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26 pages, 1610 KiB

Open AccessArticle

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

by Felix Garcia-Torres, Ascension Zafra-Cabeza, Carlos Silva, Stephane Grieu, Tejaswinee Darure and Ana Estanqueiro

Energies 2021, 14(5), 1296; https://doi.org/10.3390/en14051296 - 26 Feb 2021

Cited by 45 | Viewed by 4933

Abstract

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key [...] Read more.

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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28 pages, 2467 KiB

Open AccessArticle

Using MPC to Balance Intermittent Wind and Solar Power with Hydro Power in Microgrids

by Madhusudhan Pandey, Dietmar Winkler, Roshan Sharma and Bernt Lie

Energies 2021, 14(4), 874; https://doi.org/10.3390/en14040874 - 07 Feb 2021

Cited by 6 | Viewed by 2691

Abstract

In a microgrid connected with both intermittent and dispatchable sources, intermittency caused by sources such as solar and wind power plants can be balanced by dispatching hydro power into the grid. Both intermittent generation and consumption are stochastic in nature, not known perfectly, [...] Read more.

In a microgrid connected with both intermittent and dispatchable sources, intermittency caused by sources such as solar and wind power plants can be balanced by dispatching hydro power into the grid. Both intermittent generation and consumption are stochastic in nature, not known perfectly, and require future prediction. The stochastic generation and consumption will cause the grid frequency to drift away from a required range. To improve performance, operation should be optimized over some horizon, with the added problem that intermittent power varies randomly into the future. Optimal management of dynamic system over a future horizon with disturbances is often posed as a Model Predictive Control (MPC) problem. In this paper, we have employed an MPC scheme for generating a hydro-turbine valve signal for dispatching necessary hydro power to the intermittent grid and maintaining grid frequency. Parameter sensitivity analysis shows that grid frequency is mostly sensitive to the turbine valve signal. We have found that controller discretization time, grid frequency, and power injection into the grid are interrelated, and play an important role in maintaining the grid frequency within the thresholds. Results also indicate that the fluctuations in grid frequency are insignificant on the turbine valve position during power injection into the grid. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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15 pages, 5063 KiB

Open AccessArticle

PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

by Mario Tovar, Miguel Robles and Felipe Rashid

Energies 2020, 13(24), 6512; https://doi.org/10.3390/en13246512 - 10 Dec 2020

Cited by 43 | Viewed by 5738

Abstract

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent [...] Read more.

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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24 pages, 3009 KiB

Open AccessArticle

Integration of a Multi-Stack Fuel Cell System in Microgrids: A Solution Based on Model Predictive Control

by Antonio José Calderón, Francisco José Vivas, Francisca Segura and José Manuel Andújar

Energies 2020, 13(18), 4924; https://doi.org/10.3390/en13184924 - 19 Sep 2020

Cited by 15 | Viewed by 2622

Abstract

This paper proposes a multi-objective model predictive control (MPC) designed for the power management of a multi-stack fuel cell (FC) system integrated into a renewable sources-based microgrid. The main advantage of MPC is the fact that it allows the current timeslot to be [...] Read more.

This paper proposes a multi-objective model predictive control (MPC) designed for the power management of a multi-stack fuel cell (FC) system integrated into a renewable sources-based microgrid. The main advantage of MPC is the fact that it allows the current timeslot to be optimized while taking future timeslots into account. The multi-objective function solves the problem related to the power dispatch at time that includes criteria to reduce the multi-stack FC degradation, operating and maintenance costs, as well as hydrogen consumption. Regarding the scientific literature, the novelty of this paper lies in the proposal of a generalized MPC controller for a multi-stack FC that can be used independently of the number of stacks that make it up. Although all the stacks that make up the modular FC system are identical, their levels of degradation, in general, will not be. Thus, over time, each stack can present a different behavior. Therefore, the power control strategy cannot be based on an equal distribution according to the nominal power of each stack. On the contrary, the control algorithm should take advantage of the characteristics of the multi-stack FC concept, distributing operation across all the stacks regarding their capacity to produce power/energy, and optimizing the overall performance. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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18 pages, 3630 KiB

Open AccessArticle

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

by Mohammed Olama, Jin Dong, Isha Sharma, Yaosuo Xue and Teja Kuruganti

Energies 2020, 13(18), 4593; https://doi.org/10.3390/en13184593 - 04 Sep 2020

Cited by 1 | Viewed by 1774

Abstract

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to [...] Read more.

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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23 pages, 2482 KiB

Open AccessArticle

A Stochastic MPC Based Energy Management System for Simultaneous Participation in Continuous and Discrete Prosumer-to-Prosumer Energy Markets

by Pablo Baez-Gonzalez, Felix Garcia-Torres, Miguel A. Ridao and Carlos Bordons

Energies 2020, 13(14), 3751; https://doi.org/10.3390/en13143751 - 21 Jul 2020

Cited by 3 | Viewed by 2218

Abstract

This article studies the exchange of self-produced renewable energy between prosumers (and with pure end consumers), through the discrete trading of energy packages and proposes a framework for optimizing this exchange. In order to mitigate the imbalances derived from discrepancies between production and [...] Read more.

