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Energies
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Management of Intelligent Distributed Energy Resources
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Management of Intelligent Distributed Energy Resources

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10138

Special Issue Editors

Dr. Seppo Sierla

E-Mail Website
Guest Editor
Department of Electrical Engineering and Automation, Aalto University, 02150 Aalto, Finland
Interests: simulation; digital twin; virtual power plant; demand response; industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Mahdi Pourakbari-Kasmaei

E-Mail Website
Guest Editor
Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland
Interests: power and energy systems; electric vehicle; high voltage; community energy systems; electricity markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The penetration of distributed generation, energy storage, and smart loads has resulted in the emergence of intelligent distributed energy resources—entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably on the available electricity markets. However, such resources usually have a primary purpose, which imposes constraints on the exploitation of the resource; for example, the primary purpose of an electric vehicle battery is for driving, so the battery could be used as temporary storage for excess photovoltaic energy only if the vehicle is available for driving when the owner expects it to be. The aggregation of several distributed energy resources is a solution for coping with the unavailability of one resource. Solutions are needed for managing the electricity production and consumption characteristics of diverse distributed energy resources to achieve more generic capabilities and services for electricity production, storage, and consumption. These solutions include methods for integrating, aggregating, orchestrating, and coordinating intelligent distributed energy resources. This Special Issue welcomes original research papers as well as reviews addressing any of the above issues.

Dr. Seppo Sierla
Dr. Mahdi Pourakbari-Kasmaei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Keywords

  • prosumer
  • virtual power plant
  • demand response
  • distributed energy resource
  • energy storage

Published Papers (4 papers)

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Research

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18 pages, 8745 KiB  
Article
Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation
by Vidura Sumanasena, Lakshitha Gunasekara, Sachin Kahawala, Nishan Mills, Daswin De Silva, Mahdi Jalili, Seppo Sierla and Andrew Jennings
Energies 2023, 16(5), 2245; https://doi.org/10.3390/en16052245 - 26 Feb 2023
Cited by 1 | Viewed by 2359
Abstract
Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the [...] Read more.
Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap in EVI. Results from the empirical evaluation of this AI framework on a real-world EVI case study confirm its contribution towards addressing the emerging challenges of distributed energy resources in EV adoption. Full article
(This article belongs to the Special Issue Management of Intelligent Distributed Energy Resources)
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22 pages, 3487 KiB  
Article
Analyzing the Impact of EV and BESS Deployment on PV Hosting Capacity of Distribution Networks
by Robin Filip, Verner Püvi, Martin Paar and Matti Lehtonen
Energies 2022, 15(21), 7921; https://doi.org/10.3390/en15217921 - 25 Oct 2022
Cited by 5 | Viewed by 1425
Abstract
The current article analyzes the impact of charging electric vehicles and battery energy storage systems on the photovoltaic hosting capacity of low-voltage distribution networks. A Monte Carlo-based simulation is used to analyze predominantly rural, intermediate and predominantly urban residential regions facing different penetrations [...] Read more.
The current article analyzes the impact of charging electric vehicles and battery energy storage systems on the photovoltaic hosting capacity of low-voltage distribution networks. A Monte Carlo-based simulation is used to analyze predominantly rural, intermediate and predominantly urban residential regions facing different penetrations of electric vehicles utilizing uncontrolled and controlled charging, and evaluate their impact on photovoltaic hosting capacity. Subsequently, electric vehicles are replaced or supplemented by residential battery energy storage systems, and their combined impact on the hosting capacity is studied. The results revealed that electric vehicles solely do not improve the hosting capacity unless they are connected to the network during sunshine hours. However, controlled storage provides a remarkable increase to the hosting capacity and exceptional contribution in combination with electric vehicles and customers with high loads. Finally, a feasibility analysis showed that controlled charging of the storage has a lower marginal cost of increasing hosting capacity as compared to network reinforcement. Full article
(This article belongs to the Special Issue Management of Intelligent Distributed Energy Resources)
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19 pages, 5001 KiB  
Article
Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach
by Harri Aaltonen, Seppo Sierla, Ville Kyrki, Mahdi Pourakbari-Kasmaei and Valeriy Vyatkin
Energies 2022, 15(14), 4960; https://doi.org/10.3390/en15144960 - 6 Jul 2022
Cited by 2 | Viewed by 2276
Abstract
Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multi-objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to [...] Read more.
Battery storage is emerging as a key component of intelligent green electricitiy systems. The battery is monetized through market participation, which usually involves bidding. Bidding is a multi-objective optimization problem, involving targets such as maximizing market compensation and minimizing penalties for failing to provide the service and costs for battery aging. In this article, battery participation is investigated on primary frequency reserve markets. Reinforcement learning is applied for the optimization. In previous research, only simplified formulations of battery aging have been used in the reinforcement learning formulation, so it is unclear how the optimizer would perform with a real battery. In this article, a physics-based battery aging model is used to assess the aging. The contribution of this article is a methodology involving a realistic battery simulation to assess the performance of the trained RL agent with respect to battery aging in order to inform the selection of the weighting of the aging term in the RL reward formula. The RL agent performs day-ahead bidding on the Finnish Frequency Containment Reserves for Normal Operation market, with the objective of maximizing market compensation, minimizing market penalties and minimizing aging costs. Full article
(This article belongs to the Special Issue Management of Intelligent Distributed Energy Resources)
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Review

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25 pages, 2695 KiB  
Review
A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems
by Seppo Sierla, Heikki Ihasalo and Valeriy Vyatkin
Energies 2022, 15(10), 3526; https://doi.org/10.3390/en15103526 - 11 May 2022
Cited by 14 | Viewed by 3451
Abstract
Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to [...] Read more.
Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to optimize one or more targets, such as indoor air quality, energy consumption and energy cost. The agent receives feedback from the HVAC systems to quantify how well these targets have been achieved. The feedback is captured by a reward function designed by the developer of the reinforcement learning agent. A few reviews have focused on the reward aspect of reinforcement learning applications for HVAC. However, there is a lack of reviews that assess how the actions of the reinforcement learning agent have been formulated, and how this impacts the possibilities to achieve various optimization targets in single zone or multi-zone buildings. The aim of this review is to identify the action formulations in the literature and to assess how the choice of formulation impacts the level of abstraction at which the HVAC systems are considered. Our methodology involves a search string in the Web of Science database and a list of selection criteria applied to each article in the search results. For each selected article, a three-tier categorization of the selected articles has been performed. Firstly, the applicability of the approach to buildings with one or more zones is considered. Secondly, the articles are categorized by the type of action taken by the agent, such as a binary, discrete or continuous action. Thirdly, the articles are categorized by the aspects of the indoor environment being controlled, namely temperature, humidity or air quality. The main result of the review is this three-tier categorization that reveals the community’s emphasis on specific HVAC applications, as well as the readiness to interface the reinforcement learning solutions to HVAC systems. The article concludes with a discussion of trends in the field as well as challenges that require further research. Full article
(This article belongs to the Special Issue Management of Intelligent Distributed Energy Resources)
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