Energy-efficient Operation Strategy Research Articles

Time-efficient Design of Energy-efficient Train Operation Strategy with Multi-fidelity Train Simulators

Time-efficient Design of Energy-efficient Train Operation Strategy with Multi-fidelity Train Simulators

Hybrid model-free control based on deep reinforcement learning: An energy-efficient operation strategy for HVAC systems

At present, most optimization objectives of heating, ventilation, and air-conditioning (HVAC) systems focus on the local optimization of equipment during cooling hours, usually ignoring the importance of the end conditions. In addition, traditional control methods may not perform well in complex or dynamic systems. Therefore, in this study, a hybrid model-free control (HMFC) strategy was developed that combines deep reinforcement learning (DRL) with L-BFGS-B and expert knowledge to improve its ability to cope with complex system environments. This strategy was used to optimize the cooling and heating periods of a groundwater source heat pump system in a bitterly cold region of China throughout the year, considering the end-of-system conditions. L-BFGS-B optimized the fresh-air ratios for the end air-handling unit, whereas DRL achieved global optimization of the heat pump outlet temperature, air-conditioning water circulation pump frequency, and submersible-pump frequency. To verify the effectiveness of the strategy, a simulation platform was built based on actual data and device parameters, and the simulation results of HMFC and model predictive control (MPC) were output, compared, and analyzed with data of the system measured in 2022 under expert rule-based manual control (RBC). The results show that HMFC was 7.38 % and 9.38 % more energy efficient than RBC during heating hours, respectively. HMFC was 24.26 % more energy efficient than RBC and 10.03 % more energy efficient than MPC during cooling hours. Moreover, the distribution of the system coefficient of performance under the HMFC method was more concentrated in the higher range, indicating that the proposed HMFC can save energy savings. Thus, it is a feasible optimization scheme for buildings without a large number of historical data. Finally, the strategy was applied to a real engineering experimental analysis, and the engineering practice results show that the strategy is robust and practical.

Autonomous real-time control for membrane capacitive deionization

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy. To fulfill the objectives, we established three long-short term memory models to predict applied voltage, outflow pH, and outflow electrical conductivity. Also, four RL agents were trained to minimize outflow concentration and energy consumption simultaneously. Consequently, actor-critic (A2C) and proximal policy optimization (PPO2) achieved the ion separation goal (<0.8 mS/cm) as they determined the electrical current and pump speed to be low. Particularly, A2C kept the parameters consistent in charging MCDI, which caused lower energy consumption (0.0128 kWh/m3) than PPO2 (0.0363 kWh/m3). To understand the decision-making process of A2C, the Shapley additive explanation based on the decision tree model estimated the influence of input parameters on the control parameters. The results of this study demonstrate the feasibility of RL-based controls in MCDI operations. Thus, we expect that the RL-based control model can improve further and enhance the efficiency of water treatment technologies.

The mutual effect between dual thermal power units under the advanced configuration of two units sharing one dry cooling tower and the energy-efficient and low-emission operation strategy

For a thermal power plant configuration of two power units sharing one dry cooling tower, a comprehensive computational framework was developed to reveal the mutual effect between dual units via analyzing the competition between the air-cooled radiators which are owned by two units and coupled by the shared tower, under various environmental factors and unit power load schemes. Results showed that an increase in crosswind speed vcw aggravates the uneven performance distribution among radiators, but variations in ambient temperature ta have little impact. Each growth of 10 °C in ta results in a drop of ∼1.4% in power cycle efficiency ηe for both units operating at rated load, corresponding to increases of ∼9 g/(kW·h) and ∼26 g/(kW·h) in coal consumption and CO2 emission rates. ηe of the unit with its radiators facing towards ambient crosswind is 0.3% higher than ηe of the other unit as vcw exceeds 8 m/s. When both units operate with equal power load rate Np, a decline in Np aggravates the unevenness of air inflow among radiators and amplifies the predominance of air intake of windward radiators for the shared tower. An increase in Np causes a growth in ηe for both units, but this growth becomes slight as Np exceeds 75%. For both units operating with different Np, ηe declines more sharply with vcw for the unit with a higher Np, especially when radiators of the other unit face towards ambient crosswind. To maximize the overall operation economic efficiency, load difference between dual units should be minimized and radiators owned by the unit with a higher Np should be positioned against ambient crosswind. For dual units operating at a total Np = 150% on a strong-wind summer day with ta = 32.5 °C and vcw = 12 m/s, a maximum saving of 87.9 t standard-coal can be achieved by applying the optimal operating strategy, reducing daily CO2 emission by 250.3 t.

