Recently, model predictive control (MPC) methods have been widely used to achieve the control of two-level voltage source inverters due to their superiorities. However, only one of the eight basic voltage vectors is applied in every control cycle in the conventional MPC system, resulting in large current ripples and distortions. To address this issue, a dual-vector modulated MPC method is presented, where two voltage vectors are selected and utilized to control the voltage source inverter in every control cycle. The duty cycle of each voltage vector is figured out according to the hypothesis that it is inversely proportional to the square root of its corresponding cost function value, which is the first contribution of this paper. The effectiveness of this assumption is verified for the first time by a detailed theoretical analysis shown in this paper based on the geometrical relationship of the voltage vectors, which is another contribution of this paper. Moreover, further theoretical... [more]

The absorption process of CO2 by ethanolamine solution is essentially a dynamic system, which is greatly affected by the power plant startup and flue gas load changes. Hence, studying the optimal control of the CO2 chemical capture process has always been an important part in academic fields. Model predictive control (MPC) is a very effective control strategy used for such process, but the most intractable problem is the lack of accurate and effective model. In this work, Aspen Plus and Aspen Plus Dynamics are used to establish the process of monoethanolamine (MEA) absorption of CO2 related models based on subspace identification. The nonlinear distribution of the system under steady-state operation is analyzed. Dynamic tests were carried out to understand the dynamic characteristics of the system under variable operating conditions. Systematic subspace identification on open-loop experimental data was performed. We designed a model predictive controller based on the identified model c... [more]

In this work, the optimal management of the water grid belonging to a pilot agro-industrial district, based on greenhouse cultivation, is analyzed. Different water supply plants are considered in the district, some of them using renewable energies as power sources, i.e., a solar thermal desalination plant and a nanofiltration facility powered up by a photovoltaic field. Moreover, the trade with the water public utility network is also taken into account. As demanding agents, a greenhouse and an office building are contemplated. Due to the different water necessities, demand profiles, and the heterogeneous nature of the different plants considered as supplier agents, the management of the whole plant is not trivial. In this way, an algorithm based on the energy hubs approach, which takes into account economic terms and the optimal use of the available resources in its formulation, is proposed for the pilot district with a cropping area of 616 m2. Simulation results are provided in order... [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 with... [more]

Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyze... [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.

Nowadays, uninterruptible power supplies (UPS) play an important role in feeding critical loads in the electric power systems such as data centers or large communication hubs. Due to the increasing power of these loads and frequent need for expansion or redundancy, UPS systems are frequently connected in parallel. However, when UPS systems are parallel-connected, two fundamental requirements must be verified: potential circulating currents between the systems must be eliminated and the load power must be distributed between the systems according to UPS systems availability. Moreover, a high-quality load voltage waveform must be permanently ensured. In this paper innovative control strategies are proposed for paralleled UPS systems based on Finite Control Set Model Predictive Control (FCS-MPC). The proposed strategies simultaneously provide: controlled load power distribution, circulating current suppression and a high-quality load voltage waveform. A new dynamic converters deactivation... [more]

Home energy management systems (HEMS) are a key technology for managing future electricity distribution systems as they can shift household electricity usage away from peak consumption times and can reduce the amount of local generation penetrating into the wider distribution system. In doing this they can also provide significant cost savings to domestic electricity users. This paper studies a HEMS which minimizes the daily energy costs, reduces energy lost to the utility, and improves photovoltaic (PV) self-consumption by controlling a home battery storage system (HBSS). The study assesses factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policy. Two management strategies have been used to control the HBSS; (1) a HEMS based on a real-time controller (RTC) and (2) a HEMS based on a model predictive controller (MPC). Several methods have been developed for home demand energy forecasting and PV generation forecasting and their impact o... [more]

A problem of the switched reluctance drive is its natural torque pulsations, which are partially solved with finite control set model predictive control strategies. However, the continuous control set model predictive control, required for precise torque stabilization and predictable power converter behavior, needs sufficient computation resources, thus limiting its practical implementation. The proposed model predictive control strategy utilizes offline processing of the magnetization surface of the switched reluctance motor. This helps to obtain precalculated current references for each torque command and rotor angular position in the offline mode. In online mode, the model predictive control strategy implements the current commands using the magnetization surface for fast evaluation of the required voltage command for the power converter. The proposed strategy needs only two lookup table operations requiring very small computation time, making instant execution of the whole control... [more]

The implementation of model predictive controls (MPCs) in buildings represents an important opportunity to reduce energy consumption and to apply demand side management strategies. In order to be effective, the MPC should be provided with an accurate model that is able to forecast the actual building energy demand. To this aim, in this paper, a data-driven model realized with an artificial neural network is compared to a physical-based resistance−capacitance (RC) network in an operative MPC. The MPC was designed to minimize the total cost for the thermal demand requirements by unlocking the energy flexibility in the building envelope, on the basis of price signals. Although both models allow energy cost savings (about 16% compared to a standard set-point control), a deterioration in the prediction performance is observed when the models actually operate in the controller (the root mean square error, RMSE, for the air zone prediction is about 1 °C). However, a difference in the on-time... [more]

