Energy Optimization Problem Research Articles

Active distribution network operational optimization problem: A multi-objective tuna swarm optimization model

This study proposes multi-objective tuna swarm optimization through multi-objective transformation, initialization improvement and population variation for active distribution network (ADN). The ADN energy optimization model with dynamic reconfiguration, reactive power compensation, on-load tap changer and controllable load coordinated control are established with the minimum economic and environmental cost. Several controllable resources increase and increase the complexity of energy optimization problem in realized the effective clean energy consumption and the power grid stable operation. The minimum control cost and the minimum node voltage deviation are proposed. This study also proposes a decision-making method based on pareto front and an index to evaluate the maximum extensible dimension of intelligent algorithms in the process of analyzing energy optimization problems with intelligent algorithms. The proposed ADN energy optimization method based on multi-objective tuna swarm optimization shows excellent results after testing with the improved IEEE33 system. The voltage deviation and network loss are reduced by 51.62% and 22.16% on average compared with the single means control. The proposed model provides the support for the new energy consumption and the power grid stable operation, has strong engineering significance in the intelligent upgrading and power grid active control transformation.

Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches

This paper focuses on the energy optimization problem of traction substations. The paper addresses the difficulty of considering time-varying parameters and environmental characteristics of freight train in mechanism modeling. In response, a new optimization method called “Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches” is proposed. Firstly, a data-driven model considering the “Train-Track-Power grid” (TTP) is constructed based on recorded data. The energy optimization problem is regarded as a finite Markov decision process. A dynamic programming (DP) algorithm is utilized to optimize the train’s control force. The data-driven model is then used to solve for the train’s speed curve, traction substation power, and contact network voltage. Secondly, a multi-input multi-output self-organizing fuzzy neural network (MIMO-SOFNN) model is designed during the modeling process, and an Levenberg–Marquardt algorithm with self-adaption damping factor (SA-LM) is proposed for model parameter learning. Finally, experimental analysis is conducted to validate the high accuracy of the MIMO-SOFNN model compared to five other models. The effectiveness of the SA-LM algorithm is also verified through a comparison with five other algorithms. In the energy optimization experiments, when compared with the actual operation data of a freight railway company in China, the proposed energy optimization method in this paper reduces the energy consumption of the traction substation by 34.8%.

An energy-efficient resource allocation strategy in massive MIMO-enabled vehicular edge computing networks

The vehicular edge computing (VEC) is a new paradigm that allows vehicles to offload computational tasks to base stations (BSs) with edge servers for computing. In general, the VEC paradigm uses the 5G for wireless communications, where the massive multi-input multi-output (MIMO) technique will be used. However, considering in the VEC environment with many vehicles, the energy consumption of BS may be very large. In this paper, we study the energy optimization problem for the massive MIMO-based VEC network. Aiming at reducing the relevant BS energy consumption, we first propose a joint optimization problem of computation resource allocation, beam allocation and vehicle grouping scheme. Since the original problem is hard to be solved directly, we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them. Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.

An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems

The spherical evolution algorithm (SE) is a unique algorithm proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due to the challenges arising from the multiple choices of operators and the utilization of a spherical search method. In this paper, we introduce an enhancement method that incorporates weights in individuals’ dimensions that are affected by individual fitness during the iteration process, aiming to improve SE by adaptively balancing the tradeoff between exploitation and exploration during convergence. This is achieved by reducing the randomness of dimension selection and enhancing the retention of historical information in the iterative process of the algorithm. This new SE improvement algorithm is named DWSE. To evaluate the effectiveness of DWSE, in this study, we apply it to the CEC2017 standard test set, the CEC2013 large-scale global optimization test set, and 22 real-world problems from CEC2011. The experimental results substantiate the effectiveness of DWSE in achieving improvement.

Bir İnceleme: Farklı Teknolojilerin Kullanımı ile Elektrik Enerjisi Depolama Sistemi Bağlantılı Hibrit Güç Sistemleri

Energy storage system is becoming crucial in the electric power system. It can response to economic, environmental, geopolitical and technological considerations. Energy storage system has a great role to covering energy for power electric system as renewable energy source, improves energy efficiency and promotes the integration of variable renewable energies, brings security and flexibility to networks, control and regulation. Several studies have been focused on different energy storage technologies connected to various hybrid energy systems to supply power to the grid /load. Almost of these recent papers have been mentioned the performance and the utility of storage system in term of management, control, cost, lifetime. For this reason, an overview is offered in this paper including hybrid energy system and using various energy storage technologies to generate electric power in the aim to clarify the use of diverse storage technologies from several sides as energy management strategy, control and optimization problems. A comparative study is made to demonstrate more the analysis of this paper.

