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Optimization and Coordination Algorithms for Energy Management Systems

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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 17414

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Special Issue Editor

Dr. Abdulsalam Yassine

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Guest Editor

Department of Software Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Interests: AI and data analytics; smart cities; energy informatics; blockchain technology

Special Issue Information

Dear Colleagues,

Future energy management systems are expected to integrate Vehicle- to-Everything (V2X) energy transaction systems, such as Vehicle-to-Vehicle, Vehicle-to-Building, Vehicle-to-Grid, etc., to relieve the stress on the grid network and boost the adoption of clean energy. In this Special Issue, we welcome contributions outlining solutions that address pressing challenges related to optimizing matching algorithms, decentralized and centralized energy management and coordination algorithms, trust-based interaction methods, payment exchange mechanisms for energy management systems, and energy data privacy and security. Besides innovative methodologies and advancement of the state-of-the-art on energy management systems, solutions that address the exchange of energy credits by means of blockchain technology are also welcomed. Publication of this Special Issue on the advancements in energy management V2X will help accelerate the adoption of new approaches and applications in this field.

Dr. Abdulsalam Yassine
Guest Editor

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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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

energy management systems
V2X
optimization algorithms
energy trading
trust models
matching and coordination algorithms
optimization algorithms
blockchain

Published Papers (7 papers)

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Research

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16 pages, 426 KiB

Open AccessArticle

Application of the SSA for Optimal Reactive Power Compensation in Radial and Meshed Distribution Using D-STATCOMs

by Javier Andrés Mora-Burbano, Cristian David Fonseca-Díaz and Oscar Danilo Montoya

Algorithms 2022, 15(10), 345; https://doi.org/10.3390/a15100345 - 24 Sep 2022

Cited by 7 | Viewed by 1521

Abstract

This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, and commercial loads within a daily operation scenario. The optimal reactive power flow compensation problem is formulated through a mixed-integer nonlinear programming (MINLP) model. The objective function is associated with the minimization of the expected energy losses costs for a year of operation by considering the investment costs of D-STATCOMs. To solve the MINLP model, the application of a master–slave optimization approach is proposed, which combines the salp swarm algorithm (SSA) in the master stage and the matricial backward/forward power flow method in the slave stage. The master stage is entrusted with defining the optimal nodal location and sizes of the D-STATCOMs, while the slave stage deals with the power flow solution to determine the expected annual energy losses costs for each combination of nodes and sizes for the D-STATCOMs as provided by the SSA. To validate the effectiveness of the proposed master–slave optimizer, the IEEE 33-bus grid was selected as a test feeder. Numerical comparisons were made against the exact solution of the MINLP model with different solvers in the general algebraic modeling system (GAMS) software. All the simulations of the master–slave approach were implemented in the MATLAB programming environment (version 2021b). Numerical results showed that the SSA can provide multiple possible solutions for the studied problem, with small variations in the final objective function, which makes the proposed approach an efficient tool for decision-making in distribution companies. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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19 pages, 473 KiB

Open AccessArticle

Power Flow Solution in Bipolar DC Networks Considering a Neutral Wire and Unbalanced Loads: A Hyperbolic Approximation

by Simón Sepúlveda-García, Oscar Danilo Montoya and Alejandro Garcés

Algorithms 2022, 15(10), 341; https://doi.org/10.3390/a15100341 - 22 Sep 2022

Cited by 9 | Viewed by 1434

Abstract

This paper addresses the problem of the power flow analysis of bipolar direct current (DC) networks considering unbalanced loads and the effect of a neutral wire, which may be solidly grounded or non-grounded. The power flow problem is formulated using the nodal admittance representation of the system and the hyperbolic relations between power loads and voltages in the demand nodes. Using Taylor series expansion with linear terms, a recursive power flow method with quadratic convergence is proposed. The main advantage of the hyperbolic approximation in dealing with power flow problems in DC bipolar networks is that this method can analyze radial and meshed configurations without any modifications to the power flow formula. The numerical results in three test feeders composed of 4, 21, and 85 bus systems show the efficiency of the proposed power flow method. All of the simulations were conducted in MATLAB for a comparison of the proposed approach with the well-established successive approximation method for power flow studies in distribution networks. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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19 pages, 2574 KiB

