Rule-based Control Strategy Research Articles

Potential quantification and impact factors analysis of energy flexibility in residential buildings with preheating control strategies

Demand response (DR) is an effective method for achieving energy flexibility. Preheating can store a certain amount of energy in the building while simultaneously reducing the heat load. The energy flexibility of a building during the heating season mainly depends on the types of DR events, the thermal performance of the building envelope, and weather conditions. Analyzing the impact of these factors on the preheating performance and exploring their interactions can reveal the potential for energy flexibility. This study proposes a simulation-based method to evaluate the energy flexibility of a residential building in Kitakyushu, Japan from the perspectives of energy efficiency, load and energy reduction. Rule-based control strategies are applied to heating, ventilation, and air conditioning systems. The results show that compared to preheating temperature of 24 °C, high insulation building at 25 °C achieves energy efficiency improvement of 3.5%–14.8% and load reduction increase of 1.6%–22.2%. Compared to low insulation building, preheating at 25 °C for 1 h increases energy efficiency in high insulation building by 6.2%–10.3%, with corresponding load reduction increase of 1.0%–19.1%. Extending the preheating duration improves load reduction but leads to heat loss. Preheating during heating system startup should be avoided as it produces the worst energy flexibility. Moreover, statistical analysis identifies key indicators for energy reduction: the inside surface temperature of the exterior wall (Pearson correlation coefficient = 0.820, standard regression coefficient = 0.924), followed by global horizontal radiation and building insulation performance.

An Economic Velocity Planning Strategy Based on Driving Style and Improved Dynamic Programming for a Hybrid Electric Truck

The power coupling equation and energy consumption model for enhancing the fuel economy and power performance of plug-in hybrid electric trucks (PHETs) are proposed based on the economic velocity planning strategy (EVPS-DSIDP), which takes into account the driving style and an improved dynamic programming (IDP) algorithm. This strategy employs a fuzzy controller to identify the driving style, and optimizes the efficiency and accuracy of the conventional dynamic programming (DP) algorithm by associating decision variables, dynamically adjusting the discretization step size, and restricting the state space. Additionally, a penalty function is introduced to enhance the robustness of the DP algorithm. Under our EVPS-DSIDP, the variation of velocity is liberated from the constraints of fixed driving conditions, and directly correlates with road information and driving styles, which is of significant importance for addressing energy management issues in real-time traffic conditions. Moreover, the proposed IDP algorithm can improve computational efficiency while ensuring calculation accuracy, thereby greatly enhancing the potential for the practical application of this algorithm in real-world vehicle scenarios. The simulation results demonstrate that compared to the rule-based control strategy, the application of the proposed EVPS-DSIDP in the economy velocity planning strategy can achieve an average reduction of 2.88% in economic costs and 10.6% in travel time across different driving styles. This approach offers a more comprehensive optimization of both fuel economy and power performance.

Application of deep learning techniques to minimize the cost of operation of a hybrid solar-biomass system in a multi-family building

Concerns related to climate change put renewable energy at the centre of most of the policies aimed at achieving a deep decarbonisation of the building sector. The combined use of two or more renewable energy sources in the same energy system can lead to an increase in the total share of renewable energy and in the flexibility of the system. In this direction, the SolBio-Rev project aims to develop an innovative system that uses solar thermal collectors and a biomass boiler to meet energy demand in buildings in different climatic regions. An advanced control that used deep reinforcement learning techniques was considered in this paper to find an optimal control strategy for a specific SolBio-Rev system installed in a standard multi-family residential building located in Madrid. The advanced control was developed to minimize the total cost of operation of the system. The results indicated that the advanced control strategy achieved a cost reduction of 35% in winter, compared to a standard rule-based control strategy. However, the improved control was not able to produce a significant cost reduction in summer.

Model predictive control of air-based building integrated PV/T systems for optimal HVAC integration

High performance of HVAC connected Building Integrated Photovoltaic/Thermal (BIPV/T) systems relies on appropriate control. However, optimal control is often overlooked, resulting in systems that operate inefficiently. This paper investigates how model predictive control (MPC) can improve the operation of open loop air-based BIPV/T systems connected to multiple thermal applications.The BIPV/T system at the first institutional net-zero energy building in Canada, the Varennes library, is used as an archetype. The BIPV/T covers 16% of the south-facing roof and operates under a simple rule-based control strategy. The developed control and design strategies consider variations of this system, to achieve higher thermal utilization efficiency.A control-oriented BIPV/T model is developed and calibrated using monitored data. The BIPV/T airflow is regulated through MPC for simultaneous heat supply to an Energy Recovery Ventilator and air-to-water heat pump. The BIPV/T air flow is efficiently controlled, considering the connected thermal applications, environmental conditions, and PV temperature. Model-based control for BIPV/T systems can increase the amount of useful heat and reduce PV overheating. The MPC controller for the examined system reduced the building energy consumption compared to the business-as-usual operation by 40% and together with increased BIPV/T area can supply excess heat to adjacent buildings.

Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio

The innovation and development of energy management strategies attract more and more attention as a key technology in hybrid electric vehicles. This paper focuses on a novel type of electric-hydraulic hybrid vehicle with multiple working modes and zero emissions, which offers a deeper potential for energy efficiency. Steady-state simulation with a rule-based mode switching strategy verifies that the electric-hydraulic ratio in the hybrid driving mode can interfere with energy management performance. Committed to filling the literature gap on the electric-hydraulic ratio, this paper proposes the idea of combining deep reinforcement learning with a rule-based control strategy and employs the Twin Delayed Deep Deterministic Policy Gradient to control the electric-hydraulic ratio. Thereafter, an energy management strategy framework based on the self-adaptive electric-hydraulic ratio was developed. Offline training and online test can demonstrate that the proposed energy management strategy enables the self-adaptive electric-hydraulic ratio under various driving cycles and a significant reduction in the energy consumption rate. The research findings in this paper are the crystallization of traditional control strategy and advanced algorithm, which has stronger practical value and development prospects. This is the first time that the self-adaptive electric-hydraulic ratio is applied to the establishment of energy management strategies.

Modeling, Simulation and Control Strategy Optimization of Fuel Cell Hybrid Electric Vehicle

This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others.

Grouped Charging of Decentralised Storage to Efficiently Control Collective Heating Systems: Limitations and Opportunities

Collective heating systems have multiple end-users with time-varying, often different temperature demands. There are several concepts catering to this, e.g., multi-pipe networks and 2-pipe networks with or without decentralised booster systems. In this study, we focus on 2-pipe networks with a changing supply temperature by smart use of decentralised storage. By grouping high-temperature demands, the average supply temperature can be lowered during large parts of the day, which is beneficial for system efficiency. The actual energy-saving potential, however, can be case-specific and is expected to depend on design choices and implemented control strategies. In this paper, these dependencies are assessed and identified by implementing two optimised rule-based control strategies, providing in such a way a bench-mark for other control strategies. The results show that grouping yields energy savings of up to 36% at similar peak demand as with conventional control strategies. The energy-saving potential is greatest for large storage volumes and small networks, but large networks with large storage and proper control choices can also achieve around 30% energy savings. Moreover, high-temperature time can easily be reduced to less than 40% of the day, which could make space cooling without decentralised booster heat pumps possible, but this requires further research.

Assessment of Battery–Supercapacitor Topologies of an Electric Vehicle under Real Driving Conditions

Road transport is shifting towards electrified vehicle solutions to achieve the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP27) carbon neutrality target. According to life cycle assessment analyses, battery production and disposal phases suffer a not-negligible environmental impact to be mitigated with new recycling processes, battery technology, and life-extending techniques. The foundation of this study consists of combining the assessment of vehicle efficiency and battery ageing by applying supercapacitor technology with different topologies to more conventional battery modules. The method employed here consists of analysing different hybrid energy storage system (HESS) topologies for light-duty vehicle applications over a wide range of operating conditions, including real driving cycles. A battery electric vehicle (BEV) has been modelled and validated for this aim, and the reference energy storage system was hybridised with a supercapacitor. Two HESSs with passive and semi-active topologies have been analysed and compared, and an empirical ageing model has been implemented. A rule-based control strategy has been used for the semi-active topology to manage the power split between the battery and supercapacitor. The results demonstrate that the HESS reduced the battery pack root mean square current by up to 45%, slightly improving the battery ageing. The semi-active topology performed sensibly better than the passive one, especially for small supercapacitor sizes, at the expense of more complex control strategies.

