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41 pages, 15850 KiB

Open AccessArticle

A Novel Prairie Dog-Based Meta-Heuristic Optimization Algorithm for Improved Control, Better Transient Response, and Power Quality Enhancement of Hybrid Microgrids

by Gagan Kumar Sahoo, Subhashree Choudhury, Rajkumar Singh Rathore and Mohit Bajaj

Sensors 2023, 23(13), 5973; https://doi.org/10.3390/s23135973 - 27 Jun 2023

Cited by 8 | Viewed by 1746

Abstract

The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient [...] Read more.

The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient controllers. In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to achieve effective compensation of load demand and mitigation of PQ problems. The MG system has been applied to various intentional PQ issues such as swell, unbalanced load, oscillatory transient, and notch conditions to study the response of the proposed PDO controller. For evaluating the efficacy of the proposed PDO algorithm, the simulation results obtained are compared with those of earlier popular methodologies utilized in the current literature such as bee colony optimization (BCO), thermal exchange optimization, and PI techniques. A detailed analysis of the results found emphasizes the efficiency, robustness, and potential of the suggested PDO controller in significantly improving the overall system operation by minimizing the THD, improving the control of active and reactive power, enhancing the power factor, lowering the voltage deviation, and keeping the terminal voltage, DC-link voltage, grid voltage, and grid current almost constant in the event of PQ fault occurrence. As a result, the proposed PDO method paves the way for real-time employment in the MG system. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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18 pages, 481 KiB

Open AccessArticle

Techno-Economic Comparison of Stationary Storage and Battery-Electric Buses for Mitigating Solar Intermittency

by Arif Ahmed and Tobias Massier

Sensors 2023, 23(2), 630; https://doi.org/10.3390/s23020630 - 05 Jan 2023

Cited by 2 | Viewed by 1424

Abstract

The need to reduce greenhouse gas emissions from power generation has led to more and more installation of renewable energies such as wind and solar power. However, the high intermittency of these generators poses a threat to electrical grid stability. The power output [...] Read more.

The need to reduce greenhouse gas emissions from power generation has led to more and more installation of renewable energies such as wind and solar power. However, the high intermittency of these generators poses a threat to electrical grid stability. The power output of solar photovoltaic (PV) installations, for instance, depends on the solar irradiance, and consequently on weather conditions. In order to mitigate the adverse effects of solar intermittency, storage such as batteries can be deployed. However, the cost of a stationary energy storage system (SESS) is high, particularly for large PV installations. Battery electric vehicles (BEVs) are an alternative to SESS. With increasing number of BEVs, more and more storage capacity becomes available while these vehicles are charging. In this paper, we compare stationary batteries to mobile batteries of battery electric buses (BEBs) in a public bus terminus for balancing fluctuations of solar PV installations. Public buses have been chosen due to their large batteries and because they are more easily manageable than private cars. An optimisation model has been developed considering both the bus operator’s and the PV operator’s objectives. Cycle ageing of batteries is included in the investigation. Our analysis reveals that utilising public BEBs with high battery capacity to balance solar PV fluctuations can present a positive financial case. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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18 pages, 9153 KiB

Open AccessArticle

DTTrans: PV Power Forecasting Using Delaunay Triangulation and TransGRU

by Keunju Song, Jaeik Jeong, Jong-Hee Moon, Seong-Chul Kwon and Hongseok Kim

Sensors 2023, 23(1), 144; https://doi.org/10.3390/s23010144 - 23 Dec 2022

Cited by 5 | Viewed by 1627

Abstract

In an era of high penetration of renewable energy, accurate photovoltaic (PV) power forecasting is crucial for balancing and scheduling power systems. However, PV power output has uncertainty since it depends on stochastic weather conditions. In this paper, we propose a novel short-term [...] Read more.

In an era of high penetration of renewable energy, accurate photovoltaic (PV) power forecasting is crucial for balancing and scheduling power systems. However, PV power output has uncertainty since it depends on stochastic weather conditions. In this paper, we propose a novel short-term PV forecasting technique using Delaunay triangulation, of which the vertices are three weather stations that enclose a target PV site. By leveraging a Transformer encoder and gated recurrent unit (GRU), the proposed TransGRU model is robust against weather forecast error as it learns feature representation from weather data. We construct a framework based on Delaunay triangulation and TransGRU and verify that the proposed framework shows a 7–15% improvement compared to other state-of-the-art methods in terms of the normalized mean absolute error. Moreover, we investigate the effect of PV aggregation for virtual power plants where errors can be compensated across PV sites. Our framework demonstrates 41–60% improvement when PV sites are aggregated and achieves as low as 3–4% of forecasting error on average. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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20 pages, 4494 KiB

Open AccessArticle

Demand Response Contextual Remuneration of Prosumers with Distributed Storage

by Cátia Silva, Pedro Faria, Bruno Ribeiro, Luís Gomes and Zita Vale

Sensors 2022, 22(22), 8877; https://doi.org/10.3390/s22228877 - 17 Nov 2022

Cited by 6 | Viewed by 1139

Abstract

Prosumers are emerging in the power and energy market to provide load flexibility to smooth the use of distributed generation. The volatile behavior increases the production prediction complexity, and the demand side must take a step forward to participate in demand response events [...] Read more.