Energy management optimization for connected hybrid electric vehicle using offline reinforcement learning

Energy management strategy (EMS) is critical to ensure the long-term energy economy of hybrid electric vehicles. The classical deep reinforcement learning algorithms exist many issues such as the safety constraint and the simulation-to-real gap, resulting in difficulties in applications to industrial tasks. Thus, this paper proposes a novel EMS and a policy updating method based on the offline deep reinforcement learning algorithm to address the energy optimization problem. Firstly, the batch-constrained deep Q-learning algorithm is applied to provide a solution training the control strategy based on the existing datasets without interaction with the environment. Secondly, the EMS updating method is proposed to improve the adaptability under complicated driving cycles. The Jensen–Shannon divergence is introduced to determine when offline control strategy updates in real time. Finally, the optimality and effectiveness of the proposed EMS are validated, and the real-time and adaptive performance of the proposed method is also verified. The results indicate that the proposed EMS can learn a superior policy from fixed data, and the proposed updating method can utilize the real-time data to update the offline policy approaching the online deep reinforcement learning-based strategy in energy consumption.

Model‐based Crowd Behaviours in Human‐solution Space

AbstractRealistic crowd simulation has been pursued for decades, but it still necessitates tedious human labour and a lot of trial and error. The majority of currently used crowd modelling is either empirical (model‐based) or data‐driven (model‐free). Model‐based methods cannot fit observed data precisely, whereas model‐free methods are limited by the availability/quality of data and are uninterpretable. In this paper, we aim at taking advantage of both model‐based and data‐driven approaches. In order to accomplish this, we propose a new simulation framework built on a physics‐based model that is designed to be data‐friendly. Both the general prior knowledge about crowds encoded by the physics‐based model and the specific real‐world crowd data at hand jointly influence the system dynamics. With a multi‐granularity physics‐based model, the framework combines microscopic and macroscopic motion control. Each simulation step is formulated as an energy optimization problem, where the minimizer is the desired crowd behaviour. In contrast to traditional optimization‐based methods which seek the theoretical minimizer, we designed an acceleration‐aware data‐driven scheme to compute the minimizer from real‐world data in order to achieve higher realism by parameterizing both velocity and acceleration. Experiments demonstrate that our method can produce crowd animations that are more realistically behaved in a variety of scales and scenarios when compared to the earlier methods.

All-in-One Control Framework for Distributed Drive Electric Buses Path Tracking Subject to Uncertain Crosswind and Varied Passenger Mass

This paper proposes an all-in-one control framework for distributed drive electric buses (DDEBs) that can effectively attenuate the effects of uncertain crosswind and varied passenger mass in path tracking, while minimizing the electricity consumption of battery. To track the desired path and ensure driving stability, a robust tube-based model predictive control method is proposed considering lateral, yaw and roll motions. Simultaneously, for the longitudinal motion, a robust sliding mode control method is designed to achieve accurate tracking of longitudinal velocity. Meanwhile, the uncertain crosswind and varied passenger mass faced by the DDEBs are decoupled into external disturbances and their effects on the system are eliminated in both controllers. Then, to cope with energy optimization problem in path tracking, a torque distribution controller is constructed by formulating the vehicle stability margin, tracking dynamic requirements and energy efficiency as constrained weighted least squares problem. Finally, a Trucksim-Simulink co-simulation is performed, where the simulation results demonstrated that the proposed control framework is robust and effective, and can achieve better energy saving effect compared to the original control methods.

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

An energy optimal schedule method for distribution network considering the access of distributed generation and energy storage

AbstractThe access of large‐scale distributed generation (DG) easily leads to energy imbalance in distribution network. To deal with this issue, this paper proposes an energy optimal schedule method for distribution network considering the participation of source‐load‐storage aggregation groups (SAGs). Firstly, the system model consisting of distribution network layer and SAGs layer is established, and the schedule objectives and constraints of each layer are also given. Secondly, considering the fluctuation on the load side, a forecasting method based on Adaboost integrated convolutional neural networks and bidirectional long‐short term memory is proposed. Then, the improved sparrow search algorithm (ISSA) is proposed by using the tent map and Levy flight on the original sparrow search algorithm. At the same time, by introducing Pareto dominance relation and adaptive grid algorithm, the multi‐objective sparrow search algorithm (MOSSA) is derived. After that, a two‐layer optimization framework (ISSA–MOSSA) is proposed to solve the studied system. The simulation results verify the accuracy of the proposed load forecasting model, the superiority of ISSA as well as MOSSA, and the effectiveness of ISSA–MOSSA in solving the energy optimal schedule problem of the distribution system with the access of DG.

