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The present work introduces a general and novel quality-of-experience-aware energy management system. The said system is designed to be responsible for supervising the operation of a smart house, where it accounts for both economic performance and demand management (DM) actions taking into account user comfort, and adopting a quality of experience (QoE) metric. Considering the availability of distributed generation (DG), smart houses are taken as hybrid nano-grids (NGs), and the Energy Management System (EMS) works as a Nano-Grid-Central Controller. Part of the energy storage in this NG is done using renewable hydrogen, which results in a reduction of pollutant emissions. A Model Predictive Control (MPC) algorithm is the foundation for the proposed smart-house EMS, and its formulation as a mixed-integer quadratic programming (MIQP) optimization problem is given, which avoids the use of nonlinear optimization tools. Validated by simulation, the system achieves the required standards: runs the smart house for a year with a 21% electricity bill reduction and 77% reduction in user discomfort. • A quality-of-experience-aware model predictive control energy management system-EMS. • The EMS accounts for economic performance, demand management and user comfort. • Part of the energy storage is done using renewable hydrogen, reducing emissions. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for achieving optimal energy management (OEM) in renewable-based hybrid microgrids (HMGs). The HMGs exhibit enhanced efficiency, minimizing power loss while maximizing generated power, making them advantageous over traditional MGs. This new approach consists of a modified model predictive control based improved Firefly1to3 algorithm (MMPC-IFA1to3). Here, MMPC adeptly regulates converter switching phenomena, ensuring stability during uncertain conditions in HMG. On the other hand, FA1to3 states that for each member movement of a firefly population, occurs towards three members. Here, the improved nature of FA1to3 is achieved by using the chaotic function to fine-tune the regulation parameter. This unified method generates the best power references for both active and reactive powers, making switching operations in voltage source converters simpler. This paper mainly emphasizes active and reactive power management through objective function minimization. The proposed IFA1to3 approach effectively incorporates constraints to minimize costs, ensure power availability, and mitigate voltage deviations in renewable-based HMGs. Moreover, charging/discharging of energy storage devices and power exchange between the utility grid and DC MG are carried out through the MMPC-IFA1to3 algorithm by monitoring active and reactive loads simultaneously. The corresponding converters are modeled based on the bidirectional power flow to cope with active and reactive power management. Further, the robustness of the proposed approach is verified under different operating conditions, and outcomes are compared with benchmark techniques in a MATLAB/Simulink environment to evidence its superior EM in HMG. Finally, the proposed MMPC-IFA1to3 approach is validated in a real-time environment through the dSPACE DS 1104 embedded processor to evidence its industrial applications through its robustness and OEM during various case studies. • In the hybrid MG, MPC regulates converter of the connected sources, while MMPC controls the VSC converter. • A novel FA, combining FA1to3 with chaotic logistic function, optimizes power references generation in this study. • Proposed MMPC-IFA1to3 approach ensures voltage stability and effectively manages ARP during uncertainties in MG. • The proposed IFA1to3 is more effective for multi-objective functions optimization. • Real-time validation of the proposed approach in dSPACE DS1104 evidences its practical applicability. [ABSTRACT FROM AUTHOR]

In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model. • Data-driven Koopman MPC for EVs under vehicle-following scenarios is proposed. • A Koopman prediction model is developed considering parameters such as slope. • The proposed method reduces energy consumption by 5.55%. [ABSTRACT FROM AUTHOR]

