a variable fuel supply, no inertia, and provide less fault current for system protection. A hybrid power plant with renewables, energy storage, and a synchronous generator can play a significant role in restoring power system operation after the occurrence of a blackout. This paper presents an improved method to utilize inverter-based resources (IBR) with existing synchronous generation to improve the black start capability while minimizing the overall system’s operation cost and providing additional ancillary grid services. A battery energy storage system is modeled with grid forming inverters to provide black start to the synchronous unit while the solar is modeled with grid following inverters. A Long-Short Term Memory (LSTM) is developed to model the auxiliary load for reducing the fuel consumption in synchronous generators and reducing the cost. Several case studies are conducted to verify the performance of the grid forming inverters with battery storage to start the largest direct online (DOL) and soft start motors. Utilizing actual synchronous generator auxiliary load data for a year, a quasi-dynamic simulation analysis is performed to determine energy storage requirements for black start. Finally, the energy benefits of the solar installation are estimated from simulating the hybrid system for 1 year. A reduced fuel burn simulation is performed by constraining the export power to the actual data and reducing synchronous generation to account for the solar generation and the reduced auxiliary load. The study finds that the IBR resources are capable of successfully black starting the synchronous generator and reducing fuel consumption and earning additional revenue from the solar plants.

Electric power systems during transient states are extensively investigated using variations of the Kuramoto model to analyze their dynamic behavior. However, the majority of current models fail to capture the physics of power flows and the heterogeneity of the grids under study. This study addresses this gap by comparing the levels of heterogeneity in continent-sized power grids in Europe and North America to reveal the underlying universality and heterogeneity of grid frequencies, electrical parameters, and topological structures. Empirical data analysis of grid frequencies from the Hungarian grid indicates that q-Gaussian distributions best fit simulations, with spatio-temporally correlated noise evident in the frequency spectrum. Comparing European and North American power grids reveals that employing homogeneous transmission capacities to represent power lines can lead to misleading results on stability, and nodal behavior is heterogeneous. Community structures of the continent-sized grids are detected, demonstrating that Chimera states are more likely to occur when studying only subsystems. A topographical analysis of the grids is presented to assist in selecting such subsystems. Finally, synchronization calculations are provided to illustrate the occurrence of Chimera states. The findings underscore the necessity of heterogeneous grid models for dynamic stability analysis of power systems.

The proliferation of distributed energy resources and the introduction of new loads in distribution networks present significant challenges for monitoring and operation. To satisfy the enhanced observability and controllability requirements of modern distribution networks, there is an increasing demand for advanced monitoring devices. Distribution Network Phasor Measurement Units (DPMUs) offer high-precision measurement data with precise timestamps, thereby improving both the accuracy and redundancy of measurements within the distribution network.This paper introduces an optimization model for the strategic placement of PMUs within distribution networks, leveraging node metric indices. The indices considered are node degree, spatiotemporal correlation, and node power ratio. The relative importance of these indices is determined using an improved entropy weight method, which quantifies the differentiation of nodes within the network. This method facilitates the prioritized placement of DPMUs at critical nodes. The proposed model also incorporates constraints such as the depth of unobservability and zero injection nodes. Utilizing a 0–1 integer programming algorithm, the model derives a multi-stage optimal placement scheme for PMU placement. This scheme evolves from incomplete observability to critical observability and ultimately to full redundancy. Importantly, this approach allows for the monitoring of key nodes within the distribution network and enhances measurement redundancy without necessitating an increase in the number of placements.

The deployment of mobile networks has imposed an urgent requirement for the pursuit of low-carbon communication infrastructures. The increasing energy consumption of mobile networks has brought about challenges of techno-economic and environmental sustainability. Renewable energy-enabled mobile networks have received a lot of attention due to their capability to evade greenhouse gas emissions and easy availability. Microgeneration-based renewable energy provision is a feasible and effective solution for 5G networks. Dimensioning of microgeneration renewable energy power supply is an essential issue to make the system operate for a long period cost-effectively with a minimum amount of grid energy consumption. For effective deployment of microgeneration renewable energy system, it is essential to provision it with adequate PV panel capacity and storage devices. This work attempts to identify the cost-effective, energy-efficient, and emissions-aware sizing of PV panels and storage for 5G HetNet. An energy-saving strategy based on an optimal policy of advanced sleep modes and traffic-aware load offloading is developed and the interaction of the energy-saving strategy on the system dimensioning is explicitly examined. The proposed solution aims to ensure the communication quality of service whilst keeping the optimal cost-effective deployment and network operation. The system performance in terms of grid energy consumption, empty storage probability, emission performance, and total cost of the system is extensively assessed through experiments for a range of operational scenarios. The numerical results demonstrated that the proposed sustainable energy system dimensioning and operation integrated with an energy-saving strategy is energy-efficient and cost-effective.