Optimal Energy Management Strategy Research Articles

Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning

Reliability and cost-effectiveness in the operation of the multiple microgrid (MMG) system depend on the skillful management of its energy resources. Traditional energy management approaches are physics-model-based, which rely on the precise system parameters (e.g., line parameters) of the electricity and heat network. It is difficult to establish the precise system parameters in practice because they depend on a variety of factors. In this context, this paper proposes a physics-model-free control framework for the energy management of MMGs with heat-electricity energy, consisting of proposed surrogate model and multi-agent deep reinforcement learning (MADRL) approach. An important step is to use historical data to train a surrogate model in supervised manner that can imitate the realistic power and thermal flow calculations. Meanwhile, the energy management problem is reformulated as a Markov game. It is solved by the proposed MADRL-based approach by modeling each MG controller as an agent with a specific objective. The historical trajectories representation, parameter space technology, and deep dense architecture in reinforcement learning are introduced in MADRL to overcome the negative impact brought by the time series data from the input state on the decision-making process and construct an efficient exploration mechanism to overcome inefficient optimization of the MMG system in a multi-agent setting. During training period of MADRL, the trained surrogate models integrate into the environment of the MADRL, which can develop optimal energy management strategy based on the continuous interaction with the surrogate models. The proposed surrogate model enabled MADRL approach can reduce the reliance on precise physical systems and prevent having an impact on the real system while being trained involving trial and error process. Simulation results demonstrate the effectiveness of the proposed control framework.

Incorporating the best sizing and a new energy management approach into the fuel cell hybrid electric vehicle design

Under the banner of sustainable transportation, fuel cell hybrid electric vehicles (FC-HEVs) have become a matter of fascination in today's ever-growing need to save fossil fuels and reduce greenhouse gas emissions, addressing the 3Es challenges (efficiency, economy, and environment). However, one of the most pressing issues at the moment is determining how to incorporate the optimal sizing and energy management strategy (EMS) into FC-HEVs. In this regard, this article presents an integrated approach for optimal sizing as well as a new energy strategy for the design of FC-HEVs. To minimize the decision variables, including operating cost, fuel consumption, and component weight, an in-house optimization MATLAB code with a Multi-Objective Particle Swarm Optimization algorithm was developed. Four energy cases were tested under ARTEMIS and NEDC driving cycles for simulation, and their influence on the key decision variables was also investigated. The results show that using a battery pack with a supercapacitor reduces fuel consumption by 19% and 30.3% in both driving cycles, while decreasing the maximum output power of the fuel cell. It should be noted that hybrid energy sources have the potential to improve vehicle performance.

Multi-objective optimal day-ahead scheduling of desalination-hydrogen system powered by hybrid renewable energy sources

An energy system with desalination and hydrogen production and storage is a promising option for remote areas with shorelines, e.g., Middle East, to jointly manage electricity, desalinated water and hydrogen resources. Thus, a hybrid renewable energy system considering seawater reverse osmosis desalination, proton exchange membrane electrolyzer stacks, and also proton exchange membrane fuel cell, is proposed. This work focuses on the optimal operation problem of the system. It is established in a multi-objective optimization manner, with consideration of minimizing system total cost, power transmission and renewable energy curtailment. The problem is solved with Non-dominated Sorting Genetic Algorithm-III, a meta-heuristic method dedicated to multi-objective optimization. Results show through the solving of the optimization problem the optimal energy management strategy can be obtained, and in the studied scenario, the system can avoid 38–42% of carbon dioxide emission compared to conventional electricity generation and gray hydrogen production measures. The operational benefit of fuel cell is also verified. Compared to existing works, this work maintains the flexibility and cleanness of green hydrogen production components, consider more aspect in operation and can be solved with limited computational resources and obtain a satisfying result.

