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Strategies and Algorithms for Energy Management Optimization of Renewable Energy Based Microgrids

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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: closed (10 June 2022) | Viewed by 11611

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Special Issue Editors

Prof. Dr. Yun-Su Kim

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Guest Editor

Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju, Korea
Interests: distribution network; microgrid; renewable energy; distributed generation
Special Issues, Collections and Topics in MDPI journals

Dr. Jin-Oh Lee

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Guest Editor

Korea Electrotechnology Research Institute (KERI), 27 Dosicheomdansaneop-ro, Nam-gu, Gwangju, Korea
Interests: DC distribution systems; microgrids; energy management; renewable energy sources

Special Issue Information

Dear Colleagues,

Energy transition to future power systems can improve energy efficiency and reduce environmental problems but may cause difficulties in system operation. Microgrids can be one of the essential part for future power system by providing engineering solutions under the proliferation of renewable energy resources. There are many successful cases of microgrid field demonstration, but advanced techniques to cope with high penetration level of renewable energy sources are still necessary. Among the various functions of microgrids, energy management system can effectively solve problems associated with renewable energy sources using the state-of-the-art method.

This Special Issue focuses on the state-of-the-art strategies and algorithms of energy management system for renewable energy-based microgrids. The scope of this Special Issue includes (but is not limited to) the following:

Application and analysis of energy management system of AC, DC, and hybrid AC/DC microgrids.
Energy management system of campus, factory, military, and building microgrids for islanded mode and grid-connected mode.
Optimization techniques for handling uncertainty in renewable energy sources: design, operation, and control method using energy storage systems, demand-side response, virtual power plant, electric vehicle, etc.;
Algorithm design and mathematical formulation for optimal operation and control of renewable energy-based microgrids.
Distributed and decentralized energy management between distributed energy resources or microgrids.

Prof. Yun-Su Kim
Dr. Jin-Oh Lee
Guest Editors

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Keywords

Microgrid
Energy Management
Renewable Energy
Optimization
Algorithm
Strategy
Formulation

Published Papers (5 papers)

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Research

17 pages, 1943 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Stochastic Distributed Control for Arbitrarily Connected Microgrid Clusters

by Maryam Khanbaghi and Aleksandar Zecevic

Energies 2022, 15(14), 5163; https://doi.org/10.3390/en15145163 - 16 Jul 2022

Cited by 3 | Viewed by 2206

Abstract

Due to the success of single microgrids, the coming years are likely to see a transformation of the current electric power system to a multiple microgrid network. Despite its obvious promise, however, this paradigm still faces many challenges, particularly when it comes to the control and coordination of energy exchanges between subsystems. In view of that, in this paper we propose an optimal stochastic control strategy in which microgrids are modeled as stochastic hybrid dynamic systems. The optimal control is based on the jump linear theory and is used as a means to maximize energy storage and the utilization of renewable energy sources in islanded microgrid clusters. Once the gain matrices are obtained, the concept of ε-suboptimality is applied to determine appropriate levels of power exchange between microgrids for any given interconnection pattern. It is shown that this approach can be efficiently applied to large-scale systems and guarantees their connective stability. Simulation results for a three microgrid cluster are provided as proof of concept. Full article

(This article belongs to the Special Issue Strategies and Algorithms for Energy Management Optimization of Renewable Energy Based Microgrids)

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14 pages, 1951 KiB

Open AccessArticle

Optimal Scheduling Model of a Battery Energy Storage System in the Unit Commitment Problem Using Special Ordered Set

