Real-time Optimal Control Strategy Research Articles

Minimum decision of collision injury based on drivable area in unavoidable scene

The existing technology for assisted driving collision avoidance has improved the active safety of vehicles, but it is primarily designed for scenarios where collisions can be avoided. It is not suitable for situations where pedestrians suddenly intrude. Therefore, there is a need to develop a control strategy that can minimize collision injuries in unavoidable situations. This study focuses on typical collision scenarios where pedestrians suddenly intrude. It proposes a real-time online double-layer optimal pre-collision control strategy based on drivable area, using the Head Injury Criteria ( HIC) as a quantifiable measure of collision injury. The upper-layer controller designs a cost function based on a three Degrees of Freedom (DOF) collision solving model to minimize collision injuries. It takes the constraints of vehicle dynamics, maximum front wheel steering angle, pedestrian avoidance into account, and the optimal pre-impact state point with the minimum HIC value is calculated in real-time. Meanwhile, the lower-layer controller is designed to track the optimal pre-impact state point using Model Predict Control (MPC) algorithm. The effectiveness of the proposed control strategy is verified through Simulink simulation, achieving the dual objectives of avoiding pedestrians suddenly intruding and minimizing passenger collision injuries.

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

The precision motor losses-based real-time optimal control method for air-conditioning system considering energy saving and thermal comfort

The power consumption of air-conditioning (AC) systems is often miscalculated due to the impact of multiple motor systems, leading to difficulties in system control and significant energy waste. In this work, we propose a precision motor losses-based real-time optimal control (PMLRTOC) strategy for AC systems, which considers fully the unique characteristics of pump load and variable frequency speed regulation. The proposed strategy offers an innovative solution to this issue, integrating constraints on the water supply temperature, the temperature difference between supply and return water, and indoor thermal comfort. The control strategy is applied and examined in the context of a 90-floor, Grade A mega office building in Hong Kong. The findings demonstrate that the proposed mathematical model implemented through the optimal control strategy is able to reduce energy consumption by 18.2% at low load rates and 23.9% at high load rates. Compared to a control strategy based on a constant temperature difference, a strategy employing variable temperature difference can further reduce energy consumption by 13.7%. Furthermore, most parameters required for the PMLRTOC strategy are fixed and can be readily obtained from equipment manuals, which enhances the practicality of the model. Therefore, this work offers a widely applicable optimal control model for various AC systems, accelerating energy savings in public buildings and contributing to the achievement of carbon peak and neutrality targets.

Voltage Optimization Control and Risk Assessment Technology for High-proportion Distributed Photovoltaic Access to Distribution Network

The high proportion of distributed photovoltaic connected to the distribution network will lead to an increase in terminal voltage and network loss, which will not only generate many adverse effects on the safe and reliable operation of the distribution network but also easily lead to power supply disconnection and power supply interruption accidents caused by voltage out of limit. At present, the voltage control of the distribution network does not fully consider the regulation characteristics of the photovoltaic inverter itself and ignores the impact of different control methods on the distribution network security risk management and control. Therefore, this paper establishes a voltage closed-loop control model for high-proportion distributed photovoltaic access to the distribution network and proposes a real-time active voltage optimization control strategy with the goal of minimizing the deviation of the actual operation state of the distribution network from the scheduling plan. Finally, a typical industrial park is taken as a case for calculation and analysis. The results show that using real-time data to formulate a real-time voltage optimization control strategy can not only ensure the economic operation of each voltage regulating unit in the distribution network but also reduce operational risks. The control method proposed in this paper provides a new idea for improving the voltage control level of distributed photovoltaic access distribution network and fully guarantees the operation reliability of the entire distribution network.

Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings

Model predictive control (MPC) method shows superiority in enhancing system performance. But its actual performance in operation is hampered by forecast uncertainties. Different methods have been proposed to mitigate the impacts of these uncertainties, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC). However, the year-round performance of these methods has rarely been investigated, and little is known about their relative performance, particularly in utilization of building flexibility-resources. In this study, the year-round performance of conventional MPC (CMPC), SHMPC and SMPC strategies when used to optimize utilization of flexibility-resources in buildings with PV power generation and battery storage systems are compared using real-time optimal control (RTC) as the benchmark, and their enhancement is ultimately proposed. Forecast uncertainties are quantified based on real meteorological data. Different optimization horizon start times and shrinking intervals are tested. Results show that there is no one control strategy which is superior to the other strategies under all operating conditions. The SMPC strategy has a higher probability of achieving daily cost saving than CMPC and SHMPC strategies, but still has higher daily costs than the RTC strategy in the winter. Hybrid control strategies with proper control schemes implemented under different operating conditions are therefore recommended.