Thermo-economic analysis of the impact of the interaction between two neighboring dry cooling towers on power generation of dual thermal power units and the energy-efficient operation strategy

Thermo-hydraulic behavior of the dry cooling tower and thermodynamic performance of the power unit was coupled by a well-developed computational framework. The interaction between two neighboring towers was detected through a numerical model under variable working conditions, and the impact of cooling performance variation of each tower on cycle efficiency of the corresponding unit was quantified by a thermo-economic model. Results showed that tower interaction mostly impacts heat rejections of radiators at the neighboring halves of both towers. A higher unit load ensures both towers better cooling performances subject to crosswind. The unfavorable wind-effect on cooling behavior of the upstream tower mitigates with increasing wind attack angle, while that on the downstream tower varies little initially and drops sharply as wind attack angle approaching 90°. For each unit, thermodynamic cycle efficiency is more sensitive to crosswind under a higher unit load. Cycle efficiency grows with unit load at low wind speeds, but generally increases and then decreases with unit load at medium–high wind speeds. Dual units should operate at the same load to maximize operational energy efficiency. Otherwise, the unit corresponding to the upstream tower should follow higher and lower load commands under small-medium and large wind attack angles, respectively.

Collaborative optimization of train timetable and speed trajectory considering stochastic photovoltaic power: A two-step approach

Faced with the integrated system composed of the train power system, the photovoltaic (PV) power system, and the energy storage system (ESS), this research studies the energy-efficient operation and energy management strategy from the perspective of both train optimal control and timetable optimization, aiming at achieving a long-term energy consumption reduction. A two-step approach for collaboratively optimizing the train timetable, speed trajectory, and energy management strategy considering the stochastic characteristics of PV power generation is proposed to solve this large-scale complex problem. Before the two-step approach, a mixed-integer linear programming (MILP) model is established to optimize the energy consumption of the inter-station operation. On this basis, explicit energy consumption expressions for all inter-stations of the entire line are obtained by the proposed data fitting method. The historical PV power data is clustered to generate scenarios with different probabilities to characterize the stochastic PV power. The first step of this two-step approach is to minimize the total energy consumption expectations of all inter-stations determined by the obtained explicit energy consumption expressions to optimize the timetable while ensuring the total time and time window constraints are met. The second step is to minimize the weighted sum of energy consumption under all possible scenarios to obtain the optimal speed trajectory and energy management strategy based on the optimized timetable obtained in the first step. The validity of the model is verified by case studies using the real data of Qingdao Metro Line 11 under both scenarios with and without PV power. This study provides a novel method for energy-efficient operation and energy management of the integrated system and demonstrates the prospect of the proposed two-step stochastic optimization in reducing the net grid-supplied energy for the long-term operation of urban rail transportation systems.

Energy Efficient Operation Optimization of Building Air-conditioners via Simulator-assisted Asynchronous Reinforcement Learning

At present, buildings are the major energy consumers in most developed and developing countries. Energy efficiency is one of the primary objectives of today’s building projects, especially in the context of carbon-peak and carbon-neutral. In buildings, air-conditioning systems account for nearly half of total building energy consumption. Optimal control and operation can significantly improve air-conditioning’s energy efficiency but the key challenge is the complex dynamics of air-conditioning systems. This study focuses the energy efficient operation of air-conditioners in office buildings, and proposes a simulation-assisted reinforcement learning (RL) method to develop energy efficient operation strategies. This study uses a whole building energy simulation model to build the environmental simulator for the air-conditioning system, and uses asynchronous RL algorithm to learn energy efficient operation strategies. Energy saving and Setpoint Not Met is used as the optimization objective as well as the criteria for evaluating the performance of the RL operation strategies. A validation simulator with varied weather conditions is also built to validate the robustness of reinforcement learning. The results show that, compared to common rule-based control strategy, 16.1% energy saving with better thermal comfort can be achieved by the RL operation strategy. In addition, the results also show the RL operation strategy has a certain level of robustness.

Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods

Residential buildings consume a large amount of energy in operation, which can be reduced by planning energy-efficient design and operation strategies. Understanding and quantifying the impact of key driving factors to the residential building energy end-use is essential for promoting more energy-efficient building design and operation schemes. Although the climate, building, and occupant-related features have been proven as the key driving factors to residential building energy end-use, their impacts are rarely compared and quantified simultaneously. This study conducts the first attempt in combining the machine learning method with Monte Carlo method to quantify the impacts of these key driving factors simultaneously. Data collected from the Residential Energy Consumption Survey 2015 of the U.S. was investigated in this study. The results indicate that the total energy end-use has positive correlations with the total building area, rooms’ numbers, windows’ numbers, indoor heating temperature setpoint and the occupants’ age; and has a negative correlation with the cooling temperature setpoint; the impacts of heating degree days and cooling degree days are nonlinear and complicated; the impact of the level of insulation is nuanced and offset by the harsh climate. The results of this study contribute good references to policymakers and architects for the synthesis of energy-efficient residential building design and operation; guidelines can be developed for the future survey on residential building energy for relevant and precise data collection to improve the building energy modelling.