• A test-bench for PV-CHP hybrid system has been build. • Input profiles (PV and load) are emulated with accuracies between 0.1% and 14%. • The CHP and gas burner should have modulated power supply for higher efficiency. • Hardware and metering generates additional deviations from forecast schedules. • A battery needs a faster control added to the MPC for higher accuracy. In order to reduce the global warming to less than two degrees, a large increase in renewable energy resources like photovoltaic systems is necessary. A business case based on feed-in tariffs does not exist in every countries and self-consumption is limited due to the unsteady nature of solar radiation on earth. A combination with other systems such as batteries, heat-pumps or even combined heat and power plants can enhance the use of generated power by photovoltaic systems, particularly in private households and small businesses. Rule-based controllers and optimization algorithms (model predictive control) can both realise the efficient operation of a photovoltaic system in combination with storage systems and a combined heat and power plant. However, different controllers and energy management systems have hitherto only been compared theoretically. A comparison of such controllers in a real, controllable hardware environment has not yet been carried out. In this study, a test-bench is introduced to test different control algorithms for photovoltaic systems in combination with storage systems and a micro- combined heat and power plant. The operation has been tested for a one day period. Key performance parameters have been derived and compared for a rule-based control, an optimized control and simulation results including a forecast. The results show that the operational costs can be reduced by 7.3% for the chosen test-period using the optimized algorithm in the laboratory compared to the same system with a rule-based control. The results also indicate that even under perfect forecast conditions the hardware, metering and energy management cause latencies and inaccuracies leading to deviations, which are not accounted for in simulations. Hence, the accuracy of the forecast methods need not be higher than the deviations introduced by the hardware. These deviations often lead to unwanted charging and discharging events of the battery. A faster way of processing data and a second order or low level control is needed for short term reaction and higher efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive power management strategy for a novel anti-idling system, regenerative auxiliary power system (RAPS), designed for service vehicles. RAPS is able to utilize recovered braking energy for electrified auxiliary systems; this feature distinguishes it from its counterparts - auxiliary power unit (APU) and auxiliary battery powered unit (ABP). To efficiently operate the RAPS, a power management strategy is required to coordinate power flow between different energy sources. Thus, a model predictive controller (MPC) is developed to improve the overall efficiency of the RAPS. As an optimization-based approach, the MPC-based power management strategy usually requires the drive cycle or the drivers’ command to be known a priori. However, in this study, an average concept based MPC is developed without such knowledge. MPC parameters are tuned over an urban drive cycle; whereas, the robustness of this MPC is tested under different drive cycles (e.g. highway and combined). Analysis shows that, the presented MPC has a comparable performance as the prescient MPC regarding fuel consumption, which assumes knows the drive cycle beforehand. Meanwhile, with the help of the proposed MPC and RAPS, the service vehicle saves up to 9% of the total fuel consumption. The proposed MPC is independent of powertrain topology such that it can be directly extended to other types of hybrid electric vehicles (HEVs), and it provides a way to apply the MPC even though future driving information is unavailable. [ABSTRACT FROM AUTHOR]

• An MPC framework for space heating system with PCM-based active latent heat storage. • A linear, high-fidelity active latent heat storage model for fast building control. • Highly dynamic electricity prices considered for demand-responsive building control. • The MPC approach reduces electricity costs for space heating by 53.2% to 122.7% • A good synergy between the MPC approach and active latent heat storage is observed. Active latent heat storage (ALHS) involving phase-change materials constitutes a promising energy-efficient solution for building energy management (BEM) by reshaping building energy demands without occupant comfort degradation. Current BEM systems based on conventional reactive control lack the level of control delicacy required to exploit the full potential of ALHS for BEM under certain conditions, such as highly dynamic electricity prices. This study proposes a smart model predictive control (MPC) approach for BEM to minimize the energy cost while maintaining the indoor climate by fully applying ALHS. More specifically, a reduced-order, high-fidelity state-space model (SSM) of ALHS is proposed for fast building control. An MPC framework considering highly dynamic electricity prices and ALHS dynamics is developed based on the proposed ALHS SSM integrated with a building SSM. A case study entailing a set of simulations is designed based on a single-family house with a space heating system, including an ALHS, ground source heat pump, and radiator. The proposed MPC approach, compared to conventional reactive control, enables substantial reductions in the electricity cost (ranging from 53.2% to 122.7% depending on the MPC settings and ALHS capacity), even financial gains under certain scenarios. Further analysis reveals that coupling ALHS with MPC is critical to ensure that ALHS adoption is economically convincing: while conventional reactive control of an ALHS-equipped building increases the electricity cost, an MPC-enabled building could reduce the electricity cost by 45.1% due to ALHS adoption. The proposed MPC approach also exhibits promising feasibility for real-world BEM applications. [ABSTRACT FROM AUTHOR]

• Stochastic co-optimization of kinematic states and EMS for CAEVs is developed. • Dynamic probabilistic constraints ensuring driving safety are formulated. • A deep learning model predicting the PDF of preceding vehicle speed is constructed. • An efficient hierarchical solver for the co-optimization problem is designed. Ameliorating energy efficiency and enhancing driving safety are both extremely concerning issues for connected and automated electric vehicles (CAEVs) driving in a random traffic environment. To enhance driving safety and fully coordinate the potential conflict between driving safety and energy efficiency, an adaptive co-optimization method of speed planning and energy management strategy (EMS) with dynamic probabilistic constraints is proposed under the framework of stochastic model predictive control. The dynamic probabilistic constraints are enabled by the proposed composite sequence generation model, which predicts the future speed distribution of the preceding vehicle according to the probability relationship among future speed sequence, historical speed sequence, and macroscopic traffic state of downstream road segments, effectively modeling the macro and micro disturbance from random traffic environment and improving the prediction accuracy by about 10% (along with an over 57% decrease in distribution divergence) compared with pure sequence generation model. Comparison with existing co-optimization methods under the same car-following tasks validates the promising performance of the proposed adaptive co-optimization method, which produces dynamic feasible regions for kinematic states according to downstream traffic state and the driving state of the preceding vehicle, raising the driving safety by 14.81% and retaining the relatively high energy efficiency. [ABSTRACT FROM AUTHOR]

