Sustainable Energy Grids & Networks最新文献

This study introduces a significant advancement in peer-to-peer (P2P) energy trading systems within smart grids, addressing a crucial gap in existing research by incorporating optimal energy storage capacities to accommodate varying energy demands resulting from lifestyle changes. Through a two-level optimization approach, aimed at maximizing self consumption and optimizing energy flow within the grid, we propose a novel energy management strategy. Our contribution lies in the introduction of a new layer of deep learning and rules control, forming a self-energy sharing system for each prosumer. This architecture, termed the smart node, integrates deep learning techniques, to predict and customize energy services through dynamic adjustment of lower and upper bounds of battery capacities. Additionally, we leverage cellular automaton (CA) approaches to establish sustainable consensus among P2P network users, enhancing the adaptability and efficiency of the energy management system. The results show that the proposed algorithm could reduce the energy consumed by the P2P community from the utility by around 20% and maximize the collective self-consumption by around 8% compared to conventional energy trading in microgrids.

In this study, we introduce an innovative deep learning framework designed to achieve precise detection, localization, and rate estimation of gas distribution pipeline system leakages. Our method surpasses conventional statistical approaches, particularly those based on Bayesian inference, by accommodating the system’s intricate behaviors, including variable usage and production from both sources and sinks. Notably, our approach demonstrates remarkable accuracy in localizing leakages even amidst multiple occurrences within the system. Specifically, achieving over 98% accuracy in single-leakage scenarios underscores its effectiveness. Furthermore, through data augmentation involving the introduction of noise into the training dataset, we significantly enhance the model’s performance, particularly when tested against real-world-like noisy data. This study not only showcases the efficacy of our proposed deep learning framework but also underscores its adaptability and robustness in addressing complex challenges in gas pipeline systems.

This paper presents a week-long scheduling approach to address the issues associated with uncertain stochastic generation. Specifically, the method is designed for active distribution networks (ADNs) hosting hybrid energy storages, composed by a hydrogen energy storage system (HESS) and a battery energy storage system (BESS). The inclusion of a pressurized HESS allows to balance energy over longer time periods, as opposed to methods considering only BESSs. To this end, this paper combines linearized models for the electricity grid with linearized models of the HESS to solve a tractable scheduling problem. The proposed optimal schedule consists of an active power trajectory at the grid connection point (GCP), called the dispatch plan, and the unit commitment schedule of a PEM fuel cell and electrolyzer system interfacing the electricity network with the HESS. Additionally, a bilevel model predictive control strategy is proposed, where the upper layer MPC computes a storage target accounting for the full horizon, while the lower layer computes the controllable resource setpoints to minimize the dispatch tracking error in each period. A numerical experiment shows the effectiveness of the proposed scheduling and control to accurately compute and track a dispatch plan over a full week. The results clearly show the benefits of combining a HESS with a BESS especially in periods where the prosumption is highly uncertain. Finally, we discuss the computational challenges associated with the weekly horizon and the use of a HESS that exhibits different dynamics than a BESS and propose an approach to mitigate the computational cost.

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.

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.

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.

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.