Open AccessEditor’s ChoiceArticle

GA−Reinforced Deep Neural Network for Net Electric Load Forecasting in Microgrids with Renewable Energy Resources for Scheduling Battery Energy Storage Systems

by Chaoran Zheng, Mohsen Eskandari, Ming Li and Zeyue Sun

Algorithms 2022, 15(10), 338; https://doi.org/10.3390/a15100338 - 21 Sep 2022

Cited by 16 | Viewed by 3046

Abstract

The large−scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The microgrid concept has been proposed to locally control and manage a cluster of local distributed energy resources (DERs) and loads. If the net load power can be accurately predicted, it is possible to schedule/optimize the operation of battery energy storage systems (BESSs) through economic dispatch to cover intermittent renewables. However, the load curve of the microgrid is highly affected by various external factors, resulting in large fluctuations, which makes the prediction problematic. This paper predicts the net electric load of the microgrid using a deep neural network to realize a reliable power supply as well as reduce the cost of power generation. Considering that the backpropagation (BP) neural network has a good approximation effect as well as a strong adaptation ability, the load prediction model of the BP deep neural network is established. However, there are some defects in the BP neural network, such as the prediction effect, which is not precise enough and easily falls into a locally optimal solution. Hence, a genetic algorithm (GA)−reinforced deep neural network is introduced. By optimizing the weight and threshold of the BP network, the deficiency of the BP neural network algorithm is improved so that the prediction effect is realized and optimized. The results reveal that the error reduction in the mean square error (MSE) of the GA–BP neural network prediction is 2.0221, which is significantly smaller than the 30.3493 of the BP neural network prediction. Additionally, the error reduction is 93.3%. The error reductions of the root mean square error (RMSE) and mean absolute error (MAE) are 74.18% and 51.2%, respectively. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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15 pages, 2735 KiB

Open AccessArticle

Optimal Design Parameters for Hybrid DC Circuit Breakers Using a Multi-Objective Genetic Algorithm

by Van-Vinh Nguyen, Nhat-Tung Nguyen, Quang-Thuan Nguyen, Van-Hai Bui and Wencong Su

Algorithms 2022, 15(9), 298; https://doi.org/10.3390/a15090298 - 25 Aug 2022

Viewed by 1719

Abstract

The primary function of hybrid direct current circuit breakers (HCBs) is to quickly interrupt fault currents to protect high-voltage direct current (HVDC) systems. To enhance the reliability and stability of HVDC systems, optimal design of HCBs is required to minimize the peak fault current, interruption time, and recovery time. Therefore, this study develops a multi-objective genetic algorithm (MOGA)-based optimization model to identify the optimal parameters for HCBs. The MOGA model consists of three objective functions that provide trade-offs among reductions in the peak fault current, the interruption time, and the recovery time. The proposed algorithm is verified with a novel HCB topology using inverse current injection techniques. The performance of the HCB topology with the optimal parameters is validated in the MATLAB/Simulink environment. In addition, a comparison study between the optimal design of an HCB using the proposed algorithm and a typical HCB model is presented in this study to show the effectiveness of the proposed optimization method. Our simulation results show that the optimal parameter design of HCBs significantly reduces the magnitude of the peak fault current and operating time, thus maintaining the safe and stable operation of the entire system. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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16 pages, 476 KiB

Open AccessArticle

Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm

by Oscar Danilo Montoya, Walter Gil-González and Luis Fernando Grisales-Noreña

Algorithms 2022, 15(8), 277; https://doi.org/10.3390/a15080277 - 05 Aug 2022

Cited by 8 | Viewed by 1811

Abstract

This paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with the inclusion of solar photovoltaic (PV) generators while considering a planning period of 20 years. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which binary variables define the nodes where the PV generators must be located, and continuous variables are related to the power flow solution and the optimal sizes of the PV sources. The implementation of a master–slave optimization approach is proposed in order to address the complexity of the MINLP formulation. In the master stage, the discrete-continuous generalized normal distribution optimizer (DCGNDO) is implemented to define the nodes for the PV sources along with their sizes. The slave stage corresponds to a specialized power flow approach for monopolar DC networks known as the successive approximation power flow method, which helps determine the total energy generation at the substation terminals and its expected operative costs in the planning period. Numerical results in the 33- and 69-bus grids demonstrate the effectiveness of the DCGNDO optimizer compared to the discrete-continuous versions of the Chu and Beasley genetic algorithm and the vortex search algorithm. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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23 pages, 14110 KiB

Open AccessEditor’s ChoiceArticle

Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter

by Srilakshmi Koganti, Krishna Jyothi Koganti and Surender Reddy Salkuti

Algorithms 2022, 15(8), 256; https://doi.org/10.3390/a15080256 - 25 Jul 2022

Cited by 20 | Viewed by 3578

Abstract

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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Review

Jump to: Research

29 pages, 4471 KiB

Open AccessReview

Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review

by Laxman Bhukya, Narender Reddy Kedika and Surender Reddy Salkuti

Algorithms 2022, 15(10), 365; https://doi.org/10.3390/a15100365 - 29 Sep 2022

Cited by 27 | Viewed by 3196

Abstract

In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages and disadvantages. Hence, a proper review of these methods is essential. In this paper, a state-of-the-art review on various MPPT techniques based on their classifications, such as offline, online, and hybrid techniques under uniform and nonuniform irradiances, is presented. In comparison to offline and online MPPT methods, intelligent MPPT techniques have better tracking accuracy and tracking efficiency with less steady state oscillations. Unlike online and offline techniques, intelligent methods track the global MPP under partial shade conditions. This review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for additional research and development in the MPPT field. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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