Optimal configuration of a large scale on-grid renewable energy systems with different design strategies

Several oil-importing countries tend to utilize renewables as alternatives to traditional energy sources. This is to replace conventional fossil fuel plants with clean, economical, and reliable energy resources. In this paper, all possible on-grid renewable energy systems with different design scenarios are investigated to satisfy the load demand of Al-Mastaba city, Jordan. These grid-connected cases include solar photovoltaic (PV) only, wind only, hybrid PV/wind, PV/pumped hydro storage (PHS), wind/PHS, and hybrid PV/wind/PHS systems. In each scenario, the optimal sizing and energy management strategy are achieved using flow direction optimization algorithm. This is done by minimizing the grid usage factor (GUF). The decision variables include the number of PV panels, the number of wind turbines, and/or the height of upper reservoir (UR) of the PHS facility (hhydro). The input hourly measured data for systems' components were brought from formal institutions to get realistic and future real-life recommendations. Results showed that hybrid PV/wind/PHS (Scenario 6) has the best design. It has the lowest GUF of 0.59%. It comprises of 12,692 solar panels, 28 wind turbines, and 106.98m as hhydro of the UR. The uncertainty analysis on input measured data is implemented to test the system's robustness. It is noticed that the 6th scenario released an amount of CO2 of 122.29 t/year and the corresponding renewable storage factor is 63.72% with 0.042 $/kWh as the lowest value of levelized cost of energy. The methodology of this paper is highly recommended anywhere to reduce grid purchases and make it dispensable by adopting renewable retrofitting systems.

Optimal energy management strategy for distributed renewable energy power generation system based on “three-flow” theory

Aiming at the off-grid application of distributed renewable energy, this study proposes a combined hydrogen, heating and power system based on solar energy, which mainly includes alkaline water electrolysis, metal-hydride hydrogen storage and proton exchange membrane fuel cells. To achieve the best combination of electricity, heat, and hydrogen, four energy management strategies based on mass, energy, and information flow are proposed. Taking an ecological community in Ningbo as the application scenario, the system performance under four control strategies in typical summer and winter days is studied, and the best control strategies in summer and winter are given, respectively. Electricity-led and high-led strategies are recommended for summer and winter, and both strategies can achieve a real-time supply of electric power, heating power, and hydrogen. After a day of operation, the two strategies have surpluses of 0 and 221.5 kW h of electricity, 5706 and 102.16 MJ of heat, and 57 and 17.82 kg of hydrogen, respectively.

Optimal sizing and energy management strategy for EV workplace charging station considering PV and flywheel energy storage system

Optimal sizing and energy management strategy for EV workplace charging station considering PV and flywheel energy storage system

An Online Efficiency Optimized Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

A reasonable energy management strategy (EMS) for a hybrid power system can effectively optimize the critical indicators of the system. In this paper, a Multi-level EMS that can improve the energy utilization rate of the fuel cell hybrid electric vehicle (FCHEV) was proposed. Focused on the energy distribution, a system overall efficiency optimization model was built first. Then, to reduce the computational burden caused by solving the optimization model, an instantaneous optimal search (IOS) algorithm with better online performance was proposed. Thus, the corresponding load demand power distribution strategy that can optimize the system overall efficiency can be designed. Hardware in the loop (HiL) experimental results indicated that the proposed EMS can realize optimization of the FCHEV energy utilization rate compared with other traditional strategies. To further validate that the proposed strategy can contribute to the practical application of the fuel cell electric vehicle, a 1.2kW hybrid electric vehicle was built in the laboratory. Results of the vehicle driving tests indicated that the designed EMS can effectively optimize the overall efficiency of the FCHEV hybrid power system, which can help to contribute to the application of fuel cell hybrid electric vehicles.