Comparative Assessment of Alternative Mpc Strategies Using Real Meteorological Data and Their Enhancement for Optimal Utilization of Flexibility-Resources in Buildings

Model predictive control (MPC) method shows superiority in enhancing system performance. But its actual performance in operation is hampered by forecast uncertainties. Different methods have been proposed to mitigate the impacts of these uncertainties, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC). However, the year-round performance of these methods has rarely been investigated, and there is little knowledge on their relative performance, particularly in optimal utilization of flexibility-resources in buildings. In this study, the year-round performance of conventional MPC (CMPC), SHMPC and SMPC strategies when used to optimize utilization of flexibility-resources in buildings are compared using real-time optimal control (RTC) as the benchmark, and their enhancement is ultimately proposed. Forecast uncertainties are quantified based on real meteorological data. Different optimization horizon start times and shrinking intervals are tested. Results show that there is no one control strategy which is superior to the other strategies under all operating conditions. The SMPC strategy has a higher probability of achieving daily cost saving than CMPC and SHMPC strategies, but still has higher daily costs than the RTC strategy in the winter. Hybrid control strategies with proper control schemes implemented under different operating conditions are therefore recommended.

A Two-Layer Real-Time Optimization Control Strategy for Integrated Battery Thermal Management and HVAC System in Connected and Automated HEVs

With the increase in the capacity and power rating of batteries in today's HEVs and EVs, the battery thermal management (BTM) system bears growing importance in vehicle safety and efficiency. A practical BTM system is commonly coupled with the passenger cabin heating-ventilation-air-conditioning (HVAC) system, which makes a major energy consumer and a challenging control object. Thanks to the connected and automated vehicle (CAV) technology, predictions of the vehicle speed profile and power trajectory can be obtained, providing the possibility for predictive control of the BTM and HVAC coupled system in order to maintain battery safety, passenger comfort, and to reduce energy consumption. However, the tradeoff among higher energy saving potential and wider control range from a long and sparse horizon, and higher accuracy from a short and dense horizon is inevitable in the conventional predictive control. In this paper, a two-layer predictive control strategy for warm/hot weather is proposed to address the aforementioned tradeoff. The upper layer controller firstly plans the optimized battery temperature trajectory according to the look-ahead speed preview and battery power profiles provided by the CAV network and the integrated BTM and HVAC system efficiency surface from off-line data. Then the lower layer model predictive controller tracks the planned trajectory and cabin temperature reference while enforcing the energy consumption optimization. Simulation results demonstrate that the proposed strategy exhibits accurate battery and passenger cabin temperature reference tracking while achieving up to 10.47% HVAC energy saving comparing to a baseline control strategy with UDDS profile and typical driving conditions. Implementation of the proposed strategy on a real-time vehicle emulator based on rapid control prototyping (RCP) and hardware-in-the-loop (HIL) platforms demonstrates the real-world implementation capability of the proposed two-layer framework.

Mechanism Analysis and Real-time Control of Energy Storage Based Grid Power Oscillation Damping: A Soft Actor-Critic Approach

In this paper, the mechanism of energy storage (ES)-based power oscillation damping is derived by the small signal and the classical electric torque method. And then, by cooperating PI with an integral reduction loop, a controller is designed to form a novel PI-IR controller to guarantee that the energy variation of ES damper is zero at the end of one oscillation. Furthermore, for the controller parameters tuning, the conventional model-based methods require a forecasting model on the uncertainty disturbances. To this end, this problem is formulated as a finite Markov decision process with unknown transition probability, and introduce a deep reinforcement learning (DRL) based model-free agent, the soft actor-critic, to obtain the real-time optimal control strategy. After numerous training, the well-trained agent can act as an experienced decision maker to provide the real-time near-optimal parameters setting for PI-IR control under different operating conditions. Time-domain and eigenvalue analysis results demonstrate the effectiveness of the proposed PI-IR controller and the superiority of the employed DRL based model-free method.