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In wireless communications, the channel plays a major role in determining energy requirements. Therefore, a mixed-integer nonlinear programming problem is formulated to jointly optimize power allocation, sub-channel allocation, user selection, and the activated remote radio units (RRUs) in a combinatorial approach according to the channel conditions. Although it is an NP-hard problem, the optimization problem is solved through fractional programming properties, converting it into an equivalent tractable and parametric form. The resulting problem is then solved optimally by using the Lagrangian decomposition method and an improved Kuhn–Munkres algorithm. The results show that the proposed technique significantly improves the energy efficiency of IoT systems as compared to the state-of-the-art work.

Multi-parameter adaptive online energy management strategy for concrete truck mixers with a novel hybrid powertrain considering vehicle mass

Given the characteristics of dynamic vehicle mass, multi-operation scenes, and multi-energy-source and multi-energy-consumption-source, the energy optimization problem of concrete truck mixers remains a challenge. To this end, in this paper, a novel hybrid powertrain with a single-shaft parallel for the upper-part system and extended-range for the driving system is utilized and an adaptive online energy management strategy with multi parameters including the driving cycle, vehicle mass, battery state of charge (SOC), and remaining mileage is presented. First, a driven-data clustering method with fuel consumption differentiation is adopted. Then, using the parallel computing genetic algorithm-optimized rule-based method, a driving condition knowledge base, including optimal control models of the various typical driving conditions under multi-parameter combinations, is built to realize the adaptability and robustness. Parameter identifiers are designed, including a sparrow search-optimized-based deep mixed kernel extreme-learning machine recognizer and a vehicle mass recognizer based on sensor and working scene fusion for obtaining corresponding information online. Finally, simulation experiments are conducted to validate the performance and robustness of the presented control strategy. Results indicate it can reduce fuel consumption by an average value of 15.17% and performs better in the convergence level of terminal SOC in five real composite driving conditions.

Efficient energy management for a plug-in hybrid electric vehicle considering motor current alert mechanism

Plug-in hybrid electric vehicle (PHEV), due to its energy cleanliness and sustainability, has gained plenty of research in cleaner production. Energy management strategy (EMS) can govern the energy flow and reduce fossil fuels wasting. Most PHEVs prefer their motors working prolonged time to pursue better fuel-saving performance, and the overcurrent protection (OCP) would easily occur due to the motor taking additional demand power, and it would be a challenge to develop self-examination of EMS and ensure the efficient energy distribution. Thus, this paper proposes an efficient energy management for a PHEV considering motor current alert (MCA) mechanism. First, an MCA mechanism is originally established for PHEV to prevent large motor currents that persist for a long time, which improves the current sensitivity of EMS. Second, a multi-step Markov chain is used to predict future velocities and a new clustering method for Markov states is designed, which improves the grid clustering method. Third, the cooperative game theory (CGT) of the energy optimization problem is formulated, and its calculation process is implemented by model predictive control (MPC) method. This CGT-MPC can optimize both group profit and personal profit. Finally, the proposed strategy is validated against other baseline strategies in both simulation and bench test. Comparison results show that the proposed strategy can reduce the frequency and duration of large motor current occurrences by 71% under complex driving conditions, while at most reducing the fuel consumption by 10.28% and electricity consumption bias within 1%.

Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning

Aiming to improve the distance per charge of four in-wheel independent motor-drive electric vehicles in intelligent transportation systems, a hierarchical energy management strategy that weighs their computational efficiency and optimization performance is proposed. According to the information of an intelligent transportation system, a method combining reinforcement learning with receding horizon optimization is proposed at the upper level, which solves the cruising velocity for eco-driving in a long predictive horizon based on the online construction of a velocity planning problem. At the lower level, a multi-objective optimal torque allocation method that considers energy saving and safety is proposed, where an analytical solution based on the state feedback control was obtained with the vehicle following the optimal speed of the upper level and tracking the centerline of the target path. The energy management strategy proposed in this study effectively reduces the complexity of the intelligent energy-saving control system of the vehicle and achieves a fast solution to the whole vehicle energy optimization problem, integrating macro-traffic information while considering both power and safety. Finally, an intelligent, connected hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this study. The simulation results demonstrate that the proposed method reduces energy consumption by 12.98% compared with the conventional constant-speed cruising strategy. In addition, the computational time is significantly reduced.

