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Coordination and Optimization of Energy Management in Smart Grids

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

Deadline for manuscript submissions: 28 June 2024 | Viewed by 8133

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Special Issue Editors

Dr. Vivek Prakash

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Guest Editor

Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
Interests: power and energy system management; power system operation and control

Dr. Bhanu Pratap Soni

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Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur 302017, India
Interests: optimization; smart grid; energy management; dimand side management; power system stability

Dr. Kailash Chand Sharma

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Department of Electrical Engineering, Dr. B R Ambedkar National Institute of Technology, Jalandhar, India
Interests: power system economics; electricity markets; energy forecasting; flexibility; energy storage; AI applications in electricity markets

Special Issue Information

Dear Colleagues,

The large integration of variable distributed energy resources (DERs), such as photovoltaic panels, wind power, electric vehicles, and energy storage systems, etc., poses a formidable challenge to the power and energy system management. The variable and uncertain generation characteristics of DERs would make power system operation challenging to continuously balance generation and demand. An excess or scarcity of electricity in the production or consumption of energy can disrupt the system operation and cause serious difficulties to maintain voltage and frequency within prescribed limits. In extreme cases, it may result in power outages and the shutdown of the complete system. Energy management systems can efficiently increase the balance between supply and demand while reducing peak load during unscheduled periods. The use of energy management systems can effectively increase the balance between supply and demand and decrease peak load throughout unplanned durations. The energy management system can handle distributing or exchanging energy among the many energy resources available and economically supplying loads in a stable, safe, and effective manner under all power grid operating situations.

Dr. Vivek Prakash
Dr. Bhanu Pratap Soni
Dr. Kailash Chand Sharma
Guest Editors

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Published Papers (7 papers)

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Research

16 pages, 3500 KiB

Open AccessArticle

Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

by Zahraa Hijazi and Junho Hong

Energies 2024, 17(6), 1420; https://doi.org/10.3390/en17061420 - 15 Mar 2024

Viewed by 500

Abstract

Over the past few years as COVID-19 was declared a worldwide pandemic that resulted in load changes and an increase in residential loads, utilities have faced increasing challenges in maintaining load balance. Because out-of-home activities were limited, daily residential electricity consumption increased by about 12–30% with variable peak hours. In addition, battery energy storage systems (BESSs) became more affordable, and thus higher storage system adoption rates were witnessed. This variation created uncertainties for electric grid operators. The objective of this research is to study the optimal operation of residential battery storage systems to maximize utility benefits. This is accomplished by formulating an objective function to minimize distribution and generation losses, generation fuel prices, market fuel prices, generation at peak time, and battery operation cost and to maximize battery capacity. A mixed-integer linear programming (MILP) method has been developed and implemented for these purposes. A residential utility circuit has been selected for a case study. The circuit includes 315 buses and 100 battery energy storage systems without the connection of other distributed energy resources (DERs), e.g., photovoltaic and wind. Assuming that the batteries are charging overnight, the results show that energy costs can be reduced by 10% and losses can decrease by 17% by optimally operating batteries to support increased load demand. Full article

(This article belongs to the Special Issue Coordination and Optimization of Energy Management in Smart Grids)

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29 pages, 1161 KiB

Open AccessFeature PaperArticle

Coordination Model and Digital Twins for Managing Energy Consumption and Production in a Smart Grid

by Philippe Glass and Giovanna Di Marzo Serugendo

Energies 2023, 16(22), 7629; https://doi.org/10.3390/en16227629 - 17 Nov 2023

Cited by 1 | Viewed by 712

Abstract

Smart grids play an important role for energy management by directly supporting the socio-ecological transition of neighbourhoods. This research provides the design of a coordination model to enable the management and exchange of electrical energy between producers and consumers at a micro-grid level. This model, which derives from the SAPERE coordination model, allows the intelligent digital twins to interact and generate services on the fly to meet different needs in real time. We have designed producer and consumer digital twins, which autonomously generate supply contracts in the form of a transaction, and supervisor digital twins, which regulate energy at the node level, managing threshold violations and proactively avoiding future threshold violations by using predictions. This coordination model allows energy exchanges in a single node and in a micro-grid structure that contains several neighbouring nodes. We have implemented and tested the platform with realistic data, based on the consumption statistics of a real household, and with real data, collected in the living-lab of “Les Vergers” located near Geneva. The results show that the combination of a coordination model and intelligent digital twins actually supports self-adaptive energy management in a smart grid. Such approaches are fundamental to develop efficient and reliable smart grids. Full article

(This article belongs to the Special Issue Coordination and Optimization of Energy Management in Smart Grids)

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20 pages, 4914 KiB

Open AccessArticle

Development of an Optimal Port Crane Trajectory for Reduced Energy Consumption

by Rofhiwa Lutendo Edward Takalani and Lesedi Masisi

Energies 2023, 16(20), 7172; https://doi.org/10.3390/en16207172 - 20 Oct 2023

Viewed by 948

Abstract

This paper is concerned with the development of an optimal load-handling trajectory for port cranes. The objective is to minimize load cycle time and reduce energy consumption. Energetic macroscopic representation formalism is used to model a port crane load-handling mechanism. The crane model developed includes the mathematical model, the crane’s local control system, and a MATLAB/Simulink model for simulation. The particle swarm optimization algorithm is used to find the set of pareto optimal crane trajectories given the variation in crane size, ship size, and wind speed. Experimental validation of the crane model is conducted by comparing it with a real-world crane. Simulation results show that the optimal crane load trajectory is 38% faster and more productive than the nonoptimal crane load trajectory. Furthermore, the results show that the optimal trajectory reduces the cranes’ peak power and energy consumption by 36% when compared with the nonoptimal trajectory. Full article