A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method

Determining the proper trade-off among thermal comfort, Indoor Air Quality (IAQ) and energy use is important for optimal control of air-conditioning systems. The number of optimization variables increases as systems become increasingly complex, as with multi-zone VAV (Variable Air Volume) air-conditioning systems, leading to large-scale mathematics programming challenges and inconveniences in the implementation of conventional centralized optimization strategies. This paper therefore proposes a real-time optimal control strategy adopting a multi-agent based distributed optimization method for multi-zone VAV air-conditioning systems. The proposed strategy consists of three novel schemes. First, a temperature set-point reset scheme adopts a linear rule to correlate the resetting of the temperature set-points in individual zones to simplify the optimization problem while applying proper optimization in individual zones. Second, a multi-objective optimization scheme optimizes the fresh air ratio of the supply air and the temperature set-point in the critical zone by formulating the multi-objective optimization problem. Third, a multi-agent distributed optimization scheme is developed to solve the optimization problem in a distributed manner, facilitating the deployment of local control devices of limited capacity. A TRNSYS-MATLAB co-simulation testbed is constructed to test and validate the proposed strategy. Test results show that the strategy is effective in properly balancing thermal comfort, IAQ and energy use while largely reducing programming challenges. The distributed optimization method can provide almost the same optimal outputs as conventional centralized optimization methods.

Real-Time Energy-Efficient Actuation of Induction Motor Drives Using Approximate Dynamic Programming

This article proposes a real-time control strategy for induction motor drives, harmonizing accurate torque, and flux tracking with energy efficiency. Common problems related to lengthy control horizons and the associated computational burden are dealt with by introducing a value function approximation in the model-predictive structure to quantify the impact of instantaneous decisions on future states. An augmented model of the drive is introduced to determine this value function approximation offline. The resulting optimization problem is implemented in a real-time environment and its effectiveness is established on an experimental setup for both steady state and dynamic load conditions. Experiments show that the proposed approach outperforms default model-based methods by up to 89.7%, thus making it a promising real-time optimal control strategy in the domain of electric drives.

Real-Time Optimal Control Strategy for Multienergy Complementary Microgrid System Based on Double-Layer Nondominated Sorting Genetic Algorithm

Because of the problems of low operation efficiency and poor energy management of multienergy input and output system with complex load demand and energy supply, this paper uses the double-layer nondominated sorting genetic algorithm to optimize the multienergy complementary microgrid system in real-time, allocating reasonably the output of each energy supply end and reducing the energy consumption of the system on the premise of meeting the demand of cooling, thermal and power load, so as to improve the economy of the whole system. According to the system load demand and operation mode, the first layer of this double-layer operation strategy calculates the power required by each node of the microgrid system to reduce the system loss. The second layer calculates the output of each equipment by using nondominated sorting genetic algorithm with various energy values calculated in the first layer as constraint conditions, considering the operation characteristics of various equipment and aiming at economy and environmental protection. In this paper, a typical model of energy input-output is established. This model combines with the operation control strategy suitable for multienergy complementary microgrid system, considers the operation mode and equipment characteristics of the system, and uses a double-layer nondominated sorting genetic algorithm to optimize the operation of each equipment in the multienergy complementary system in real time, so as to reduce the operation cost of the system.

Adaptive regression model-based real-time optimal control of central air-conditioning systems

Model-based, real-time optimal control is an effective tool to improve the energy efficiency of central air-conditioning systems. However, its performance relies heavily on the accuracy of the system models, whereas the development of accurate models for central air-conditioning systems is not easy due to their complex dynamics and non-linearities. This study presents an adaptive regression model-based real-time optimal control strategy for central air-conditioning systems. In the proposed strategy, regression models are adopted to describe the relationship between the power consumption of the system and the variables that are optimised. Their simple structures enable a low computation load for model updating and real-time optimisation. The length of the training data (for model updating) is investigated, and a suitable length is found using a similarity check-based method. Case studies were carried out to assess the performance of the proposed strategy, and they demonstrated that (1) a week was the optimal length of the training data for the case system, (2) the proposed strategy saved energy use by 3.48–10.59% when compared with a benchmark system with no optimisation, and (3) the proposed method reduced the computational load by 85% when compared with a simplified physical model-based optimal control without adaptive modelling.

An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks

Existing studies on distributed optimal control of HVAC systems rarely consider the needs and constraints for practical applications and the deployment of control strategies on the physical building automation platforms. This paper proposes an agent-based distributed real-time control strategy for building HVAC systems with the objective to be deployed in the local control devices with limited programming and computation capacities, i.e. smart sensors integrated in future IoT-based field networks and local controllers in field networks of current LAN-based building automation systems. A complex optimization task with high computational complexity (i.e., computation code and computation load) is decomposed into a number of simple tasks, which can be handled by coordinating agents among the integrated local control devices. The computation task of an optimization decision is further distributed into a number of steps, each performed at a sampling interval of controllers. The test results show that the computation loads of all individual agents at each step were below 2000 FLOPs, which can be handled by the typical smart sensors using simple optimization codes and the number of iterations for each optimization decision was within 50, well below the convergence rate needed for optimal control with typical time interval of minutes. The proposed agent-based optimal control strategy is also convenient and effective to deal with multiple components of different performances and the optimization considering such performance deviations could reduce the overall energy consumption significantly.