Powertrain modeling and performance simulation of a novel flywheel hybrid electric vehicle

To improve vehicle performance and energy utilization, a novel planetary gear set based flywheel hybrid electric powertrain (PGS-FHEP) is proposed. The PGS-FHEP involves an internal combustion engine, a planetary gear set that integrated a control motor and an energy storage flywheel, which combines the high efficiency of the mechanical flywheel energy storage system with the flexible and controllable characteristics of the electric motor. The powertrain is analyzed and modeled using lever analogy method, and a rule-based control strategy is designed and verified under different test cycles. The simulation results indicate that compared with the traditional manual transmission vehicle, the fuel economy of the vehicle equipped with PGS-FHEP can be improved by more than 50%, and the acceleration performance can be increased by 28.01%. Up to 60.61% of vehicle kinetic energy can be recovered by PGS-FHEP, among which 37.85% can be directly captured by the energy storage flywheel. In addition, the battery charging power is reduced, which is beneficial to prolong the battery life.

Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria

Microgrids can integrate variable renewable energy sources into the energy system by controlling flexible assets locally. However, as the energy system is dynamic, an effective microgrid controller must be able to receive feedback from the system in real-time, plan ahead and take into account the active electricity tariff, to maximize the benefits to the operator. These requirements motivate the use of optimization-based control methods, such as Model Predictive Control to optimally dispatch flexible assets in microgrids. However, the major bottleneck to achieve maximum benefits with these methods is their predictive accuracy. This paper addresses this bottleneck by developing a novel multi-step forecasting method for a Model Predictive Control framework. The presented methods are applied to a real test-bed of a renewable energy community in Austria, where its operational costs and CO2 emissions are benchmarked with those of a rule-based control strategy for Flat, Time-of-Use, Demand Charge and variable energy price tariffs. In addition, the impact of forecast errors and electric battery capacity on energy community operational savings are examined. The key results indicate that the proposed controller can outperform a rule-based dispatch strategy by 24.7% in operational costs and by 8.4% in CO2 emissions through optimal operation of flexibilities if it has perfect foresight. However, if the controller is deployed in a realistic environment, where forecasts for electrical load and PV generation are required, the same savings are reduced to 3.3% for cost and 7.3% for CO2, respectively. In such environments, the proposed controller performs best in highly dynamic tariffs such as Time-of-Use and Real-time pricing rates, achieving real cost savings of up to 6.3%. These results show that the profitability of optimization-based control of microgrids is threatened by forecast errors. This motivates future research on control strategies that compensate for forecast errors in real-world operation and more accurate forecasting methods.

Model predictive control of stormwater basins coupled with real-time data assimilation enhances flood and pollution control under uncertainty

Smart stormwater systems equipped with real-time controls are transforming urban drainage management by enhancing the flood control and water treatment potential of previously static infrastructure. Real-time control of detention basins, for instance, has been shown to improve contaminant removal by increasing hydraulic retention times while also reducing downstream flood risk. However, to date, few studies have explored optimal real-time control strategies for achieving both water quality and flood control targets. This study advances a new model predictive control (MPC) algorithm for stormwater detention ponds that determines the outlet valve control schedule needed to maximize pollutant removal and minimize flooding using forecasts of the incoming pollutograph and hydrograph. Comparing MPC against three rule-based control strategies, MPC is found to be more effective at balancing between multiple competing control objectives such as preventing overflows, reducing peak discharges, and improving water quality. Moreover, when paired with an online data assimilation scheme based on Extended Kalman Filtering (EKF), MPC is found to be robust to uncertainty in both pollutograph forecasts and water quality measurements. By providing an integrated control strategy that optimizes both water quality and quantity goals while remaining robust to uncertainty in hydrologic and pollutant dynamics, this study paves the way for real-world smart stormwater systems that will achieve improved flood and nonpoint source pollution management.

Optimal Power Flows Control for Home Energy Management With Renewable Energy and Energy Storage Systems

This article presents a home energy management system (HEMS) based on the proposed adaptive artificial bee colony (AABC) optimization method, of managing a residential microgrid connected to the main grid. The studied microgrid consists of a photovoltaic system (PVS), a battery storage system (BSS), and electricity loads. The proposed AABC-based HEMS optimizes power flow ratio among PVS, BSS, and the main grid with respect to the load demand and electricity rate in order to minimize electricity consumption cost. Three dc-dc converters are actively controlled with an active dual-loop control architecture to dominate the power distribution among multi-power sources. The performances of the proposed optimal power flows control (PFC) under different electricity load levels and PVS generation conditions are examined. In the PFC experiments, the designed HEMS is implemented on a digital signal processor. The experimental results demonstrate the effectiveness and superiority of the proposed AABC-based HEMS compared with the rule-based control strategy and the equivalent consumption minimization strategy in terms of electricity consumption cost.