Dedicated Adaptive Particle Swarm Optimization Algorithm for Digital Twin Based Control Optimization of the Plug-In Hybrid Vehicle

The digital twin (DT) is a promising technology that will provide a cyber-physical platform to enable rapid product development boosted by artificial intelligence and big data. This paper focuses on DT-based optimization of the energy management strategy (EMS) for a plug-in hybrid vehicle (PHEV). A dedicated adaptive particle swarm optimization (DAPSO) algorithm is developed to enhance the optimality and trustworthiness of the DT-based EMS optimization in a variety of driving conditions. By using the chassis dynamometer data, the DAPSO is developed by incorporating the widely-used PSO algorithm with an adaptive swarm control strategy that coordinates the exploration and exploitation of the individual swarming particles following the global information extracted from the swarm. With the cross-validation testing under three worldwide driving conditions, this study determines the unified settings of the DAPSO algorithm for the PHEV application. Experimental evaluations are conducted by monitoring the PHEV’s performance using different EMSs with control thresholds optimized by the DAPSO and conventional PSO algorithms (baseline). The results show that by introducing DAPSO for DT-based EMS optimization, up to 24% improvement in cost function value (weighted sum of the fuel consumption and SoC sustaining error) and more than 49.1% reduction in computing time can be achieved compared to the baseline methods.

Capacity Optimal Allocation Method and Frequency Division Energy Management for Hybrid Energy Storage System Considering Grid-Connected Requirements in Photovoltaic System

The coordination between a hybrid energy storage system (HESS) and photovoltaic (PV) power station can significantly reduce grid-connected PV power fluctuations. This study proposes a HESS capacity optimal allocation method considering the grid-connected PV requirements. Firstly, based on the power fluctuation requirements in the PV power station grid-connected regulations, the maximum power point tracking working point switching control is performed for the PV power station, from which the grid-connected PV power and HESS power are obtained. Then, a capacity optimal allocation method and frequency division energy management strategy (EMS) for HESS is proposed to find the energy response and power response of each energy storage source. Furthermore, a multi-objective optimization function with HESS cutoff frequency as the independent variable is constructed, and the input cost of HESS and the life loss of the lithium battery are optimized. Finally, the overall strategy is compared and analyzed under the scenarios of three typical PV power fluctuations. Simulation results show that the control strategy has a good smoothing effect on PV power fluctuations. From the perspective of the annual comprehensive input cost, HESS realizes the optimal capacity allocation when the cutoff frequency is 0.0066 Hz.

Research on Global Optimal Energy Management Strategy of Agricultural Hybrid Tractor Equipped with CVT

This paper presents a proposed global optimal energy management strategy based on dynamic programming to enhance the energy consumption efficiency of an agricultural hybrid tractor that is equipped with a continuously variable transmission (CVT). Firstly, using a diesel-electric parallel agricultural hybrid tractor as the research object, a tractor-rotary tillage coupling dynamics model is constructed. Secondly, with the torque and speed of the motor, the torque and speed of the diesel engine, and the CVT speed ratio as the control variables, the state of charge (SOC) of the power battery as the state variable, and the goal of minimizing the total energy consumption of the whole machine, a global optimal energy management model based on dynamic programming is established. Finally, the field operation measured data is injected into the MATLAB simulation model, and experiments are carried out to verify the effectiveness of the energy management strategy. The results show that compared with the power-following energy management strategy, the proposed energy management strategy can make the diesel engine and electric motor work in the optimal area, and effectively reduce the total cost of energy consumption of the tractor during field operations. Under the condition of rotary tillage, the total cost of energy consumption is decreased by 16.89%.

A new optimal energy management strategy of microgrids using chaotic map‐based chameleon swarm algorithm

AbstractThis study provides an optimal and efficient energy management strategy (EMS) for the cost‐effective performance of a combined solar and green energy microgrid in both independent and grid‐connected modes. A microgrid is formed by the system that includes a fuel cell (FC), a battery storage (BS), and a photovoltaic system (PV). Evidently, the unguaranteed features of the renewable energy and load electricity generate instability problems as well as economic ones, like operational expenses. To tackle these issues, a novel procedure is proposed that has been improved by a modified metaheuristic algorithm, called chaotic map‐based chameleon Swarm Algorithm (CCSA). In this method, the simulation is based on a one‐day planning perspective. The method aims to supply the power requirements of the load at the lowest possible cost under a constant DC bus voltage, protect the battery from overcharging and depletion, and improve the efficiency of the total system. To illustrate the suggested method's effectiveness, the simulation results of CCSA are compared with some studied methods in the literature, including GAMS, bald eagle search optimization algorithm (BEOA), original chameleon swarm algorithm (CSA), and grey wolf optimization algorithm (GWOA).

