Microgrids and Its Application to Integrated Energy Systems and Islanded Active Distribution Networks, 2nd Edition

Dear Colleagues,

Future active distribution networks will incorporate a combination of distributed generators (DGs), microgrids (MGs), and different types of renewable-based distributed energy resources (DERs), allowing them to provide ancillary services in grid-connected mode and, if necessary, to operate in an islanded mode to increase network proactivity, continuity of supply, reliability, and resilience.

This Special Issue aims to publish high-quality research papers on the inter-disciplinary field of microgrid optimization under uncertainty, and targets new probabilistic methods (i.e., scalable computational solutions) for managing microgrid energy management systems (EMS), from long-term planning to real-time operation, with the introduction of a wide range of renewable energy technologies. Research sought includes innovative approaches to the analytical and simulation techniques for assessing the optimal operation and control of microgrids and DERs, while committing, holding, dispatching, and maintaining different ancillary services for the grid in a reliable and economical manner. Research can be extended to self-optimizing control solutions for building microgrids (BMGs) for integration in net-zero energy buildings, as well as electric mobility (vehicle-to-grid, V2G, or boat-to-grid, B2G) within maritime, island microgrids, or integrated port energy systems.

The scope includes the predictive maintenance and fault detection capabilities in hybrid (grid-connected and islanded) microgrids to ensure a smooth operation and maximize the self-consumption of renewable energy intermittency through predictive control, artificial intelligence, machine learning, and/or novel forecasting techniques.

This Special Issue aims to examine original research papers as well as review articles on the most recent developments and research efforts on this subject.

Prof. Dr. Ionel Vechiu
Dr. Ignacio Hernando-Gil
Dr. Chenghong Gu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• optimization under uncertainty
• risk modelling
• quality of supply assessment
• grid resiliency performance
• probabilistic network analysis
• integration of RES
• behind-the-meter energy storage systems
• utility-scale storage systems
• hydrogen-based energy storage systems
• AC/DC microgrids
• building microgrids
• net-zero energy buildings (NZEB)
• integrated port energy systems
• island microgrids
• maritime microgrids
• microgrid clusters/communities
• ancillary services under high variable RES penetration
• ancillary services in islanded and grid-connected mode
• vehicle-to-grid (V2G) or boat-to-grid (B2G)

• Microgrids and Its Application to Integrated Energy Systems and Islanded Active Distribution Networks in Energies (14 articles)

Title: Application of the paraller hybrid heuristic optimization algorithm to solution problem of economic load dispatch problem
Authors: Łukasz Knypiński
Affiliation: Poznan University of Technology

Title: Robust Energy Management Policies for Solar Microgrids via Reinforcement Learning
Authors: Gerald Jones; Xueping Li; Yulin Sun
Affiliation: University of Tennessee
Abstract: As renewable energy integration expands, effective energy system management becomes increasingly crucial. Distributed renewable generation microgrids offer green energy and resilience. Combining them with energy storage and suitable Energy Management System Strategies (EMSS) is essential due to the variability in renewable energy generation. Reinforcement Learning (RL)-based EMSS has shown promising results in handling these complexities. However, concerns about policy robustness arise with the growing number of grid disruptions. This work evaluates policies derived from Actor-Critic (A2C) and Proximal Policy Optimization (PPO) networks. These policies are optimized for cost coverage in grid-connected mode and demand coverage in islanded scenarios. Additionally, comparisons are made between methods of training RL agents in connected, isolated, or intermittently disrupted scenarios through online simulation. These experiments aim to determine if a single network handles both situations, and if models trained for specific scenarios provide the best performance. Stochastic models incorporating solar energy and load uncertainties, using real-world data from Knoxville, TN, are employed in simulation. The results of cost and demand coverage achieved by the trained networks are compared to those obtained from a stochastic day-ahead forecasting model to examine the optimality of the generated policies. The findings indicate that PPO and A2C excel in cost coverage, with PPO performing better. However, in isolated scenarios, the demand coverage performance hierarchy shifts. This study enhances the understanding of the resilience of solutions generated by reinforcement learning agents to unforeseen grid disruptions and opens avenues for RL agents to excel in either grid-connected or isolated cases.

Title: Planning and scheduling a system for charging of electric vehicles
Authors: Jacek Kamiński, Przemysław Kaszyński, Tomasz Śliwiński, Piotrek Pałka, Bogdan Ruszczak, Marta Kuta.
Affiliation: Poland
Abstract: The main research problem addressed in the paper is the development of an integrated optimization system for supporting drivers' decisions regarding planning and scheduling electric vehicle charging on a route. The integrated system consists of an optimization and a machine learning modules/models. The optimization model was formulated as a mixed-integer linear programming (MILP) problem and was implemented using GAMS. Numerical calculations were executed using the CPLEX solver. The model was formulated with the application of sets of elements, including edges and nodes of the route, as well as charging stations for electric vehicles and time zones in which the trip can take place. The minimization of the multi-criteria objective function was proposed in the study. The decision criteria include: (i) vehicle charging cost, (ii) travel time, and (iii) penalties for not meeting the driver’s requirements (binary preferences such as ATMs, restaurants, shops, etc.). Obtained model results for three assumed scenarios prove the proper functioning of the developed optimization model and the selection of charging stations in accordance with model assumptions/constraints and examined objective function criteria. The paper also evaluates several machine learning architectures to develop an estimation model that would provide information about the energy consumption for the road segments that are the input data to the optimization model. Among all the models that were verified using the actual registered drives data, we found the extreme gradient boosting model to be the best performing. This estimator provided us with the lowest errors of less than 1 kWh per 100 km, while at the same time allowing for sufficiently fast predictions.