Prosumers are emerging in the power and energy market to provide load flexibility to smooth the use of distributed generation. The volatile behavior increases the production prediction complexity, and the demand side must take a step forward to participate in demand response events triggered by a community manager. If balance is achieved, the participants should be compensated for the discomfort caused. The authors in this paper propose a methodology to optimally manage a community, with a focus on the remuneration of community members for the provided flexibility. Four approaches were compared and evaluated, considering contextual tariffs. The obtained results show that it was possible to improve the fairness of the remuneration, which is an incentive and compensation for the loss of comfort. The single fair remuneration approach was more beneficial to the community manager, since the total remuneration was lower than the remaining approaches (163.81 m.u. in case study 3). From the prosumers’ side, considering a clustering method was more advantageous, since higher remuneration was distributed for the flexibility provided (196.27 m.u. in case study 3). Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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15 pages, 315 KiB

Open AccessArticle

Optimal Reactive Power Compensation in Distribution Networks with Radial and Meshed Structures Using D-STATCOMs: A Mixed-Integer Convex Approach

by Víctor Manuel Garrido, Oscar Danilo Montoya, Ángeles Medina-Quesada and Jesus C. Hernández

Sensors 2022, 22(22), 8676; https://doi.org/10.3390/s22228676 - 10 Nov 2022

Cited by 6 | Viewed by 1603

Abstract

This paper deals with the problem regarding the optimal siting and sizing of distribution static compensators (D-STATCOMs) in electrical distribution networks to minimize the expected total annual operating costs. These costs are associated with the investments made in D-STATCOMs and expected energy losses [...] Read more.

This paper deals with the problem regarding the optimal siting and sizing of distribution static compensators (D-STATCOMs) in electrical distribution networks to minimize the expected total annual operating costs. These costs are associated with the investments made in D-STATCOMs and expected energy losses costs. To represent the electrical behavior of the distribution networks, a power flow formulation is used which includes voltages, currents, and power as variables via incidence matrix representation. This formulation generates a mixed-integer nonlinear programming (MINLP) model that accurately represents the studied problem. However, in light of the complexities involved in solving this MINLP model efficiently, this research proposes a mixed-integer convex reformulation. Numerical results regarding the final annual operating costs of the network demonstrate that the proposed mixed-integer convex model is efficient for selecting and locating D-STATCOMs in distribution networks, with the main advantage that it is applicable to radial and meshed distribution grid configurations. A comparative analysis with respect to metaheuristic optimizers and convex approximations confirms the robustness of the proposed formulation. All numerical validations were conducted in the MATLAB programming environment with our own scripts (in the case of metaheuristics) and the CVX convex disciplined tool via the Gurobi solver. In addition, the exact MINLP model is solved using the GAMS software. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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21 pages, 3583 KiB

Open AccessArticle

Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes

by Liu Fei, Muhammad Shahzad, Fazal Abbas, Hafiz Abdul Muqeet, Muhammad Majid Hussain and Li Bin

Sensors 2022, 22(19), 7448; https://doi.org/10.3390/s22197448 - 30 Sep 2022

Cited by 19 | Viewed by 2837

Abstract

In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered [...] Read more.

In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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20 pages, 2038 KiB

Open AccessArticle

Adaptive Load Forecasting Methodology Based on Generalized Additive Model with Automatic Variable Selection

by Sovjetka Krstonijević

Sensors 2022, 22(19), 7247; https://doi.org/10.3390/s22197247 - 24 Sep 2022

Cited by 1 | Viewed by 1400

Abstract

For decentralized energy management in a smart grid, there is a need for electric load forecasting at different places in the grid hierarchy and for different levels of aggregation. Load forecasting functionality relies on the load time series prediction model, which provides accurate [...] Read more.

For decentralized energy management in a smart grid, there is a need for electric load forecasting at different places in the grid hierarchy and for different levels of aggregation. Load forecasting functionality relies on the load time series prediction model, which provides accurate forecasts. Complex and heterogeneous multi-source load time series in a smart grid require flexible modeling approaches to meet the accuracy demand. This work proposes an adaptive load forecasting methodology based on the generalized additive model (GAM) with the big data estimation method. It is based on a set of GAM terms, constructed for a specific multi-source load forecasting application in the grid and a procedure that dynamically selects the most relevant terms and generates forecasts for particular load time series. Data from publicly available New York Independent System Operator (NYISO) databases are used for testing. The 24-hour-ahead forecasting results for eleven New York City zones, of different sizes and types, indicate the applicability of the proposed methodology. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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24 pages, 2893 KiB

Open AccessArticle

Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks

by Flaviu Turcu, Andrei Lazar, Vasile Rednic, Gabriel Rosca, Ciprian Zamfirescu and Emanuel Puschita

Sensors 2022, 22(16), 6259; https://doi.org/10.3390/s22166259 - 20 Aug 2022

Cited by 2 | Viewed by 1334

Abstract

Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction [...] Read more.

Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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21 pages, 5222 KiB

Open AccessArticle

Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis

by Li Bin, Muhammad Shahzad, Haseeb Javed, Hafiz Abdul Muqeet, Muhammad Naveed Akhter, Rehan Liaqat and Muhammad Majid Hussain

Sensors 2022, 22(16), 6150; https://doi.org/10.3390/s22166150 - 17 Aug 2022

Cited by 17 | Viewed by 2210

Abstract

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can [...] Read more.

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage. Full article

(This article belongs to the Special Issue Intelligent Energy Management in Smart Grids and Microgrids)

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