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Special Issue "Optimal Dispatch of Smart Grids"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A5: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (10 August 2020) | Viewed by 2171

Special Issue Editors

Prof. Dr. José María Yusta-Loyo
E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Zaragoza, Calle María de Luna 3, 50018 Zaragoza, Spain
Interests: energy markets; optimal dispatch of smart grids; vulnerability assessment of critical infrastructure
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Antonio Domínguez-Navarro
E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Zaragoza, Calle María de Luna 3, 50018 Zaragoza, Spain
Interests: electrical network planning; renewable energy integration; application of computing techniques (neural networks, fuzzy systems and heuristic optimization algorithms)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my pleasure to invite submissions to the Special Issue on “Artificial Intelligence and Mathematical Models Applied in Optimal Dispatch of Smart Grids”. Energy policy strategies are promoting renewable energy to address global warming, reduce their dependence on fossil-fuel-based electricity, and improve the security of energy supply. Introducing renewable energy to an increasingly competitive electricity market requires new technologies and operating systems to address new technical and economic challenges arising from the optimal integration of available resources. Smart grids, virtual power plants and digital transformation are keys to this integration.

In this Special Issue, we invite original and unpublished research work in areas including (but not limited to):

- Optimal techno-economic dispatch of smart grids and virtual power plants

- Mathematical models for integration of distributed energy resources and demand in electricity markets

- Aggregated flexible electricity consumption and generation

- Real implementation of virtual power plants

- Optimization methods for managing uncertainties from renewable energy sources in smart grid dispatch

- Big data and machine learning in smart grid operation

- Multi-agent systems for smart grid dispatch

- Energy management systems for coordinating power flow between generators, loads and storage

- Provision of ancillary services by smart grids

Prof. Dr. Jose M. Yusta
Prof. Dr. José Antonio Domínguez-Navarro
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 2200 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

  • Smart grids
  • Virtual power plants
  • Economic dispatch
  • Distributed energy resources
  • Electricity markets
  • Optimization techniques
  • Artificial intelligence
  • Smart grid operation
  • Virtual power plants
  • Renewable energy self-consumption
  • Economic dispatch
  • Energy management
  • Demand aggregation
  • Distributed energy resources
  • Demand response
  • Energy storage for renewables integration
  • Renewable generation uncertainties
  • PV hosting capacity
  • IT and OT for smart grids
  • Electricity markets
  • Ancillary services
  • Balancing markets
  • Energy security challenges
  • Vulnerability assessment of smart grids
  • Power grid resilience
  • Optimization techniques
  • Artificial intelligence
  • Multi-agent systems
  • Big data
  • Machine learning

Published Papers (2 papers)

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Research

Article
Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model
Energies 2020, 13(11), 2900; https://doi.org/10.3390/en13112900 - 05 Jun 2020
Cited by 12 | Viewed by 1004
Abstract
The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in [...] Read more.
The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs. Full article
(This article belongs to the Special Issue Optimal Dispatch of Smart Grids)
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Article
Distribution Power Loss Reduction of Standalone DC Microgrids Using Adaptive Differential Evolution-Based Control for Distributed Battery Systems
Energies 2020, 13(9), 2129; https://doi.org/10.3390/en13092129 - 27 Apr 2020
Cited by 11 | Viewed by 884
Abstract
With high penetrations of renewable energy sources (RES), distributed battery systems (DBS) are widely adopted in standalone DC microgrids to stabilize the bus voltages by balancing the active power. This paper presents an Adaptive Differential Evolution (ADE)-based hierarchical control for DBS to achieve [...] Read more.
With high penetrations of renewable energy sources (RES), distributed battery systems (DBS) are widely adopted in standalone DC microgrids to stabilize the bus voltages by balancing the active power. This paper presents an Adaptive Differential Evolution (ADE)-based hierarchical control for DBS to achieve online distribution power loss mitigation as well as bus voltage regulations in standalone DC microgrids. The hierarchical control comprises two layers, i.e., ADE for the secondary layer and local proportional-integral (PI) control for the primary layer. The secondary layer control provides the bus voltage references for the primary control by optimizing the fitness function, which contains the parameters of the bus voltage deviations and the power loss on the distribution lines. Simultaneously, the state-of-charge (SoC) of the battery packs are controlled by local controllers to prevent over-charge and deep-discharge. Case studies using a Real-Time Digital Simulator (RTDS) validate that the proposed ADE-based hierarchical control can effectively reduce the distribution power loss and regulate the bus voltages within the tolerances in DC microgrids. Full article
(This article belongs to the Special Issue Optimal Dispatch of Smart Grids)
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