Investigation of integrated uninterrupted dual input transmission and hybrid energy storage system for electric vehicles

Transportation sector is one of the major sources of pollutants as it contributes more than 80% of CO, while almost all HC and 90% of NOx and PM. Although battery electric vehicles are well-known for reducing environmental pollution, the driving range and battery lifespan put a significant barrier to its large-scale commercialization. In this study, an integrated system, which includes an uninterrupted dual input transmission and hybrid energy storage system, is proposed to improve energy efficiency and extend battery lifespan. Given the limitations of dynamic programming in practice, a real-time optimal control strategy is designed to evaluate the power loss and battery capacity degradation of the proposed integrated system based on detailed mathematical models of individual powertrain components. To achieve a desirable trade-off between battery degradation, energy consumption, and acquisition cost, a mixed-integer multi-objective genetic algorithm is implemented to optimize the parameters of the hybrid energy storage system, while Pareto principal is adopted to find the proper solution according to different purposes. The simulation results reveal that the proposed integrated system shows the potential of saving 15.85%–20.82% of the energy consumption in typical driving cycles and more than 22.61%–31.11% Life-cycle cost compared with the single-ratio transmission-based battery electric vehicles. The selected Pareto front can further enhance Life-cycle cost from 26.53% to 28.13% in the HWFET cycle. It can be concluded that the integrated uninterrupted dual input transmission and hybrid energy storage system not only can improve motor efficiency and reduce energy consumption, it also can extend the battery lifespan to decrease Life-cycle cost compared to conventional single-ratio battery-only EV.

Real-Time Optimization Energy Management Strategy for Fuel Cell Hybrid Ships Considering Power Sources Degradation

In order to protect the marine environment, the fuel cell has become a good candidate for the main energy source of marine vehicles due to its remarkable performance of high efficiency, quietness, and cleanness. However, the fuel cell of the hybrid power system has a critical drawback of short lifetime. For this reason, the development of the energy management strategy considering power sources degradation becomes significant and urgent. In addition, the power system configuration has a strong link with the control strategy. In this vein, a sizing method of the hybrid energy storage system for a fuel cell hybrid excursion ferry is proposed to prolong the fuel cell lifetime. Based on the configuration of the power system, a real-time optimization control strategy considering efficiency and durability is developed. To verify the control strategy, the mathematical model of the hybrid power system is established. Compared with the wavelet-based control strategy and rule-based control strategy, the proposed strategy has better performance in terms of the fuel economy, the system durability as well as the dynamic property.

Application Conditions of Bounded Rationality and a Microgrid Energy Management Control Strategy Combining Real-Time Power Price and Demand-Side Response

Microgrid energy management is a typical discrete non-linear optimization problem that is usually solved by off-line optimization, day-ahead demand-side management, and long-term dynamic optimization scheduling strategy. However, due to the intermittent distributed generation and time-varying load in microgrids, more attention should be paid to the real-time optimal scheduling of the overall operation of energy to ensure the dynamic balance of supply and demand in microgrids. Combining demand-side response with real-time power price, this paper applies the strategy to microgrid energy management and proposes a distributed energy real-time management model of microgrid based on demand-side response function. A deep adaptive dynamic programming optimization algorithm is also proposed for the model. The real-time interaction between microgrid operators and users is realized. The closed-loop feedback control structure of the proposed model ensures the real-time optimization control strategy. Therefore, the proposed energy management model and control strategy can realize intra-day dispatching in microgrids. The real-time performance and effectiveness of the proposed energy management model and control strategy are also verified by numerical simulation. Finally, since the proposed model is approximate, whether the solution obtained by the algorithm is the optimal or satisfactory solution of the optimization strategy set is a lack of theoretical support. Therefore, according to the approximation theorem of bounded rationality, the application conditions of the model in power markets are proposed. It is proved that the proposed model meets the application conditions, and is a specific application of bounded rationality approaching complete rationality in the power market. It is also proved that the best solution is involved in the satisfactory solution set of the model. Thus, the control strategy is a rational and feasible optimal management control strategy, which provides a theoretical basis for its further implementation.

Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms

This study is aimed at developing a real-time optimal control strategy for variable air volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using genetic algorithms and a simulated large-scale office building. The two selected control variables are the settings for the supply air temperature and the duct static pressure to provide optimal control for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal control settings for each control variable. The proposed optimal control conditions were evaluated according to the total energy consumption of the HVAC system based on its component parts (fan, chiller, and cold-water pump). The results confirm that the supply air temperature and duct static pressure change according to the cooling load of the simulated building. Using the proposed optimal control variables, the total energy consumption of the building was reduced up to 5.72% compared to under ‘normal’ settings and conditions.

Deep learning adaptive dynamic programming for real time energy management and control strategy of micro-grid

With the widespread application of distributed renewable energy in power systems, energy management problems in micro-grids have become increasingly significant. Offline or static optimization methods are frequently employed to solve the problems which are typically discrete and nonlinear. There are few online optimization methods and they are not only complex but also normally consider the distributed generations and loads in micro-grid as a whole. The outcome is that the online methods fail to reflect the composing characteristic of distributed multi-energy and the contribution made by developing renewable energy to reduce the consumption of traditional fossil energy. Moreover, results obtained by either the offline or the on-line methods can deviate to certain extents from the true values. This paper treats the management of distributed energy in micro-grids as an optimal control problem. Using the system control theory, a framework of real-time management of distributed energy in micro-grids is proposed. A deep learning adaptive dynamic programming is proposed for this framework. Due to the introduction of the concept of closed-loop feedback, the proposed management and control strategy is a real-time algorithm. Furthermore, the accuracy of managing and controlling the objective function can be improved. The gap of the flexible load after the optimization can be helpful in terms of guiding the flexible load consumers to change their habits of energy consumption, thereby reducing the coal-fired power generation and providing room for reducing carbon emissions. Because it is real-time, micro-grid operators can also realize intra-day scheduling, which can be done through combining this optimized management and control strategy with the mechanism of energy operating and managing. Moreover, some data sets of the real-time optimal control strategy are obtained in this paper. These intuitive and accurate data sets can be used to further optimize energy operating and managing of micro-grids, thereby realizing effective energy management for micro-grids. Finally, the real-time and effectiveness of the proposed management and control strategy are proved by the simulations. Some positive conclusions are drawn.

Parameter optimization of plug-in hybrid electric vehicle based on quantum genetic algorithm

The parameters of the power system and the control strategy parameters are coupled,they work together to affect the power, economy and emission of the plug-in hybrid electric vehicle. Firstly, the real-time optimal control strategy based on the optimum of the vehicle power system’s efficiency is developed. Secondly, the cost of the power system and indicators of vehicle’s power performance are taken as constraints, the fuel consumption and emissions considering the engine’s cold-heat effect are taken as the optimization objective, and in order to obtain the optimum solution of the power system’s parameters and control strategy parameters, the standard genetic algorithm (SGA) and the quantum genetic algorithm (QGA) are applied to coordinated optimize them respectively. Finally, the simulation model of the vehicle system is established. After simulation, the simulation results show that compared with the original vehicle, the fuel economy and emission performance of the vehicle which parameters optimized by SGA and QGA has greatly improved, compared to the parameters optimization by SGA, the optimization by QGA can suppress premature convergence and have better fuel economy and emission performance.

Distributed Real-Time Optimal Power Flow Control in Smart Grid

Conventionally, power system has a hierarchical control structure including primary, secondary, and tertiary controls. The drawbacks of this hierarchical scheme are manifest: 1) it lacks flexibility and scalability, which is against the trend toward an open-access power system; 2) load forecast as the basis of tertiary control could be inaccurate and infeasible, especially in microgrid for example; 3) as the penetration of renewable energy increases, the relatively long time-scales of secondary and tertiary controls cannot accommodate to more severe power fluctuation within the system. To avoid these drawbacks, a distributed real-time optimal power flow control strategy is introduced in this paper. With the aid of up-to-date smart grid technologies such as two-way communication and distributed sensor, the proposed approach can avoid the need of load forecast and achieve the same objective as hierarchical control with a feedback mechanism in real time, that is to recover the nominal system frequency and maintain the active power of the generators close to the optimal operational condition in the presence of any disturbance. Convergence of the proposed approach is analytically proved. Simulation results in a 34-bus islanded microgrid and the IEEE 118-bus bulk power grid validate the effectiveness and efficiency of the proposed approach.