Optimization Method For Energy Management Research Articles

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Energy Management Optimization Method of Plug-In Hybrid-Electric Bus Based on Incremental Learning

The state of charge (SOC) trajectory planning for the power battery is the basis of realizing the global online optimal control of the plug-in hybrid-electric bus (PHEB) energy management system. This article proposed an adaptive rolling planning method for SOC trajectory of PHEBs power battery. First, a mathematical model for simulation research is established. Second, the driving cycles of PHEBs are collected, and the optimal SOC trajectories of driving cycles are obtained using dynamic programming (DP). In order to provide training data for the planning model of the optimal SOC trajectory of a trip segment (TS), the driving cycle and the optimal SOC trajectory of the driving cycle are cut into segments with a time length of 30 s. The incremental learning is used to construct a planning model of the TS’s optimal SOC trajectory and planning the optimal SOC trajectory of the actual TS. The optimal SOC trajectory of the actual TS is applied to proportion–integration-differentiation (PID) control and compared with PID based on mileage-based SOC planning (BMP). The result shows that this study can reduce fuel consumption by 0.8 L/100 km for the same driving cycle. Through incremental learning, the fuel consumption can be further reduced by 0.13 L/100 km.

Sustainable Building Optimization Model for Early-Stage Design

Buildings represent the largest potential for carbon reduction worldwide. This highlights the need for a simulation and optimization method for energy management. The early design stage of buildings represents an important phase in which choices can be made to optimize design parameters. These parameters can focus on multiple areas, including energy and thermal comfort. This paper introduces the optimization of early-stage sustainable building design considering end-user energy consumption. It proposes an optimization model that integrates multiple layers, which consist of a parametric energy simulation, artificial neural network, and genetic algorithm. The proposed optimization model considers a single objective function to obtain the optimal design. The targeted goal is to obtain minimal energy consumption for residential buildings during the early design stages. Key design parameters of the building were identified for optimization and feasible ranges for them were obtained using genetic algorithms. Finally, the results of this paper include the identification of the optimal building design for the thermal comfort analysis and optimal energy performance. The model was applied to a case study in Egypt and the results showed that using the developed optimization model can lead to a 25% reduction in energy consumption.

A Novel Energy Management Optimization Method for Commercial Users Based on Hybrid Simulation of Electricity Market Bidding

Energy management and utilization for commercial users is becoming increasingly intelligent and refined, fostering a closer and growing connection with the electricity market. In this paper, a novel energy management optimization theoretical framework for commercial users is proposed based on the hybrid simulation of electricity market bidding. The hybrid simulation model based on Multi-Agent Simulation (MAS) with reinforcement learning and System Dynamic Simulation (SDS) is established to solve the problem using a single simulation method: it cannot adjust the clearing price when considering the whole market; considering the uncertainty of Electric Vehicles (EVs) travel and Lighting Loads (LLs), the multi-objective optimization model of energy management for commercial users is constructed to minimize the total energy cost of commercial users, as well as maximize the lighting comfort of indoor office staff, which compensates for the lack of the single-objective optimization of the power consumption for commercial users. A multi-objective optimization model of energy management for commercial users is established based on the hybrid simulation of electricity market bidding. By running the multi-objective optimization model based on hybrid simulation, the results show that the proposed method can realize the optimization of energy management for commercial users considering electricity market bidding.

A Distributed Online Learning Approach for Energy Management With Communication Noises

An online distributed neurodynamic optimization method for energy management in smart grids is proposed to minimize the operational cost of smart grids, considering various constraints, such as output bounds, state of charge of battery storage systems, network congestion and voltage limits. The proposed distributed neurodynamic optimization method adopts a consensus protocol using the signs of the information differences between neighbors for information exchanging and a recurrent neutral network for optimization without dual multipliers. It has the following merits. First, the calculation and information exchange of dual multipliers are avoided to mitigate the computational complexity and reduce communication bandwidth. Second, the proposed optimization method is robust against communication noises from various components. Third, the uncertainties of renewable energy and loads are addressed by the online optimization approach, which takes advantage of the historical data. The convergence, optimality, consensus and robustness of the proposed distributed neurodynamic optimization method are rigorously proved in theory and verified in case studies on the modified IEEE 33-bus and IEEE 123-bus distribution systems.