Research on Parasitic Power of Cooling Balance of Plant System for Proton Exchange Membrane Fuel Cell

AbstractIn high-power systems of proton exchange membrane fuel cells (PEMFC), cooling systems for the balance of plants (BOP) play an extremely important role in maintaining the temperature of the key components of the fuel cell system. To evaluate the effect of the PEMFC BOP cooling system on the fuel cell system efficiency, a Simulink model of the fuel cell system and an AMEsim model of the cooling system for the BOP system are established based on experimental data. A co-simulation is conducted based on the established models to determine the effects of fuel cell stack output power, coolant flowrate, radiator fan speed, and temperature control strategies on the parasitic power consumption and fuel cell system efficiency. The simulation results show that with an increase in the stack output power, coolant flowrate, and radiator fan speed, the parasitic power of the BOP cooling system increases and the system efficiency of PEMFC decreases. With an increase in the opening temperature of the radiator fan, the parasitic power of the BOP cooling system decreases and the system efficiency of the PEMFC increases. Compared with the rule-based control strategy, the radiator fan speed control strategy based on the PID controller achieves lower parasitic power. The research presented in this paper is helpful for further development of efficient fuel cell vehicle thermal management system.

AlphaHydrogen: A virtual platform for simulating and evaluating station-based regional hydrogen-electricity networks with distributed renewables, buildings, and fuel-cell vehicles

Building-transportation integrated regional energy networks powered by hydrogen (H2) and electricity has attracted increasing research attention due to its capability to address the urgent challenge of climate change. However, modeling and optimizing H2-electricity networks are difficult due to the inter-disciplinary nature and the lack of convenient and compatible simulation approaches. In this study, an innovative open-sourced virtual platform, AlphaHydrogen, is developed for fast, accurate, and convenient simulation of regional H2-electricity networks constituted by distributed renewable generation, buildings, fuel-cell vehicles, and a H2 station. We first introduced the development of AlphaHydrogen. Then, we demonstrated how to use AlphaHydrogen to simulate a microgrid with 30 houses, rooftop photovoltaic panels, 30 fuel-cell vehicles, and a H2 station with an electrolyzer, an H2 tank, and an onsite fuel cell. We compared the simulation accuracy of AlphaHydrogen with a widely used commercial-licensed software (TRNSYS), and then demonstrated how AlphaHydrogen can be used to optimize the design and operation of a complicated microgrid energy system. Our numerical validation indicates that the simulation differences between AlphaHydrogen and TRNSYS are smaller than 5% in terms of annual energy amounts and grid powers. The optimal sizing of on-site renewable generation and the most suitable rule-based control strategy for the above microgrid system have been discussed using AlphaHydrogen. We believe AlphaHydrogen can promote the application and optimization of H2-electricity hybrid energy system by providing an accurate, open-sourced, compatible, extensible, and easy-to-use virtual testbed.

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. Test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle

The configuration and development of energy management strategies (EMSs) are generating considerable interest regarding vehicles due to the rapid blossoming momentum of electric vehicles. Battery state of charge is one of the main characterization parameters for evaluating EMSs, so the practical advancement of the range is critical to the evolution of electric vehicles. A novel electric-hydraulic hybrid electric vehicle (EHHEV) is investigated in this paper, which has the characteristics of various working modes and multi-energy sources. According to the hybrid system's energy flow, a rule-based control strategy is established, and the superiority of EHHEV in energy management is verified by steady-state simulation. Further, this paper combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided EMS to solve traditional control strategy and reinforcement learning issues. After appropriate hyperparameters setting and batch training, the EMS can make EHHEV realize the optimal switching among working modes. Experimental results showcase that the EMS can significantly enhance vehicle economy. This is the first of its kind to apply the DDQN to developing EMS for EHHEV.

An optimal-pressure control strategy for hydrostatic wheel loader

Mobile machines consume a large amount of fossil fuel each year; therefore, it is critical to reduce their fuel consumption. A wheel loader is a typical mobile machine of great research interest. Most compact wheel loaders are usually equipped with hydrostatic transmissions. To reduce the fuel consumption of the powertrain, a min-rpm strategy where the engine speed is maintained as low as possible is usually applied. It is a rule-based control strategy, however, cannot achieve minimum engine fuel consumption. Therefore, an optimal-pressure control strategy is proposed for hydrostatic wheel loaders, where the minimum fuel consumption is achieved by selecting optimal system pressure at each time step. To show the advantages of the proposed strategy, the powertrain operation and engine fuel consumption are compared with the min-rpm strategy through simulation studies. To further verify the effectiveness of the proposed strategy, comparisons are conducted using three loading cycles (30s, 57s and 90s). Results show the engine fuel savings in three loading cycles, with fuel saving up to 27% in the 90s loading cycle.