by Insu Do and Siyoung Lee

Energies 2022, 15(9), 3079; https://doi.org/10.3390/en15093079 - 22 Apr 2022

Cited by 2 | Viewed by 1571

Abstract

Nonlinear characteristics of a battery energy storage system (BESS) may cause errors in the stored energy between the operation plan and the actual operation. These errors may hinder the reliability of the power system especially in environments such as microgrids with limited power generation resources and high uncertainty. This study proposes a method to alleviate the occurrence of such errors in the charging/discharging scheduling process of the BESS by piecewise linearizing its nonlinear characteristics. Specifically, the stored energy in a BESS that changes nonlinearly according to the size of the charging/discharging power was modeled using the special ordered set of the type 2 (SOS2) method. The proposed model and the typical BESS-operation models with constant power conditioning system (PCS) input/output power efficiency were applied to the unit commitment (UC) problem in a microgrid environment, and the results were compared with the actual operation results. The proposed model operated similarly to the actual operation compared to the typical model, reducing the error in charging/discharging energy. Consequently, the proposed model was made cost-effective by reducing the cost of error correction and reduced the risk of deviating from operating range of the BESS. This study demonstrates that the proposed method can efficiently solve the operational problems caused by the nonlinear characteristics of BESS. Full article

(This article belongs to the Special Issue Strategies and Algorithms for Energy Management Optimization of Renewable Energy Based Microgrids)

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19 pages, 1568 KiB

Open AccessArticle

Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization

by Usman Bashir Tayab, Junwei Lu, Seyedfoad Taghizadeh, Ahmed Sayed M. Metwally and Muhammad Kashif

Energies 2021, 14(24), 8489; https://doi.org/10.3390/en14248489 - 16 Dec 2021

Cited by 7 | Viewed by 2253

Abstract

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy. Full article

(This article belongs to the Special Issue Strategies and Algorithms for Energy Management Optimization of Renewable Energy Based Microgrids)

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29 pages, 2592 KiB

Open AccessArticle

A Market-Driven Management Model for Renewable-Powered Undergrid Mini-Grids

by Tatiana González Grandón, Fernando de Cuadra García and Ignacio Pérez-Arriaga

Energies 2021, 14(23), 7881; https://doi.org/10.3390/en14237881 - 24 Nov 2021

Viewed by 2065

Abstract

Renewable-powered “undergrid mini-grids” (UMGs) are instrumental for electrification in developing countries. An UMG can be installed under a—possibly unreliable— main grid to improve the local reliability or the main grid may “arrive” and connect to a previously isolated mini-grid. Minimising costs is key to reducing risks associated with UMG development. This article presents a novel market-logic strategy for the optimal operation of UMGs that can incorporate multiple types of controllable loads, customer smart curtailment based on reliability requirements, storage management, and exports to and imports from a main grid, which is subject to failure. The formulation results in a mixed-integer linear programming model (MILP) and assumes accurate predictions of the following uncertain parameters: grid spot prices, outages of the main grid, solar availability and demand profiles. An AC hybrid solar-battery-diesel UMG configuration from Nigeria is used as a case example, and numerical simulations are presented. The load-following (LF) and cycle-charging (CC) strategies are compared with our predictive strategy and HOMER Pro’s Predictive dispatch. Results prove the generality and adequacy of the market-logic dispatch model and help assess the relevance of outages of the main grid and of spot prices above the other uncertain input factors. Comparison results show that the proposed market-logic operation approach performs better in terms of cost minimisation, higher renewable fraction and lower diesel use with respect to the conventional LF and CC operating strategies. Full article

(This article belongs to the Special Issue Strategies and Algorithms for Energy Management Optimization of Renewable Energy Based Microgrids)

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15 pages, 5507 KiB

Open AccessArticle

Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

by Woan-Ho Park, Hamza Abunima, Mark B. Glick and Yun-Su Kim

Energies 2021, 14(19), 6038; https://doi.org/10.3390/en14196038 - 23 Sep 2021

Cited by 3 | Viewed by 2522

Abstract

The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost minimization algorithm preferentially selects intervals with low curtailment costs to prevent the curtailment from being concentrated at a specific time. By inducing even distribution of curtailment, this novel optimization methodology has the potential to improve the cost-effectiveness of the PV-powered microgrid Full article

(This article belongs to the Special Issue Strategies and Algorithms for Energy Management Optimization of Renewable Energy Based Microgrids)

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