Real-time energy optimization and scheduling of buildings integrated with renewable microgrid

Real-time energy optimization is essential for effective load scheduling, cost reduction, maintaining demand and supply balance, and ensuring reliable power system operations. However, real-time energy optimization is challenging due to the unpredictable nature of renewable energy sources (RES) and the behavior of electric loads. On this note, a rigid model is required that can deal with this dilemma. Thus, the Lyapunov optimization technique (LOT) emerged as a solution for the real-time energy optimization problem. This work investigates a smart home equipped with inflexible loads (TV, computer, light, etc.), flexible loads (EVs, HVAC, water heaters, etc.), and RES (photovoltaic and wind energy) in a grid-connected mode that ensures energy trading (purchasing and selling of energy). The aim is to optimize total cost, thermal discomfort cost, and batteries and EVs charging/discharging using LOT by real-time energy optimization, which does not require any system parameters to be anticipated. The proposed algorithm employs LOT for four queues to solve the real-time energy optimization problem. Simulations are conducted for different scenarios and varying weather conditions to endorse the effectiveness of the developed real-time energy optimization solution in various aspects of the performance metrics.

An energy-optimization method to study gel-swelling in confinement.

We recast the problem of hydrogel swelling under physical constraints as an energy optimization problem. We apply this approach to compute equilibrium shapes of hydrogel spheres confined within a jammed matrix of rigid beads and interpret the results to determine how confinement modifies the mechanics of swollen hydrogels. In contrast to the unconfined case, we find a spatial separation of strains within the bulk of the hydrogel as the strain becomes localized to an outer region. We also explore the contact mechanics of the gel, finding a transition from Hertzian behavior to non-Hertzian behavior as a function of swelling. Our model, implemented in the Morpho shape optimization environment and validated against an experimentally demonstrated prototypical scenario, can be applied in any dimension, readily adapted to diverse swelling scenarios and extended to use other energies in conjunction.

Neural network atomistic potentials for global energy minima search in carbon clusters.

The global energy optimization problem is an acute and important problem in chemistry. It is crucial to know the geometry of the lowest energy isomer (global minimum, GM) of a given compound for the evaluation of its chemical and physical properties. This problem is especially relevant for atomic clusters. Due to the exponential growth of the number of local minima geometries with the increase of the number of atoms in the cluster, it is important to find a computationally efficient and reliable method to navigate the energy landscape and locate a true global minima structure. Newly developed neural network (NN) atomistic potentials offer a numerically efficient and relatively accurate approach for molecular structure optimization. An important question that needs to be answered is "Can NN potentials, trained on a given set, represent the potential energy surface (PES) of a neighboring domain?". In this work, we tested the applicability of ANI-1ccx and ANI-nr NN atomistic potentials for the global minima optimization of carbon clusters Cn (n = 3-10). We showed that with the introduction of the cluster connectivity restriction and consequent DFT or ab initio calculations, ANI-1ccx and ANI-nr can be considered as robust PES pre-samplers that can capture the GM structure even for large clusters such as C20.

Multi-step Load Optimization for Thermoelectric Power Generation

The thermoelectric generator is an essential device used in the process of thermoelectric power generation. This paper researches the energy optimization problem of thermoelectric generators, and the goal is to maximize total output energy over a duration of time. The Multi-Step Load Optimization (MSLO) method is developed for addressing the problem. In our proposed method, we use Artificial Bee Colony (ABC) algorithm to search for the resistance values of multi-step to find a high-quality resistance sequence swiftly. Next, the encoding scheme and local search operators are developed. Instead of using the simulation model directly, we use the Kriging model as evaluation function of ABC algorithm to speed up the calculation of the evaluation value. Results show that the kriging model has high efficiency and accuracy, and when compared to state-of-art maximum power point tracking (MPPT) algorithms, the MSLO method has better optimization performance.

An Effective Metaheuristic Approach for Building Energy Optimization Problems

Mathematical optimization can be a useful strategy for minimizing energy usage while designing low-energy buildings. To handle building energy optimization challenges, this study provides an effective hybrid technique based on the pelican optimization algorithm (POA) and the single candidate optimizer (SCO). The suggested hybrid algorithm (POSCO) benefits from both the robust local search power of the single candidate method and the efficient global search capabilities of the pelican optimization. To conduct the building optimization task, the optimization method was developed and integrated with the EnergyPlus codes. The effectiveness of the proposed POSCO method was verified using mathematical test functions, and the outcomes were contrasted with those of conventional POA and other effective optimization techniques. Application of POSCO for global function optimization reveals that, among the thirteen considered functions, the proposed method was best at finding the global solution for seven functions, while providing superior results for the other functions when compared with competitive techniques. The suggested POSCO is applied for reducing an office buildings’ annual energy use. Comparing POSCO to POA procedures, the building energy usage is reduced. Furthermore, POSCO is compared to simple POA and other algorithms, with the results showing that, at specific temperatures and lighting conditions, the POSCO approach outperforms selected state-of-the-art methods and reduces building energy usage. As a result, all data suggests that POSCO is a very promising, dependable, and feasible optimization strategy for dealing with building energy optimization models. Finally, the building energy optimization findings for various climatic conditions demonstrate that the changes to the weather dataset had limited effect on the efficiency of the optimization procedure.