A little bit flexibility on headway distribution is enough: Data-driven optimization of subway regenerative energy

As an emerging energy-efficient management approach, the essential idea of subway regenerative energy optimization is to maximize the absorption of energy generated from train deceleration by adjusting train schedules. In the extant literature, performance evaluation and optimization rely on synthetic data (e.g., computer simulations) and train energy-efficient operation (EEO) strategy. However, when compared to real automatic train operation (ATO) data, the above-mentioned method exhibits significant errors. In this work, we develop a new optimization method driven by real ATO data for maximizing subway regenerative energy based on the following three steps: first, we provide a high-frequency ATO data-driven method for simulating the amount of regenerative energy absorption; second, we propose a concept of uniformity to measure the homogeneous degree of headway distribution; third, we formulate a headway optimization model to maximize the regenerative energy absorption under uniformity constraint. To handle the high complexity of the ATO data-driven objective function (e.g., non-monotonicity, non-convexity, multi-modality), we propose an improved genetic algorithm with multiple crossover and mutation operators to search for near-optimal solutions, in which an adaptive operator selection mechanism with reduction process on ATO data is considered for speeding up the regenerative energy simulation and optimization. The effectiveness of the proposed method is confirmed by using the real ATO data of Beijing Subway Changping line. Our numerical study reveals that, benchmarked with the uniform headway distribution (the policy that is presently in use), our proposed approach achieves a relative improvement of 7.75% at off-peak hours and 42.44% at peak hours for the regenerative energy absorption; and we show that such a significant performance improvement is obtained by allowing a small level of scheduling flexibility (less than 6% relaxation on uniformity level).

An improved brain storm optimisation algorithm for energy-efficient train operation problem

This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimisation (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimisation (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimise energy consumption of the train by finding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under the same conditions.

Energy-Efficient Train Operation Strategy with Speed Profiles Selection for an Urban Metro Line

In the daily operations of a metro system, physical trains track the speed profiles which are embedded in the automatic train operation (ATO) system, and the speed profile plays a key role in determining both energy consumption and travel time. With consideration of the real-world operational environment and uncertain levels of passenger demand, this study specifically proposes an integer programming model with respect to energy consumption to generate a robust operational strategy which determines the speed profile choice on each segment. Because of the computational complexity of the proposed model, a heuristic algorithm is designed, which combines a genetic algorithm (GA) and a nomadic algorithm (NA), to find a good solution in acceptable computational time. Finally, numerical experiments based on the Beijing Yizhuang metro line are implemented to verify the effectiveness and efficiency of this approach.

An integrated approach for the energy-efficient driving strategy optimization of multiple trains by considering regenerative braking

Abstract With the rapid development of urban rail transit networks, the energy consumption problem of urban rail trains becomes more and more prominent. A critical issue is to reduce the energy consumption of trains through advanced control and optimization techniques. The majority of existing studies in this field focus on (1) the optimization of single train operation strategy and (2) the scheduling of multiple trains, separately. In this paper, we propose an integrated energy-efficient operation optimization method for multiple trains in multiple interstations by considering regenerative braking. In particular, this paper integrates the energy-efficient operation strategy with optimized train timetable under the operation scenario of two trains in multiple interstations. First, a genetic algorithm is used to optimize the energy-efficient operation strategy for single train in multiple interstations and obtain the relationship between the operation time and operation energy consumption of trains. Then, a particle swarm optimization method is adopted to obtain the optimal operation time of trains (i.e. train timetable) for the purpose of minimizing the overall energy consumption. Finally, real-world case studies based on the operation data of the Beijing subway line 7 are presented to verify the effectiveness of the proposed approach. Numerical experiments show that the proposed approach by optimizing the train operation strategy and timetable can reduce the total energy consumption by 16.24% compared with the real-world train operation strategy.

Standardised approach to energy consumption calculations for high‐speed rail

Over recent years, high-speed rail (HSR) has increased its maximum speed of operation and strengthened its presence as a major transportation mode. However, the energy consumption of HSR has in general increased as well. In the current climate of rising concern about environmental and economic problems, energy-efficient operational strategies should be investigated, which requires a precise understanding of the relationship between energy consumption and maximum speed. There is some previous research in the field; however, it is presented in different ways and shows different pictures of energy consumption. In this study, the authors present a review of previous work and highlight the inconsistencies between the calculations. In addition, a standardised approach for energy consumption calculations for HSR is provided and an investigation of the cause of inconsistencies in previous work is presented. Through this process, the worth of the proposed standardised approach is demonstrated.