This article studies the exchange of self-produced renewable energy between prosumers (and with pure end consumers), through the discrete trading of energy packages and proposes a framework for optimizing this exchange. In order to mitigate the imbalances derived from discrepancies between production and consumption and their respective forecasts, the simultaneous continuous trading of instantaneous power quotas is proposed, giving rise to a time-ahead market running in parallel with a real-time one. An energy management system (EMS) based on stochastic model predictive control (SMPC) simultaneously determines the optimal bidding strategies for both markets, as well as the optimal utilisation of any energy storage system (ESS). Simulations carried out for a heterogeneous group of agents show that those with SMPC-EMS achieve savings of between 3% and 15% in their energy operation economic result. The proposed structures allows the peer-to-peer (P2P) energy trading between end users without ESS and constitute a viable alternative to avoid deviation penalties in secondary regulation markets. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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20 pages, 2012 KiB

Open AccessFeature PaperArticle

Economic Management Based on Hybrid MPC for Microgrids: A Brazilian Energy Market Solution

by Eduardo Conte, Paulo R. C. Mendes and Julio E. Normey-Rico

Energies 2020, 13(13), 3508; https://doi.org/10.3390/en13133508 - 07 Jul 2020

Cited by 6 | Viewed by 2607

Abstract

This paper proposes a microgrid central controller (MGCC) solution to the energy management problem of a renewable energy-based microgrid (MG). This MG is a case study from the Brazilian energy market context and, thus, has some operational particularities and rules to be obeyed. [...] Read more.

This paper proposes a microgrid central controller (MGCC) solution to the energy management problem of a renewable energy-based microgrid (MG). This MG is a case study from the Brazilian energy market context and, thus, has some operational particularities and rules to be obeyed. The MGCC development was based on a hybrid model predictive control (HMPC) strategy using the mixed logical dynamic (MLD) approach to deal with logical constraints within the HMPC structure, which results in a mixed integer programming (MIP) problem. The development of the solution is done through economic and dynamic modeling of the MG components; furthermore, it also takes into account the energy compensation rules of the Brazilian energy market and the white energy tariff. These conditions are specified through a set of MLD constraints. The effectiveness and performance of the proposed solution are evaluated through high-fidelity numerical simulation. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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21 pages, 6334 KiB

Open AccessArticle

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

by Jura Arkhangelski, Pierluigi Siano, Abdou-Tankari Mahamadou and Gilles Lefebvre

Energies 2020, 13(7), 1764; https://doi.org/10.3390/en13071764 - 07 Apr 2020

Cited by 20 | Viewed by 2705

Abstract

In this paper, an innovative method for managing a smart-community microgrid (SCM) with a centralized electrical storage system (CESS) is proposed. The method consists of day-ahead optimal power flow (DA–OPF) for day-ahead SCM managing and its subsequent evaluation, considering forecast uncertainties. The DA–OPF [...] Read more.

In this paper, an innovative method for managing a smart-community microgrid (SCM) with a centralized electrical storage system (CESS) is proposed. The method consists of day-ahead optimal power flow (DA–OPF) for day-ahead SCM managing and its subsequent evaluation, considering forecast uncertainties. The DA–OPF is based on a data forecast system that uses a deep learning (DL) long short-term memory (LSTM) network. The OPF problem is formulated as a mathematical mixed-integer nonlinear programming (MINLP) model. Following this, the developed DA–OPF strategy was evaluated under possible operations, using a Monte Carlo simulation (MCS). The MCS allowed us to obtain potential deviations of forecasted data during possible day-ahead operations and to evaluate the impact of the data forecast errors on the SCM, and that of unit limitation and the emergence of critical situations. Simulation results on a real existing rural conventional community endowed with a centralized community renewable generation (CCRG) and CESS, confirmed the effectiveness of the proposed operation method. The economic analysis showed significant benefits and an electricity price reduction for the considered community if compared to a conventional distribution system, as well as the easy applicability of the proposed method due to the CESS and the developed operating systems. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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16 pages, 5876 KiB

Open AccessArticle

Efficient FPSoC Prototyping of FCS-MPC for Three-Phase Voltage Source Inverters

by Eduardo Zafra, Sergio Vazquez, Hipolito Guzman Miranda, Juan A. Sanchez, Abraham Marquez, Jose I. Leon and Leopoldo G. Franquelo

Energies 2020, 13(5), 1074; https://doi.org/10.3390/en13051074 - 01 Mar 2020

Cited by 14 | Viewed by 2720

Abstract

This work describes an efficient implementation in terms of computation time and resource usage in a Field-Programmable System-On-Chip (FPSoC) of a Finite Control Set Model Predictive Control (FCS-MPC) algorithm. As an example, the FCS-MPC implementation is used for the current reference tracking of [...] Read more.

This work describes an efficient implementation in terms of computation time and resource usage in a Field-Programmable System-On-Chip (FPSoC) of a Finite Control Set Model Predictive Control (FCS-MPC) algorithm. As an example, the FCS-MPC implementation is used for the current reference tracking of a two-level three-phase power converter. The proposed solution is an enabler for using both complex control algorithms and digital controllers for high switching frequency semiconductor technologies. An original HW/SW (hardware and software) system architecture for an FPSoC is designed to take advantage of a modern operating system, while removing time uncertainty in real-time software tasks, and exploiting dedicated FPGA fabric for the most complex computations. In addition, two different architectures for the FPGA-implemented functionality are proposed and compared in order to study the area-speed trade-off. Experimental results show the feasibility of the proposed implementation, which achieves a speed hundreds of times faster than the conventional Digital Signal Processor (DSP)-based control platform. Full article

(This article belongs to the Special Issue Model Predictive Control for Energy Management in Microgrids)

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