Proposed Energy Management Method Research Articles

Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning

The recently emerging multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization, and its energy management requires multiple-input and multiple-output (MIMO) control. At the present, the existing methods usually decouple the MIMO control into single-output (MISO) control and can only achieve its local optimal performance. To optimize the multi-mode vehicle globally, this paper studies a MIMO control method for energy management of the multi-mode PHEV based on multi-agent deep reinforcement learning (MADRL). By introducing a relevance ratio, a hand-shaking strategy is proposed to enable two learning agents to work collaboratively under the MADRL framework using the deep deterministic policy gradient (DDPG) algorithm. Unified settings for the DDPG agents are obtained through a sensitivity analysis of the influencing factors to the learning performance. The optimal working mode for the hand-shaking strategy is attained through a parametric study on the relevance ratio. The advantage of the proposed energy management method is demonstrated on a software-in-the-loop testing platform. The result of the study indicates that the learning rate of the DDPG agents is the greatest influencing factor for learning performance. Using the unified DDPG settings and a relevance ratio of 0.2, the proposed MADRL system can save up to 4% energy compared to the single-agent learning system and up to 23.54% energy compared to the conventional rule-based system.

A Risk-Averse Adaptive Stochastic Optimization Method for Transactive Energy Management of a Multi-Energy Microgrid

This paper proposes a new energy management method for a multi-energy microgrid (MEMG) which supplies both electrical and thermal energies. Based on the transactive energy (TE) concept, the problem is formulated as a Stackelberg game-theoretic bi-level optimization model. The MEMG operator optimizes the energy scheduling and pricing strategies at the upper level, and the industrial, commercial and residential agents optimize their energy trading strategies at the lower level. The equivalent single-level mixed-integer linear program (MILP) reformulation is then derived for computational tractability. To coordinate the strategies made in the day-ahead and intra-day energy markets, an adaptive stochastic optimization (SO) approach is adopted, by which a day-ahead stochastic MILP and an intra-day deterministic model are formed. Furthermore, the conditional value-at-risk (CVaR) measure is incorporated in the day-ahead stage for risk aversion of the MEMG operator towards uncertainties. To solve the models, an adaptive Progressive Hedging (PH) algorithm is developed to decompose the day-ahead stochastic MILP into multiple scenario-based subproblems which can be solved in parallel, and an outer approximation (OA) algorithm is adopted in the intra-day stage to linearize the bilinear objective function. Finally, the simulation results verify the effectiveness of the proposed energy management method and the efficiency of the solution algorithms.

A multi-objective honey badger approach for energy efficiency enhancement of the hybrid pressure retarded osmosis and photovoltaic thermal system

The pressure retarded osmosis process is emerging as one of the most promising technologies for extracting renewable energy from salinity gradient resources. The hybrid system of the pressure retarded osmosis and photovoltaic/thermal systems is capable of effectively improving overall energy efficiency. The prime research focus of hybrid energy systems is to produce environment-friendly solutions by minimizing energy consumption and improving energy efficiency. Due to the increasing demand for intelligent computing, the challenges of maximum power extraction and efficient energy management have attracted the attention of researchers around the world. Thus, a Honey Badger Approach based energy management strategy is proposed to solve the problem of performance enhancement in the pressure retarded osmosis and photovoltaic/thermal system by maximizing the multi-variable functions. The Honey badger approach uses global sight to tackle the optimization of multiple objectives and to achieve optimal solutions. The proposed energy management method is tested with various types of operational environments, including variations in the operational temperature, solar irradiance and ambient temperature in regard to detrimental effects. The practical hourly data of Perth for a year is utilized to simulate the actual operating conditions. The simulation results indicate that the proposed energy management method overwhelms other competing approaches with better energy efficiency, thereby ensuring the continuity of energy supply and improving the reliability of the system.