Home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment. This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment. • Uncertainty from weather and occupant forecasts impact home energy management. • A stochastic control framework is developed for a home energy management system. • A reduced-order linear house model is developed for model predictive control. • Incorporating uncertain forecasts reduces control performance in HEMS. • Stochastic MPC outperforms common deterministic and heuristic control methods. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to efficiently integrate long prediction horizons subject to uncertainty into a stochastic model predictive control (MPC) framework for the energy management of hybrid electric vehicles. By exploiting Pontryagin's minimum principle, we show that the energy supply required to obtain a certain change in the state of charge (SOC) of the battery can be approximated using a quadratic equation. The parameters of these mappings depend on the power request imposed by the driving mission and thus allow to compress the time-resolved power profile into only three scalar variables. Having a driving mission divided into several segments of arbitrary length, the corresponding sequence of quadratic approximations allows to reformulate the original energy management problem as a quadratic program, which offers an efficient way to include a large number of future scenarios. The resulting scenario-based stochastic MPC approach prevents SOC boundary violations with a certain probability, which can be controlled by the number of scenarios considered. To validate the quadratic approximation, we study two numerical examples using two different vehicles, a series hybrid electric passenger car and a battery-assisted trolley bus. Finally, a case study based on the operation of the latter is provided, which demonstrates the expected behavior and the real-time capability of the stochastic MPC approach. While the SOC is maintained in predefined boundaries with high probability, the required energy supply is only increased by 1.41% compared to the non-causal optimum. • New approach for long-horizon stochastic MPC for the energy management of HEVs. • Using Pontryagin's minimum principle and the scenario approach. • Numerical validations based on a series HEV passenger car and a hybrid trolley bus. • Case study demonstrating the performance and the realtime capability of the SMPC. • Only 1.4% more energy demand than optimum with low SOC violation probability. [Display omitted] [ABSTRACT FROM AUTHOR]

Buildings need advanced control for the efficient and climate-neutral use of their energy systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two powerful control techniques that have been extensively investigated in the literature for their application to building energy management. These methods show complementary qualities in terms of constraint satisfaction, computational demand, adaptability, and intelligibility, but usually a choice is made between both approaches. This paper compares both control approaches and proposes a novel algorithm called reinforced predictive control (RL-MPC) that merges their relative merits. First, the complementarity between RL and MPC is emphasized on a conceptual level by commenting on the main aspects of each method. Second, the RL-MPC algorithm is described that effectively combines features from each approach, namely state estimation, dynamic optimization, and learning. Finally, MPC, RL, and RL-MPC are implemented and evaluated in BOPTEST, a standardized simulation framework for the assessment of advanced control algorithms in buildings. The results indicate that pure RL cannot provide constraint satisfaction when using a control formulation equivalent to MPC and the same controller model for learning. The new RL-MPC algorithm can meet constraints and provide similar performance to MPC while enabling continuous learning and the possibility to deal with uncertain environments. • MPC and RL are compared and merged for the same building optimal control problem. • RL-MPC is an algorithm that combines methods from machine learning and control theory. • MPC, RL, and RL-MPC are evaluated and benchmarked in the BOPTEST simulation framework. • MPC effectively uses the controller model while pure RL violates the constraints. • RL-MPC enables learning and meets the constraints with similar performance to MPC. [ABSTRACT FROM AUTHOR]

• Speed prediction MPC is proposed for fuel cell electric vehicle energy management. • Fuel cell performance degradation cost is considered in the proposed MPC strategy. • Speed prediction influences on the energy management strategies are discussed. • Optimization strategies comparisons exhibit the effectiveness of the proposed MPC. • HIL test results verify the real-time performance of the proposed strategies. Due to the poor dynamic response ability of the fuel cell, the battery is normally applied to integrate with fuel cell to configure the hybrid power system in electric vehicles. In this paper, a vehicle speed prediction model predictive control (SP-MPC) energy management strategy is developed for the hybrid power system in fuel cell electric vehicles. The main principle of the proposed SP-MPC is that the future vehicle total power demand is forecasted via the Markov speed predictor and imported into the energy management system response prediction model to improve the control performance by more accurate disturbance description. The objective function is set for equivalent hydrogen consumption minimization and fuel cell degradation inhibition. As a contrast, the normal MPC strategy, the speed prediction dynamic programming (SP-DP) strategy and the DP offline strategy are formulated. Comparing with the normal MPC strategy, the SP-MPC strategy has a 3.74% reduction in the total operation cost under MANHATTAN condition. The SP-MPC strategy also has a 1.39% reduction in the total operation cost than the SP-DP strategy. Moreover, two scenarios are introduced with different disturbance prediction accuracy to verify the influences of the prediction inaccuracy on the SP-MPC and SP-DP results. For SP-DP strategy, the total operation cost under actual forecast scenario has increased by 5.03% compared with the perfect forecast scenario. The similar result can be seen in the SP-MPC, but the increase between perfect and actual forecast scenario is only 1.02%, which indicates a better robustness to the disturbance prediction inaccuracy compared with the SP-DP strategy. A DSP hardware in loop (HIL) test is conducted for real-time performance verification of the proposed SP-MPC. [ABSTRACT FROM AUTHOR]