Parallel and Distributed Optimization Method With Constraint Decomposition for Energy Management of Microgrids

Energy management in power systems is a thorny optimization problem. With the sizes of systems rising, centralized optimization methods are restricted by their complexities of communications, while distributed optimization methods have emerged as a powerful tool for dealing with increasingly complex systems. However, convergence rates of some widely used distributed optimization methods, such as the standard alternating direction method of multipliers (ADMM), still have room for improvement. In this paper, a parallel and distributed optimization method for energy management of microgrids (MGs) is proposed to boost the convergence rate without sacrificing the accuracy of the optima, in which agents calculate, exchange and update in parallel. At first, a decomposition method is presented, where the objective functions and constraints of an original optimization problem with separable variables are decomposed into local objective functions and constraints for agents, which is the key to our method. Further, agents solve their local optimization problems independently and then exchange determined optima with their neighbors. Finally, the method is evaluated to solve economic dispatch with demand response for microgrids. The simulation results show that compared to the standard ADMM, for a given accuracy, the number of iterations in our method is only one third or even less than that of ADMM. Furthermore, our method can minimize the cost functions of distributed generation on supply side and maximize the profit functions of flexible loads on the demand side.

Data-driven energy management and velocity prediction for four-wheel-independent-driving electric vehicles

This paper proposes an online energy management and optimization method for four-wheel-independent-driving electric vehicles via stochastic model predictive control (SMPC), where velocity is predicted as the foundation to ensure feasibility and efficiency. By utilizing operating data of real-world electric vehicles from a big data platform, a data-driven Markov chain method is adopted to achieve vehicle velocity prediction in an accurate and reliable way. On top of the proposed method, real-time updates of the sample space and online substitution of the velocity-acceleration (V-A) state space can be realized, which mitigates problems of prediction interruption resulting from deficiency of sample state. Simulation results based on a constructed Hardware-in-Loop system indicate effectiveness of velocity prediction with root-mean-square error under 1.3 km/h. In the perspective of the energy conservation, the SMPC method can decrease energy consumption by 7.92% compared with traditional Rule-based methods, which is close to the optimization result of a conventional dynamic programming method. Further simulation and test results demonstrate that the proposed data-driven method is capable of realizing online accurate velocity prediction and energy management for real-world vehicles. • An energy management optimization method for four-wheel-independent-driving vehicles. • A data-driven Markov chain with is adopted for vehicle velocity prediction. • The stochastic model prediction control method is employed for torque allocation. • The proposed energy optimization method is verified through the Hardware-in-Loop test.

An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

Multi-microgrid energy management through tertiary-level control: Structure and case study

The widespread emergence of renewable energy resources has made microgrids neighbors. These microgrids share their power, and multi-microgrids are formed, which is more stable and flexible. However, the main problem of these smart grids is their complex control. To this end, tertiary-level control can play a significant role. This paper sought to cite the existing control models and strategic plans for microgrid operation, review the literature and provide a comprehensive picture of microgrid and multi-microgrids energy management at the tertiary-level control. Then, by introducing the component fatigue life as a new factor, an optimization method for energy management of multi-microgrids at the tertiary level is developed. For solving the problem, MATLAB and DigSilent software are used simultaneously. Different connection models and secondary level controller settings are determined using the binary grey wolf optimization in MATLAB. This data is provided to the DigSilent to carry out the optimal dispatch of generation units. The iterations are repeated until the desired operating point is reached. Several case studies are implemented to evaluate the performance of the proposed method. The results show that operating costs can be reduced up to 62.91% under the proposed method compared with the island mode.

Home energy management optimization method considering potential risk cost

With the popularization of smart power consumption technology, users can optimize the use of electrical equipment through a home energy management system (HEMS), thereby reducing electricity costs while maintaining a degree of comfort. In this study, we established an HEMS with distributed power and an electrical vehicle, describing a multi-objective optimization model with risk cost and risk index constraints, which have not been considered in previous work. The problem can be solved with improved genetic methods. First, this study establishes a mathematical model for the typical electrical equipment of specific smart home users. On this basis, with the user’s electricity consumption cost and risk index being considered, we propose a multi-objective optimization model of the user’s overall satisfaction and the corresponding constraints of the various devices in the home. This model is regarded as a two-stage optimization considering both electricity cost and power fluctuation. Finally, we verify the effectiveness of the model and the optimization algorithm through example simulations.