Cyber-Physical Optimization-Based Fuzzy Control Strategy for Plug-in Hybrid Electric Buses Using Iterative Modified Particle Swarm Optimization

The fuel economy of plug-in hybrid electric bus (PHEB) is highly dependent on its energy management strategy (EMS). In practice, the fuzzy control (FC) is widely used in EMS due to its real-time performance and robustness. However, the FC with fixed parameters is difficult to obtain the optimal fuel economy under changing traffic conditions. Regarding this, the control parameters of FC need to be optimized, but this scheme needs to overcome the subsequent calculation burden and time consumption. Therefore, the design of a real-time EMS with parameter optimization is a challenging problem. Inspired by this issue, a cyber-physical optimization-based fuzzy EMS is proposed in this paper. Firstly, a cyber-physical system framework is formulated for PHEB to eliminate the conflict between parameter optimization and real-time operation of EMS. Secondly, considering the uncertainty of the vehicle environment, an IT2 FC with optimization parameters is designed for real-time torque allocation. Thirdly, an iterative modified particle swarm optimization (IMPSO) algorithm is proposed to optimize parameters to accurately and quickly converge to the optimal solution. Additionally, the optimization problem with multi-objective that takes battery life into account is introduced. Finally, simulation and hardware in loop test are used to discuss the performance of the proposed EMS. The results reveal that the IMPSO algorithm can improve the optimization effect. Compared to conventional rule-based and fuzzy-based strategies, the proposed EMS can reduce fuel consumption at least 10% and 4.5%, respectively. Meanwhile, it shows the proposed EMS could reduce the battery capacity loss by 6.42%∼9.72% with a slight increase in fuel consumption.

Realistic performance evaluation and optimal energy management of a large-scale bifacial photovoltaic system

This paper introduces an optimal energy management and a comprehensive innovative comparison between on-grid monofacial and bifacial solar photovoltaic systems in terms of energy production, reliability, economic feasibility, ecological impact, footprint area, and other performance indicators. This is to satisfy the load demand of Al-Husainya city, Jordan. Realistic measured data are obtained from authoritative companies in Jordan. This includes ambient temperature, solar irradiance, and a utility-scale load demand. The optimal sizing and energy management strategy are achieved using Marine Predators optimization algorithm. This is done by minimizing Loss of Power Supply Probability. The decision variable is the number of photovoltaic modules. The unit investigation shows that the maximum power of the bifacial panel is increased compared with a monofacial module. So, this will highly affect system size and hence reliability, footprint required area, cost and ecological indicators are impacted too. Results show that the minimized values of the objective function of Loss of Power Supply Probability for monofacial and bifacial systems are 0.3534 % and 0.2511 %, respectively. The number of monofacial and bifacial panels are 12,940 and 11,584 respectively. In addition, the area required to install the bifacial array is decreased by 10.4791 % compared with the monofacial array. Finally, four additional optimization algorithms were used to validate and compare with the obtained results using the Marine Predators algorithm. The methodology described in this paper, which adopts the bifacial photovoltaic technology, is recommended and it can be applied anywhere to get highly reliable, feasible, and more environmentally friendly solar energy systems.

Optimization of Hybrid Energy Storage System Sizing With Considering Energy Management Strategy for High-Power Pulsed Load in Aircraft