(This article belongs to the Special Issue Coordination and Optimization of Energy Management in Smart Grids)

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17 pages, 4773 KiB

Open AccessArticle

Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation

by Yi Hao, Zhigang Huang, Shiqian Ma, Jiakai Huang, Jiahao Chen and Bing Sun

Energies 2023, 16(16), 6062; https://doi.org/10.3390/en16166062 - 18 Aug 2023

Viewed by 579

Abstract

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW. Full article

(This article belongs to the Special Issue Coordination and Optimization of Energy Management in Smart Grids)

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17 pages, 2587 KiB

Open AccessArticle

Microgrid Optimal Dispatch Based on Distributed Economic Model Predictive Control Algorithm

by Yuxiang Peng, Wenqian Jiang, Xingqiu Wei, Juntao Pan, Xiangyu Kong and Zhou Yang

Energies 2023, 16(12), 4658; https://doi.org/10.3390/en16124658 - 12 Jun 2023

Cited by 1 | Viewed by 984

Abstract

A microgrid cluster is composed of multiple interconnected microgrids and operates in the form of cluster, which can realize energy complementation between microgrids and significantly improve their renewable energy consumption capacity and system operation reliability. A microgrid optimal dispatch based on a distributed economic model predictive control algorithm is proposed in this paper. Firstly, the control task of the microgrid power generation system is defined, which is required to meet the load demand while reducing the economic loss of the system and realize dynamic economic optimization. The global objective function is designed based on the control task, and the detailed design method of the distributed economic model predictive controller is given. The control law is obtained by an iterative calculation using the Nash optimal method, which can effectively reduce the amount of data in the communication network. Finally, a microgrid group composed of four microgrids is used as an example for simulation verification. The simulation results show that the distributed economic model predictive control algorithm proposed in this paper has good economic benefits for microgrid dispatching. Full article

(This article belongs to the Special Issue Coordination and Optimization of Energy Management in Smart Grids)

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28 pages, 13970 KiB

Open AccessArticle

DC Link Voltage Enhancement in DC Microgrid Using PV Based High Gain Converter with Cascaded Fuzzy Logic Controller

by Senthilnathan Rajendran, Vigneysh Thangavel, Narayanan Krishnan and Natarajan Prabaharan

Energies 2023, 16(9), 3928; https://doi.org/10.3390/en16093928 - 6 May 2023

Cited by 4 | Viewed by 1895

Abstract

Renewable-based sources can be interconnected through power electronic converters and connected with local loads and energy storage devices to form a microgrid. Nowadays, DC microgrids are gaining more popularity due to their higher efficiency and reliability as compared to AC microgrid systems. The DC Microgrid has power electronics converters between the DC loads and renewable-based energy sources. The power converters controlled with an efficient control algorithm for maintaining stable DC bus voltage in DC microgrids under various operating modes is a challenging task for researchers. With an aim to address the above-mentioned issues, this study focuses on the DC link voltage enhancement of a DC Microgrid system consisting of PV, DFIG-based wind energy conversion system (WECS), and battery Energy Storage System (ESS). To elevate PV output voltage and minimize the oscillations in DC link voltage, a high-gain Luo converter with Cascaded Fuzzy Logic Controller (CFLC) is proposed. Droop control with virtual inertia and damping control is proposed for DFIG-based WECS to provide inertia support. Artificial Neural Network (ANN) based droop control is utilised to regulate the ESS’s State of Charge (SOC). The effectiveness of the proposed converter and its control algorithms for maintaining stable DC bus link voltage has been analysed using MATLAB/Simulink and experimentally validated using a prototype model and FPGA Spartan 6E controllers. Full article

(This article belongs to the Special Issue Coordination and Optimization of Energy Management in Smart Grids)

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27 pages, 9040 KiB

Open AccessArticle

Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation

by Guodong Liu, Maximiliano F. Ferrari, Thomas B. Ollis, Aditya Sundararajan, Mohammed Olama and Yang Chen

Energies 2023, 16(7), 3014; https://doi.org/10.3390/en16073014 - 25 Mar 2023

Cited by 6 | Viewed by 1613

Abstract

For the cooperative operation of networked microgrids, a distributed energy management considering network operational objectives and constraints is proposed in this work. Considering various ownership and privacy requirements of microgrids, utility directly interfaced distributed energy resources (DERs) and demand response, a distributed optimization is proposed for obtaining optimal network operational objectives with constraints satisfied through iteratively updated price signals. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve the formulated distributed optimization. The proposed distributed energy management provides microgrids, utility-directly interfaced DERs and responsive demands the opportunity of contributing to better network operational objectives while preserving their privacy and autonomy. Results of numerical simulation using a networked microgrids system consisting of several microgrids, utility directly interfaced DERs and responsive demands validate the soundness and accuracy of the proposed distributed energy management. The proposed method is further tested on a practical two-microgrid system located in Adjuntas, Puerto Rico, and the applicability of the proposed strategy is validated through hardware-in-the-loop (HIL) testing. Full article

(This article belongs to the Special Issue Coordination and Optimization of Energy Management in Smart Grids)

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