The tuning of weighting factor has been considered as the most challenging task in the implementation of multi-objective model predictive control (MPC) techniques. Thus, this paper proposes an artificial intelligence (AI)-based weighting factor autotuning in the design of a finite control set MPC (FCS-MPC) applied to a grid-tied seven-level packed U-cell (PUC7) multilevel inverter (MLI). The studied topology is capable of producing a seven-level output voltage waveform and inject sinusoidal current to the grid with high power quality while using a reduced number of components. The proposed cost function optimization algorithm ensures auto-adjustment of the weighting factor to guarantee low injected grid current total harmonic distortion (THD) at different power ratings while balancing the capacitor voltage. The optimal weighting factor value is selected at each sampling time to guarantee a stable operation of the PUC inverter with high power quality. The weighting factor selection is p... [more]

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this paper, we present the “Solar+ Optimizer” (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area net... [more]

The diffusion of distributed energy resources in distribution networks requires new approaches to exploit the users’ capabilities of providing ancillary services. Of particular interest will be the coordination of microgrids operating as an aggregate of demand and supply units. This work reports a model predictive control (MPC) application in microgrids for the efficient energy management of energy storage systems and photovoltaic units. The MPC minimizes the economic cost of aggregate prosumers into a prediction horizon by forecasting generation and absorption profiles. The MPC is compared in realistic conditions with a heuristic strategy that acts in a instant manner, without taking into account signals prediction. The work aims at investigating the effect that different types of energy tariffs have in enhancing the end-users’ flexibility, based on three examples of currently applied tariffs, comparing the two storage control modes. The MPC always achieves a better solution than the... [more]

Distributed generation systems are emerging as a good solution as part of the response to the world’s growing energy demand. In this context multi-phase wind generation systems are a feasible option. These systems consist of renewable AC sources which requires efficient and controlled power conversion stages. This work proposes a novel predictive current control strategy that takes advantage of a multi-modular matrix converter topology in the power stage of a six-phase generation system. The proposed method uses a coupling signal between the modules to decrease the error and the total harmonic distortion compared to independent control of each module. Experimental results validate the new control strategy showing the improvement regarding the target parameters.

In the last few years, one of the most important challenges of power technologies has been the integration of traditional energy production systems and distributed energy resources. Large-scale photovoltaic systems and wind farms may decrease the quality of the electrical grid service, mainly due to voltage and frequency peaks and fluctuations. Besides, new functionalities, such as the operation in islanded mode of some portions of the medium-voltage grid, are more and more required. In this respect, a model predictive control for voltage and frequency regulation in interconnected local distribution systems is presented. In the proposed model, each local system represents a collection of intelligent buildings and microgrids with a large capacity in active and reactive power regulation. The related model formalization includes a linear approximation of the power flow equations, based on stochastic variables related to the electrical load and to the production from renewable sources. A m... [more]

Microgrids need control and management at different levels to allow the inclusion of renewable energy sources. In this paper, a comprehensive literature review is presented to analyse the latest trends in research and development referring to the applications of predictive control in microgrids. As a result of this review, it was found that the application of predictive control techniques on microgrids is performed for the three control levels and with adaptations of the models in order to include uncertainties to improve their performance and dynamics response. In addition, to ensure system stability, but also, at higher control levels, coordinated operation among the microgrid’s components and synchronised and optimised operation with utility grids and electric power markets. Predictive control appears as a very promising control scheme with several advantages for microgrid applications of different control levels.

This paper addresses the energy management of a standalone renewable energy system. The system is configured as a microgrid, including photovoltaic generation, a lead-acid battery as a short term energy storage system, hydrogen production, and several loads. In this microgrid, an energy management strategy has been incorporated that pursues several objectives. On the one hand, it aims to minimize the amount of energy cycled in the battery, in order to reduce the associated losses and battery size. On the other hand, it seeks to take advantage of the long-term surplus energy, producing hydrogen and extracting it from the system, to be used in a fuel cell hybrid electric vehicle. A crucial factor in this approach is to accommodate the energy consumption to the energy demand and to achieve this, a model predictive control (MPC) scheme is proposed. In this context, proper models for solar estimation, hydrogen production, and battery energy storage will be presented. Moreover, the controlle... [more]

Complexity of online computation is a drawback of model predictive control (MPC) when applied to the Navier−Stokes equations. To reduce the computational complexity, we propose a method to approximate the MPC with an explicit control law by using regression analysis. In this paper, we extracted two state-feedback control laws and two output-feedback control laws for flow around a cylinder as a benchmark. The state-feedback control laws that feed back different quantities to each other were extracted by ridge regression, and the two output-feedback control laws, whose measurement output is the surface pressure, were extracted by ridge regression and Gaussian process regression. In numerical simulations, the state-feedback control laws were able to suppress vortex shedding almost completely. While the output-feedback control laws could not suppress vortex shedding completely, they moderately improved the drag of the cylinder. Moreover, we confirmed that these control laws have some degre... [more]