A cascaded energy management optimization method of multimode power-split hybrid electric vehicles

A novel multimode power-split hybrid electric vehicle demonstrates significant advantages in energy conservation. The complicated structure and multiple operation modes, however, have brought significant challenges to energy management. The problems of operation mode selection, power allocation, and operating point selection need to be solved simultaneously. Traditionally, existing energy management strategies first have determined operation mode and then have solved power allocation and operating points. Moreover, traditional dynamic programming usually has not considered electric energy consumption and has deviated from the optimal solution. To solve these problems, a cascaded energy management strategy that integrates dynamic programming and equivalent consumption minimization strategy (DP + ECMS) is proposed. An iterative method is used to optimize the equivalence factor. A vehicle model based on actual control strategy is established and validated by test results. Then fuel economy simulations are conducted. Results showed that DP + ECMS could achieve 19.9% fuel economy improvement compared with the rule-based strategy for a new European driving cycle. In addition, simulation results of a Worldwide Harmonized Light-Duty Test Cycle demonstrated that DP + ECMS performed well in real road driving conditions. This study introduces a novel multimode power-split hybrid system and provides a global energy management optimization method.

Risk-Based Uncertainty Set Optimization Method for Energy Management of Hybrid AC/DC Microgrids With Uncertain Renewable Generation

Uncertainties associated with the increasing penetration of renewable power generation (RPG) in microgrids have introduced numerous challenges to their effective energy management. This paper proposes a novel risk-based uncertainty set optimization method for the energy management of typical hybrid AC/DC microgrids, where RPG outputs are considered as the major uncertainties. The underlying risks of RPG curtailment and load shedding are formulated in detail based on the probabilistic distribution of forecasted RPG values, and are further considered in the objective functions. The proposed model is solved using a piecewise linearization method combined with the quadratic Newton-Gregory interpolating polynomial technique to linearize the variable integration limit terms of the underlying risks, while the Chebyshev consistent linear approximation method is proposed to approximate the non-linear terms of the bi-directional converter conversion efficiency. Finally, the proposed model is reformulated as a mixed integer linear programming problem, and effectively solved using a high-performance solver. The proposed method is applied in simulations of an actual hybrid AC/DC microgrid system in China to demonstrate its effectiveness, good applicability, and robustness in comparison to standard robust optimization methods. The impact of RPG prediction accuracy and electric vehicle battery loss cost on the obtained solutions are further analyzed and discussed.

Smart Home Energy Management Optimization Method Considering Energy Storage and Electric Vehicle

As the last link of an integrated future energy system, the smart home energy management system (HEMS) is critical for a prosumer to intelligently and conveniently manage the use of their domestic appliances, renewable energies (RES) generation, energy storage system (ESS), and electric vehicle (EV). In this paper, we propose a holistic model to center the preference of users when scheduling the involved physical equipment of different natures. Further, a dedicatedly designed charging and discharging strategy for both the ESS and EV considering their capital cost is proposed to integrate them into the HEMS for providing a better flexibility and economic advantages as well as to prolong the life of the batteries. Based on the mixed integer linear programming (MILP) and the proposed model, the energy schedule of the smart home can be derived to guarantee both the lowest cost and the comfort for the users. An illustrative case study is employed to demonstrate the effectiveness of the proposed method.

A robust optimization method for energy management of CCHP microgrid

Energy management is facing new challenges due to the increasing supply and demand uncertainties, which is caused by the integration of variable generation resources, inaccurate load forecasts and non-linear efficiency curves. To meet these challenges, a robust optimization method incorporating piecewise linear thermal and electrical efficiency curve is proposed to accommodate the uncertainties of cooling, thermal and electrical load, as well as photovoltaic (PV) output power. Case study results demonstrate that the robust optimization model performs better than the deterministic optimization model in terms of the expected operation cost. The fluctuation of net electrical load has greater effect on the dispatching results of the combined cooling, heating and power (CCHP) microgrid than the fluctuation of the cooling and thermal load. The day-ahead schedule is greatly affected by the uncertainty budget of the load demand. The economy of the optimal decision could be achieved by adjusting different uncertainty budget levels according to control the conservatism of the model.

Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles

Range-extended electric vehicles (REEVs) are becoming a development trend of new vehicles. Energy management is one of the core problems in REEVs. The structure and control method of the auxiliary power unit (APU) is determined based on the configuration analysis in this paper. An energy management optimization problem is proposed to solve the power distributions of APUs and batteries in the charge-sustaining (CS) stage of REEVs, which are determined by dynamic programming and pseudo-spectral optimal control, respectively. The results show that different limits of the APU power changing rate significantly influence fuel consumption. To obtain the power changing rate of APUs and to evaluate the energy management optimization method of REEVs, a model of the APU control system is built and verified by a platform test; the dynamic response characteristics and control parameters of the APU are obtained by step-changing conditions. Two types of strategies for tracking APU power are proposed for different power changing rates, and the fuel consumption of REEVs is analyzed in four types of driving cycles. The effect on fuel consumption caused by the power changing rate of the APU is verified.