Energy-Efficient Train Operation in Urban Rail Transit Using Real-Time Traffic Information

Energy-efficient train operation represents an important issue for daily operational urban rail transit. Most energy-efficient train operation strategies are normally planned according to a timetable, which is designed by offline traffic information. In this paper, a new energy-efficient train operation model based on real-time traffic information is proposed from the geometric and topographic points of view through a nonlinear programming method, leading to an energy-efficient driving strategy with real-time interstation running time monitored by the automatic train supervision system. The novelty of this work lies not only in the establishment of a new model for energy-efficient train operation but also in the utilization of combining analytical and numerical methods for deriving energy-efficient train operation strategies. More specifically, the energy-efficient operation model is built based on trajectory analysis when the energy-efficient optimal controls are applied, from which an energy-efficient reference trajectory is obtained under the running time and distance constraints, in which the nonlinear programming method is utilized. In contrast to most existing methods, the proposed model turns out to be a small-scale problem, and the difficulties of solving partial differential equations or the process of predetermining and reiteratively calculating some key factors as traditionally involved are avoided. Thus, it is more feasible to implement the strategy and easier to make real-time adjustment if needed. The comparative analysis and the simulation verification with the actual operating data confirm the effectiveness of the proposed method. With the proposed method, some delayed trains are able to maintain punctuality at the next station and sometimes even reducing energy consumption.

Advanced building energy management system demonstration for Department of Defense buildings

This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced-order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The paper concludes with recommendations for widespread aBEMS success.

A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy

Given rising energy prices and environmental concerns, train energy-efficient operation techniques are paid more attention as one of the effective methods to reduce operation costs and energy consumption. Generally speaking, the energy-efficient operation technique includes two levels, which optimize the timetable and the speed profiles among successive stations, respectively. To achieve better performance, this paper proposes to optimize the integrated timetable, which includes both the timetable and the speed profiles. First, we provide an analytical formulation to calculate the optimal speed profile with fixed trip time for each section. Second, we design a numerical algorithm to distribute the total trip time among different sections and prove the optimality of the distribution algorithm. Furthermore, we extend the algorithm to generate the integrated timetable. Finally, we present some numerical examples based on the operation data from the Beijing Yizhuang subway line. The simulation results show that energy reduction for the entire route is 14.5%. The computation time for finding the optimal solution is 0.15 s, which implies that the algorithm is fast enough to be used in the automatic train operation (ATO) system for real-time control.

Adapting Optimal Operation Strategy for the Chiller Plant of a Semiconductor FAB

In a semi-conductor FAB, the chiller plant is a major drain of power among the supporting facilities. According to ISMI, the air-conditioning system consumes a quarter of the total electricity supply of a semi-conductor Fab, even more for some older Fabs. Thus, cutting down the power consumption of the chiller plant becomes extremely important to save energy. Therefore, a study was made to achieve 3% power reduction for the chiller plant. The purpose of this paper is to report the strategies used for the power reduction through testing, adjusting and balancing, and TAB. The strategies included: development of an experimental model for the chillers’ performance, the establishment of energy-efficient operational strategies, the enhancement of cooling tower energy efficiency, and conducting a full-scale experimental investigation. The development of this experimental model utilizes the ASHRAE Guideline 14-2002 as a tool, and is integrated with the BEMS system to perform a remote real time online energy auditing system, within which an intelligent operational strategy has been implemented.

Parallel PSO-Based Optimal Strategy Study of Energy Efficient Operation Control for Train

Different from existing evolutionary algorithms which usually are implemented in serial computation mode, two improved parallel particle swarm optimization algorithms based on parallel computation structure model are proposed to search the energy efficient operation strategy for a train in railway network. The algorithms are designed and verified using a simulation case. Compared with other methods, these improved parallel PSO-based algorithms have a much better performance in efficiency and may be considered to put into practice.

PSO-Based Optimal Strategy Study of Train’s Energy Efficient Operation under Disturbed Condition

Instead of changeable chromosome length genetic algorithm, a new method based on particle swarm optimization algorithm is proposed to search a train’s energy efficient operation strategy under running disturbance condition in railway network. The algorithm is designed and validated using a referenced simulation case. Compared with other methods, it demonstrates this new PSO-based algorithm has a better result in efficiency and may be considered to use in practice.

An energy-efficient air-conditioning system with an exhaust fan integrated with a supply fan

An all-air central air-conditioning system with an exhaust fan incorporated with a supply fan is presented in this article. With the proposed system, detailed year-round energy-efficient operation strategies and control methods for the air-handling unit are presented. Further, the method of determining the pressure head and air volume of the exhaust fan is also presented, which is needed for designing this presented system. As opposed to conventional one supply-fan systems in the all-air central air-conditioning system, the proposed system is shown to not only maintain indoor pressurization required for controlling indoor air quality but also operate in an improved energy-efficient manner, achieved by having proper variable fresh-air ratios during the period of intermediate season. Due to its increased outdoor air, the proposed system can improve indoor air quality further.