Mixed-Integer Real-Time Control of a Building Energy Supply System

We present a methodology based on mixed-integer nonlinear model predictive control for a real-time building energy management system in application to a single-family house with a combined heat and power (CHP) unit. The developed strategy successfully deals with the switching behavior of the system components as well as minimum admissible operating time constraints by use of a special switch-cost-aware rounding procedure. The quality of the presented solution is evaluated in comparison to the globally optimal dynamic programming method and conventional rule-based control strategy. Based on a real-world scenario, we show that our approach is more than real-time capable while maintaining high correspondence with the globally optimal solution. We achieve an average optimality gap of 2.5% compared to 20% for a conventional control approach, and are faster and more scalable than a dynamic programming approach.

A rule-based energy management strategy for a low-temperature solar/wind-driven heating system optimized by the machine learning-assisted grey wolf approach

This work presents an innovative, practical, and cost-effective solution for advancing state-of-the-art intelligent building energy systems and aiding the intended worldwide green transition with maximum renewable integration. The vanadium chloride cycle, electrolyzer unit, and Alkaline fuel cell are powered by the sun's and wind's energy to produce/store/use hydrogen. A rule-based control scheme is designed to provide a sophisticated interplay between the demand/supply sides, components, and local energy networks to reduce peak capacity, lower emissions, and save energy costs. TRNSYS is used to analyze and compare the techno-economic-environmental indicators of the conventional system and the suggested smart model for a multi-family building in Sweden. A grey wolf method is built in MATLAB with the help of machine learning to determine the optimum operating state with the maximum accuracy and the least amount of computational time. The results reveal that the suggested smart model considerably saves energy and money compared to the conventional system in Sweden while lowering CO2 emissions. According to the optimization results, the grey wolf optimizer and machine learning techniques enable greater total efficiency of 13 %, higher CO2 mitigation of 8 %, a larger cost saving of 38 %, and a reduced levelized energy cost of 41 $/MWh. The scatter distribution of important design parameters shows that altering the fuel cell current and electrode area considerably impacts the system's performance from all angles. The bidirectional connection of the proposed smart system with the heating and electrical networks through the rule-based controller demonstrates that it can supply the building's energy requirements for more than 300 days of the year. Eventually, the major contribution of the vanadium chloride cycle in the summer and the electrolyzer in the winter to the creation of hydrogen highlights the significance of renewable hybridization in reducing the dependence of buildings on energy networks.

A hybrid machine learning-assisted optimization and rule-based energy monitoring of a green concept based on low-temperature heating and high-temperature cooling system

This article aims to support the targeted worldwide green transition process by introducing and thoroughly analyzing a low-temperature heating and high-temperature cooling, smart building system. This concept allows for greater use of renewable energy while utilizing less input energy than conventional heating and cooling techniques. The proposed system consists of a reversible water-to-water heat pump driven by low-temperature geothermal energy. A rule-based control strategy is developed to establish an intelligent connection with the regional energy grids for peak shaving and compensating for the building's energy costs over the year. The dynamic simulation is carried out for a multi-family building complex in Stockholm, Sweden, using TRNSYS. The most favorable operating condition is determined via an artificial neural network-assisted tri-objective optimizer based on the grey wolf algorithm in MATLAB. The comparison of the proposed smart model with the conventional system in Sweden results in 332%, 203%, and 190% primary energy reduction, cost saving, and carbon dioxide emission mitigation, respectively. As indicated by the parametric results, the conflicting fluctuation between desirable and unfavorable indicators highlights the importance of multi-objective optimization. The grey wolf optimizer obtains 12% higher efficiency, 1.2 MWh lower annual bought energy, 24 $/MWh lower unit cost, and 5.1 MWh more yearly sold energy than the design condition. The scattered distribution reveals that tank volume and subcooling degree are sensitive parameters. According to the transient results, the suggested smart system can independently satisfy the building's heating, cooling, and electricity demands for more than 81% of the year, thanks to the two-way connection with the electricity and heating networks via the rule-based controller.