Efficiency Focused Energy Management Strategy Based on Optimal Droop Gain Design for More Electric Aircraft

Due to the substantial increase in the number of electrically driven systems onboard more electric aircraft (MEA), the onboard electric power systems (EPSs) are becoming more and more complex. Therefore, there is a need to develop a control strategy to manage the overall EPS energy flow and ensure the operation of safety-critical systems (which are electrical loads) under different operating scenarios and to consider EPS losses minimization, exploiting the thermal capability of generators, different load priorities, and available batteries with their charging and discharging schedules. This article presents an energy management (EM) strategy that considers the aforementioned objectives. The optimal droop gain approach is employed as a power-sharing method to minimize the total EPS losses in MEA. A finite state machine (FSM) has been used to implement the control strategy to realize the EPS reconfiguration operation. The proposed EM strategy is implemented and simulated using MATLAB/Simulink and hardware-in-the-loop (HIL) under different operational scenarios, such as normal operations, failure of one of the power generation channels, and failure of all power generation channels. The proposed EM method has shown its capability to efficiently manage the EPS under different operating conditions to reduce the overall system losses.

A two-stage energy management for heat-electricity integrated energy system considering dynamic pricing of Stackelberg game and operation strategy optimization

Considering the flexibility and adjustability value of integrated energy system (IES) with flexible energy units and multivariate adjustable load in urban energy market, this paper proposes a two-stage energy management method of heat-electricity integrated energy system (HE-IES) considering dynamic pricing of Stackelberg game and operation strategy optimization. Firstly, a general comprehensive energy efficiency considering the exergy properties of electric and thermal energy is established, on this basis, this paper put forward a two-stage energy management framework considering interactive relationship between energy service provider (ESP) and users. Secondly, a two-stage energy management model is established to improve the energy efficiency and operation economy of the system, which includes the multi-objective optimization model of day-ahead scheduling and Stackelberg game dynamic pricing model of real-time energy management based on the source-load interaction optimization for hybrid energy. Finally, in order to illustrate the effectiveness of the proposed energy management method in improving system energy efficiency and operation strategy, a typical HE-IES consisting of 6 users and 1 ESP is chosen to simulation. The simulation results demonstrate that the proposed approach can not only enhance renewable energy utilization and reduce the cost of both ESP and users, but helpful to promote the benign interaction between energy equipment and load in IES, which could improve the energy efficiency of system operation.

Energy Management Method of Electricity Heat Hydrogen Multi-Coupling System for Retired Power Battery Echelon Utilization in Microgrids

In order to effectively make up for the defect of service life of re-tired power battery echelon used in microgrids (MGs) and improve the reliability of MGs system, an energy management method based on the energy hub of the electricity heat hydrogen multi-energy storage system (EHH-MESS) is proposed the in the paper. Firstly, considering the uncertain factors such as energy output, load fluctuation, retired power battery life defects and multi energy flow impact, MGs supply-demand balance model is built. Secondly, taking the economy of energy supply as the goal, a control method based on energy hub is proposed, which considers the weight of multi-objective optimization to control MGs. Finally, the effectiveness of the proposed energy management method is verified by simulation analysis, which improves the power supply reliability and economy of the system.

A novel energy management method based on Deep Q Network algorithm for low operating cost of an integrated hybrid system

For minimizing the operational cost of an integrated hybrid system, which provides electricity, heating, cooling and water of a residential complex, a new energy management method presented here. The proposed energy management method uses the principles of Reinforcement Learning (RL) based on Deep Q Network (DQN) algorithm to find the operational strategy for the hybrid system. Unlike the stochastic presentation of RL with Markov Decision Process (MDP) in literature, a deterministic approach proposed in this study for energy management of a hybrid system. This presented approach does not require online trial and error or historical data of system performance to determine the operational strategy. This developed RL-DQN method used for energy management of a hybrid system with photovoltaic collectors, ET collectors, wind turbines, CHP units, a water heater, a hot water storage tank and batteries. Thus for a residential complex with variable electrical and thermal loads during a year (8760 h), RL-DQN energy management method estimated two unknown variables in operational strategy, i.e., the partial load of the CHP unit and the amount of heat generation by LPG-fueled water heater. The results of RL-DQN energy management also compared with results of two rule-based methods. The results show a reduction of the operating cost to 10983 $/year by RL-DQN which is lower 6.5% and 30.4% than that for two above mentioned rule-based methods. Lower CO2 production (2.8% and 62.8%) also observed in comparison with that for two above mentioned rule based methods. With the proposed EM method, 80.1% of the required electricity supplied with renewable energies and 82.2% of the thermal load supplied without direct consumption of fossil fuels. The benefit of using predicted load and renewable energies of future hours also investigated for EM. Results show that employing the predicted information decrease the operational cost by about 3.5 percent

Research on Energy Recovery Method of Virtual Track Train Based on Working Conditions Segmentation Strategy

In order to improve the efficiency of energy recovery of hybrid virtual track train, an energy management method based on the strategy of working condition segmentation is proposed under the condition of known line information. This method adjusts the power allocation of power battery and supercapacitor in different power modes of virtual track train. Based on the environment of MATLAB/Simulink, a simulation system for energy recovery of virtual track train is developed. Then this paper carries on the simulation analysis. The simulation results show that the proposed energy management method can achieve more efficient energy recovery under the condition of meeting the train power demand. After optimization, the power output of the train's power battery is reduced by 9.9%, and the recovery rate of the train's braking energy is increased by 9.3%.