The performance of model predictive control can be significantly affected by different choices of controller parameters such as the time intervals for model discretization and control sampling. Due to the lack of a systematic understanding on how these parameters affect the control performance, they are usually selected arbitrarily in practice. In this paper, the combined impacts of selected time intervals for model discretization and control sampling on the performance of model predictive control are comprehensively investigated for the first time through detailed simulations. The simulation results reveal that the time interval for model discretization has a much greater influence on the performance of model predictive control than that for control sampling by affecting the prediction performance, cost saving, and computation time simultaneously. In particular, there are three findings on the time interval for model discretization that are specific to the case studies considered herein. First, it affects the performance of multi-period prediction in a non-linear manner so that it is not always better to have a smaller time interval for model discretization. Second, it has four times greater influence on the resulting cost saving from model predictive control than the time interval for control sampling. Last, it affects the computation time significantly. The increase of the time interval for model discretization from 1 min to 5 min can reduce the computation time by roughly 96%. The proposed simulation-based performance evaluation sheds light on the importance of selecting appropriate time intervals for practical applications of model predictive control to building energy management. • Evaluated performance of model predictive control for building energy management. • Investigated combined impacts of model and control intervals on control performance. • Provided useful insights on selection of model and control intervals in practice. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical two-layer home energy management system to reduce daily household energy costs and maximize photovoltaic self-consumption. The upper layer comprises a model predictive controller which optimizes household energy usage using a mixed-integer linear programming optimization; the lower layer comprises a rule-based real-time controller, to determine the optimal power settings of the home battery storage system. The optimization process also includes load shifting and battery degradation costs. The upper layer determines the operating schedule for shiftable domestic appliances and the profile for energy storage for the next 24 h. This profile is then passed to the lower energy management layer, which compensates for the effects of forecast uncertainties and sample time resolution. The effectiveness of the proposed home energy management system is demonstrated by comparing its performance with a single-layer management system. For the same battery size, using the hierarchical two-layer home energy management system can achieve annual household energy payment reduction of 27.8% and photovoltaic self-consumption of 91.1% compared to using a single layer home energy management system. The results show the capability of the hierarchical home energy management system to reduce household utility bills and maximize photovoltaic self-consumption. Experimental studies on a laboratory-based house emulation rig demonstrate the feasibility of the proposed home energy management system. [ABSTRACT FROM AUTHOR]

• State space models are proposed for all-electric ships. • Novel predictive energy management and maneuvering control approaches are proposed. • Using the approaches, optimal engine loading is guaranteed. • The fuel efficiency increases by 2–15% depending on the operating profile. • Trajectory tracking performance is improved. Over the last few years, autonomous shipping has been under extensive investigation by the scientific community where the main focus has been on ship maneuvering control and not on the optimal use of energy sources. In this paper, the purpose is to bridge the gap between maneuvering control, energy management, and the control of the Power and Propulsion System (PPS) to improve fuel efficiency and the performance of the vessel. Maneuvering control, energy management, and the control of the PPS are in the literature typically studied independently from one another, while they are closely connected. A generic control methodology based on receding horizon control techniques is proposed for the ship maneuvering control as well as energy management. In the context of this research, Direct Current (DC) all-electric architectures are considered for the PPS where the relationship between the produced power by energy sources and vessel propellers is established by a DC microgrid. The objective of the proposed approach is to ensure the ship mission objectives by guaranteeing efficient power availability, decreasing the trajectory tracking error, and increasing the fuel efficiency. In this regard, for the ship motion control, a Model Predictive Control (MPC) algorithm is proposed which is based on Input–Output Feedback Linearization (IOFL). Through this algorithm, the required power for the ship mission is predicted and then, transferred to the proposed Predictive Energy Management (PEM) algorithm which decides on the optimal split between different on-board energy sources during the mission. As a result, the fuel efficiency and the power system stability can be increased. Several simulations are carried out for the evaluation of the proposed approach. The results suggest that by adopting the proposed approach, the trajectory tracking error decreases and the Specific Fuel Consumption (SFC) efficiency is significantly improved. [ABSTRACT FROM AUTHOR]