High-power pulsed load (HPPL) such as airborne laser weapon and radar poses a severe challenge to aircraft electrical power system. Since peak power cannot be satisfied only by generator, hybrid energy storage system (HESS) should be well sized to match their energetic performances versus instantaneous power requirement and improve reliability of electrical power system. To deal with these challenges, a sizing optimization for HESS with considering energy management strategy (EMS) based on virtual-droop control in aircraft is proposed in this paper. By analyzing the spectrum characteristics of HPPL profile, splitting frequency between different types of energy storage devices is specially analyzed and designed to give full play to HESS. HESS weight, loss and parameters in EMS are taken into account in this multi-objective optimization based on Non-dominated Sorting Genetic Algorithms II (NSGA-II). Series of Pareto fronts can be obtained through the optimization process, and the most suitable solutions of different HESS combinations are selected by a decision-making means which combines subjective and objective weighting methods. Finally, optimization results of four different combinations are compared. Simulation results are obtained in MATLAB/Simulink environment to prove the rationality and applicability of the optimal sizing tool. The research has resulted in a solution of HESS and EMS optimization for HPPL used in aircraft.

Collaborative Optimization of Energy Management Strategy and Adaptive Cruise Control Based on Deep Reinforcement Learning

Hybrid electric vehicles (HEVs) have great prospects in reducing fossil fuel consumption, and adaptive cruise control (ACC) technology provides safe and convenient travel for drivers. The fusion of the two technologies can theoretically improve the safety, comfort, and fuel economy of vehicles. Hence, the energy management strategy (EMS) of Prius, a typical HEV configuration, is studied under the car-following scenario. This optimization problem involves complex systems, inconsistent objectives, and stringent constraints, which may be challenging to conventional algorithms. Therefore, a novel deep deterministic policy gradient (DDPG)-based ecological driving strategy (DDPG-ECO) is proposed and the weights of multiple objectives are analyzed to optimize the training results. The extensive simulation experiment compares the effects of Ornstein-Uhlenbeck action noise (OUAN) and soft-max action noise (SAN), which act on the acceleration action. Simulations under different driving cycles show that the fuel economy of DDPG-ECO can achieve more than 90% of dynamic programming (DP)-based methods on the conditions of ensuring car-following performances.

A Hierarchical Strategy for Restorative Self-Healing of Hydrogen-Penetrated Distribution Systems Considering Energy Sharing via Mobile Resources

The self-healing ability of distribution systems (DSs) against extreme events can be significantly boosted by strategically dispatching mobile emergency resources (MERs). In recent years, the penetration of hydrogen energy in energy systems has increased dramatically. Hydrogen energy and its related infrastructure, such as hydrogen refueling stations (HRSs) and mobile fuel cell vehicles (MFCVs), exhibit great potential for enhancing the self-healing ability of DSs. In this work, a two-layer hierarchical strategy for improving the restorative self-healing ability of hydrogen-penetrated DS (HPDS) with multiple microgrids (MGs) and HRSs is proposed considering their interactions and MERs such as mobile energy storages and MFCVs. In the lower layer, an optimal energy management strategy is presented for MGs considering the coordination between hydrogen and electricity via the electrolyzer and fuel cell in the HRSs. In the upper layer, an energy sharing (ES) strategy is proposed for the distribution system operator (DSO) to dispatch MERs in order to minimize the power imbalance among MGs and reduce the unserved load. Since MERs can be dispatched for many times to swap energy and hydrogen between MGs or HRSs, the concept of the equivalent time network (ETN) is introduced, and based on the ETN, a mixed integer linear programming (MILP) model is innovatively proposed to represent the traveling pattern of MERs and to optimize their ES path among MGs. Finally, the two-layer hierarchical strategy is formulated as MILP models and solved using the commercial solver. Case studies on a modified IEEE 33 bus distribution system (DS) and a regional DS in China verify the effectiveness of the proposed strategies.

Design and Field Implementation of a Hierarchical Control Solution for Residential Energy Storage Systems

This paper presents an innovative approach to the design and real-life field implementation of a hierarchical control solution for a residential ESS (energy storage system) for consumers/prosumers. The proposed control solution minimises residential prosumers’ electricity bill. It consists of an offline controller (secondary level) implemented in the cloud, which works in total synergy with a local real-time controller (primary level) at the ESS. The offline controller proposes an optimal day-ahead energy management strategy using the forecasted demand and PV generation; whereas the local real-time controller is responsible for the management strategy in the best suitable way while dealing with the system real-time constraints and forecast uncertainties. Distributed implementation and demonstration of the solution have been performed through a cloud control platform, operated by an aggregator. Moreover, an offline analysis has been carried out to assess the performances of the real-life implementation outputs and verify the effectiveness of the proposed approach. The obtained results show promising performances of the overall system and implementation approach. This will further ease the integration process of consumer/prosumer with the future virtual power plant or local electricity market operator. The main concept has been developed to be implemented within the StoreNet pilot project platform.

Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles

This article investigates the economic viability of replacing the high-voltage battery pack of a power-split hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) by estimating the impact of battery aging on the fuel economy, drivability capability, and electric range. The HEV is modeled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. The battery pack payback period is estimated by assessing the vehicle operative costs in terms of fuel and electricity as obtained through numerical simulations as a function of battery aging. Replacing the battery pack at the conventional end-of-life limit of 80% residual capacity is suggested not to be convenient from an economic standpoint for both the HEV and the PHEV. On the other hand, acceptable payback periods (i.e., 2–5 years) can be achieved for the battery pack when being replaced at 20% to 40% residual capacity. The proposed methodology can be implemented to advise an HEV or PHEV user regarding the benefit of replacing the battery pack due to excessive aging.

A data‐driven scheduling approach for integrated electricity‐hydrogen system based on improved DDPG

AbstractThe involvement of hydrogen energy systems has been recognised as a promising way to mitigate climate problems. As a kind of efficient multi‐energy complementary system, the hydropower‐photovoltaic‐hydrogen (HPH) system could be an ideal approach to combining hydrogen with an installed renewable energy system to improve the flexibility of energy management and reduce power curtailment. However, the intra‐day scheduling of HPH system brings challenges due to the time‐related nonlinear hydropower generation process, the complex energy conversion process and the uncertain natural resource supply. Faced with these challenges, an improved deep deterministic policy gradient (DDPG)‐based data‐driven scheduling algorithm is proposed. In contrast to the prevalent DDPG, two sets of actor‐critic networks are properly designed based on prior knowledge‐based deep neural networks for the considered complex uncertain system to search for near‐optimal policies and approximate actor‐value functions. In addition, customized reward functions are proposed with the consideration of interactions among different energy supplies, which helps to improve convergence speed and stability. Finally, the case study results demonstrate that the proposed system model and the optimal energy management strategy based on the improved DDPG algorithm can guide the electricity‐hydrogen system to achieve rapid response and more reasonable energy management.

Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm

This study aims to improve the fuel economy of extended range electric vehicles (EREVs) and reduce the cumulative battery workload. Energy management strategy (EMS) of EREVs has a significant impact on improving the energy efficiency, prolonging the service life of batteries, and reducing the fuel consumption. To the best knowledge of the authors, most of existing studies are aimed at optimizing fuel economy, but few researches have taken the service life of the battery into account while improving fuel economy. Our study reflects the power fluctuation range of the battery from the perspective of the battery current, and also further analyzes it from the perspective of the battery energy flow. On the premise of meeting the vehicle power requirement, matrix calculation studies are carried out on the transmission ratio and the key parameters of EMS in the cooperative operation mode of hybrid energy storage system (HESS) based on regular EMS and auxiliary power unit (APU) based on equivalent fuel consumption minimum strategy (ECMS). Via Simulink software, the corresponding vehicle EMS model is developed, and the joint simulation platform of AVL Cruise and Simulink is constructed to verify the effectiveness of the proposed EMS. In order to further tap the energy-saving potential of the proposed strategy based on HESS, the multi-island genetic algorithm is adopted. Under WLTP working condition, the global optimization is conducted with the objectives of minimizing the equivalent fuel consumption and the cumulative ampere-hour through the battery when the vehicle adopts HESS and APU & ECMS operate in concert operation mode. The correlation analysis of the optimization variables is performed. The results show that the fuel economy of the optimized operation mode under WLTP condition is increased by 4.49 %, and the cumulative ampere-hour through the battery is reduced by 11.37 %.