Energy Management of Hybrid Diesel/Battery Ships in Multidisciplinary Emission Policy Areas

All-electric ships, and especially the hybrid ones with diesel generators and batteries, have attracted the attention of maritime industry in the last years due to their less emission and higher efficiency. The variant emission policies in different sailing areas and the impact of physical and environmental phenomena on ships energy consumption are two interesting and serious concepts in the maritime issues. In this paper, an efficient energy management strategy is proposed for a hybrid vessel that can effectively consider the emission policies and apply the impacts of ship resistant, wind direction and sea state on the ships propulsion. In addition, the possibility and impact of charging and discharging the carried electrical vehicles’ batteries by the ship is investigated. All mentioned matters are mathematically formulated and a general model of the system is extracted. The resulted model and real data are utilized for the proposed energy management strategy. A genetic algorithm is used in MATLAB software to obtain the optimal solution for a specific trip of the ship. Simulation results confirm the effectiveness of the proposed energy management method in economical and reliable operation of the ship considering the different emission control policies and weather condition impacts.

Control and energy management of hybrid system based on a fuel cell/photovoltaic/super capacitor using an AC-link inverter connected to the grid

This article deals with the power flow control in a hybrid power system, which is composed of a fuel cell, photovoltaic system and a super capacitor. The super capacitor has been employed with an aim to ameliorate the performance of the hybrid power system. In addition, it takes into account the energy fluctuations of photovoltaic energy sources and the slow dynamic of the fuel cell. Each source is connected to a DC/DC converter and their outputs are connected to a common DC-link and they supply a DC load. Furthermore, an energy management algorithm has been carried out in order to commonly share the power between the sources and the load. The proposed energy management method shows its flexibility, and an efficient energy conditioning between different sources is guaranteed in case of multi-source system. The main originality of this work lies in the use of AC-link partial resonant DC–AC converter topology to interface hybrid system and to inject the excess of energy to the grid. In this article, the system description, the control and the modeling of DC/DC converters and the AC-link converter are provided. Finally, simulations under Matlab/Simulink are shown in order to validate the effectiveness of the proposed control strategy and the energy management algorithm. An implementation on real-time using dSpace 1104 is presented to illustrate the feasibility of the proposed control strategy.

Hysteresis‐based energy management strategy for a microgrid with controllable heating loads

Hysteresis-based energy management strategy for microgrid containing photovoltaic, ESS and heating loads is proposed in this study. In this real-time optimisation method, economic cost, operation cost, comfort level, renewable energy penetration and other performance indices are optimised in real time. To solve the complex energy management problem, when power forecasting is unavailable, different operational cases are clarified and calculated accordingly. In this way, the management process is turned into a finite-solution problem. Furthermore, a hysteresis loop is also established based on the cost function to optimise the system continuously, of which the loop width is offline designed. As a result, the computation burden is reduced. Finally, simulation results are presented to verify the effectiveness of the proposed energy management method. The application of the proposed method can reduce the economic cost and improve the operational performances of a microgrid significantly, including comfort, power quality and health of ESS.

Regional integrated energy system energy management in an industrial park considering energy stepped utilization

There are multiple energy demands in industrial parks. The industrial park’s energy system includes a variety of energy sources and energy-consuming equipment, with diverse load types and high reliability requirements for power supplies. And the situation of low energy utilization rates, unreasonable energy structures, great peak-to-valley power differences and the environment pollution needs to be improved. The application of multi-energy complementary regional integrated energy systems (RIES) can improve the performance of the industrial parks. Considering reasonable correspondence between the energy supply and demand in RIES, this paper proposes an RIES energy management strategy based on energy stepped utilization to further minimize the daily cost and make full use of the energy. Additionally, the piecewise linear model of gas turbine is established considering a part load ratio. According to the grade demands, the heat loads are divided into high-grade, middle-grade and low-grade heat loads and the load models are respectively established. The scenario reduction method is used to obtain typical scenarios in order to describe the randomness of weather factors. Finally, the simulation analysis shows that the proposed energy management method for the RIES can arrange the combination of gas turbines and output of devices more flexibly, and a more economical scheduling plan can be provided by this method. The high-grade and middle-grade energy are not only supplied to corresponding loads, but also can be converted into low-grade energy. Additionally, the stability and energy efficiency of the system are improved with the application of this strategy.