In the paper a comparative study of the two control structures based on MPC (Model Predictive Control) for an electrical drive system with an induction motor are presented. As opposed to the classical approach, in which DFOC (Direct Field Oriented Control) with four controllers is considered, in the current study only one MPC controller is utilized. The proposed control structures have a cascade free structure that consists of a vector of electromagnetic (torque, flux) and mechanical (speed) states of the system. The first investigated framework is based on the finite-set MPC. A short horizon predictive window is selected. The continuous set MPC is used in the second framework. In this case the predictive horizon contains several samples. The computational complexity of the algorithm is reduced by applying its explicit version. Different implementation aspects of both MPC structures, for instance the model used in prediction, complexity of the control algorithms, and their properties t... [more]

Improvements in internal combustion engine efficiency can be achieved with proper thermal management. In this work, a simulation tool for the preliminary analysis of the engine cooling control is developed and a model-based controller, which enforces the coolant flow rate by means of an electrically driven pump is presented. The controller optimizes the coolant flow rate under each engine operating condition to guarantee that the engine temperatures and the coolant boiling levels are kept inside prescribed constraints, which guarantees efficient and safe engine operation. The methodology is validated at the experimental test rig. Several control strategies are analyzed during a standard homologation cycle and a comparison of the proposed methodology and the adoption of the standard belt-driven pump is provided. The results show that, according to the control strategy requirements, a fuel consumption reduction of up to about 8% with respect to the traditional cooling system can be achie... [more]

Energy Management System (EMS) optimal strategies have shown great potential to match the fluctuating energy production from renewables with an electric demand profile, which opens the way to a deeper penetration of renewable energy sources (RES) into the electric system. At a single building level, however, handling of different energy sources to fulfill both thermal and electric requirements is still a challenging task. The present work describes the potential of an EMS based on Model Predictive Control (MPC) strategies to both maximize the RES exploitation and serve as an ancillary service for the grid when a Heat Pump (HP) coupled with a Thermal Energy Storage (TES) is used in a residential Hybrid Renewable Energy System (HRES). Cost savings up to 30% as well as a reduction of the purchased energy unbalance with the grid (about 15%−20% depending on the season) have been achieved. Moreover, the thermal energy storage leads to a more efficient and reliable use of the Heat Pump by gen... [more]

In applications such as multiphase motor drives, classical predictive control strategies are characterized by a variable switching frequency which adds high harmonic content and ripple in the stator currents. This paper proposes a model predictive current control adding a modulation stage based on a switching pattern with the aim of generating a fixed switching frequency. Hence, the proposed controller takes into account the prediction of the two adjacent active vectors and null vector in the ( α - β ) frame defined by space vector modulation in order to reduce the (x-y) currents according to a defined cost function at each sampling period. Both simulation and experimental tests for a six-phase induction motor drive are provided and compared to the classical predictive control to validate the feasibility of the proposed control strategy.

The urgent need to reduce greenhouse gas emissions to achieve a decarbonized society has led to the active introduction of electric vehicles worldwide. Renewable energy sources that do not emit greenhouse gases during charging must also be used. However, the uncertainty in the supply of renewable energy is an issue that needs to be considered in practical applications. Therefore, in this study, we predicted the amount of electricity generated by renewable energy using model predictive control, and we considered the operation of a complete island-operated park and ride EV parking station that does not depend on commercial electricity. To perform appropriate model predictive control, we performed comparative simulations for several different forecast interval cases. Based on the obtained results, we determined the forecast horizon and we simulated the economic impact of implementing EV demand response on the electricity demand side. We found that without demand response, large amounts of... [more]

As a bi-directional converter, the Buck-Boost converter, which has the advantages of simple structure and taking the SOC of the battery as the balance variable, is adopted as the balance topology in this paper. In view of the shortcomings of traditional balance topology, which can only balance two adjacent batteries, resulting in a long balance time and insufficient balance accuracy, a cascade active balance charging topology that can balance in intra-group and inter-group situations simultaneously is proposed. At the same time, the fuzzy control algorithm and model predictive control are used as the balance control strategies, respectively, to control whether the MOSFET is on or off in the balance topology circuit. The duty cycle is dynamically adjusted to the size of the balance current to achieve the balance of the battery pack. The results show that the cascade Buck-Boost balance topology based on model prediction control can accurately control the balancing current and improve the... [more]

This work considers the DC part of a hybrid AC/DC microgrid with a meshed topology. We address cost minimization, battery scheduling and the power loss minimization within the power distribution network through constrained optimization. The novelty comes from applying differential flatness properties to the microgrid components and formulating the cost and constraints in terms of the associated B-splines parametrization of the flat outputs (the voltages and currents of the system). This allows us to obtain optimal power profiles to minimize the power dissipation and the cost of the electricity purchase from the external grid. These profiles are tracked by a model predictive controller at the higher level, while at a a lower level a controller deals with the operation of the switches within the DC/DC converters. Extensive simulations under nominal and fault-affected scenarios using realistic data validate the proposed approach.