Reliable Power Scheduling of an Emission-Free Ship: Multiobjective Deep Reinforcement Learning

Environmental pollutants, as a global concern, have led to a general increase in the utilization of renewable energy resources instead of fossil fuels. Accordingly, the penetration of these resources in all-electric ships, as well as power grids, has increased in recent years. In this article, in order to have a zero-emission and cost-effective energy management in an all-electric ferry boat, a new reliable and optimal power scheduling is presented that uses fuel cell and battery energy storage systems. Furthermore, the real information including load profile and paths is considered for the case study to assess the feasibility and superiority of the proposed approach. In addition to the cost of energy management, to have a reliable combination of the proposed resources, the loss of load expectation (LOLE) as a reliability index is considered in the energy management context and the problem is solved by the deep reinforcement learning in a multiobjective manner. The results of the consideration of two common standards, including DNVGL-ST-0033 and DNVGL-ST-0373, demonstrate that the proposed energy management method is applicable in industrial applications. Finally, the real-time simulation-based hardware-in-the-loop (HIL) is conducted to validate the performance and efficacy of the suggested power scheduling for the emission-free ships.

A real-time energy management method for electric-hydrogen hybrid energy storage microgrid based on DP-MPC

With increasing presence of intermittent energy resource in microgrid, it is so difficult to precisely predict the output of renewable resources and the load demand. In order to realize the economical operation of the system, an energy management method based on model predictive control (MPC) and dynamic programming (DP) algorithm is proposed. This method can reasonably distribute the energy of battery, fuel cell, electrolyzer and external grid, and maximize the output of distributed power supply while ensuring the power balance and cost optimization of the system. Based on the ultra-short-term forecast, the output power of photovoltaic array and the demand power of system load are predicted. The off-line global optimization of traditional dynamic programming is replaced by the repeated rolling optimization in a limited period of time to obtained power values of each unit in the energy storage system. Compared with the traditional DP, MILP-MPC and the logic based real-time management method, the proposed energy management method is proved to be feasible and effective.

Analysis of Energy Management in Micro-grid with Distributed Generation System

The Energy management in Micro-Grid system is one of the most irrefutable issues in micro grid technology development. The Micro grid energy system consists on non-conventional resources, unit with storage energy and islanded or grid connected mode of operating loads. This tabloid presents a Micro-Grid energy management system for distributed generation connected MG with hybrid energy storage system. Hybrid energy storage system is achieved by combining the Ultra-capacitor and battery storage. The proposed energy management system in micro grid with hybrid energy storage system follows the two techniques, such as forecasting and optimization. Prediction of solar irradiance, load demand and temperature are coming under the Forecasting technique. For the economical operation in a grid connected micro grid the optimal day-ahead scheduling of power generation and load demand is performed by the optimization technique. The MG will absorb or deliver the prespecified amount of power. In this method MG connected to the power grid in large numbers are integrated. The main intension of the energy management system is power balancing in the micro grid. The MATLAB/Simulink environment has been used to analyzing the effect of Energy management in micro-gird with hybrid energy storage system. The simulation results will show the effective and stoutness of the proposed energy management method.

Optimal Hierarchical Management of Shipboard Multibattery Energy Storage System Using a Data-Driven Degradation Model

The lifetime of shipboard energy storage systems (ESSs) has great impacts on the operating cost of all-electric ships (AESs) since their high investment costs. Additionally, those ESSs are designed to have multiple battery packs with high capacity redundancy to cope with various navigation scenarios and distributed in different electric zones onboard to avoid operating risks. To extend the battery lifetime while fully addressing the redundant capacity and distributed locations, an optimal hierarchical management model for shipboard multibattery ESS is proposed, which consists of three levels. In the first level, a practical quadratic degradation cost model is reformulated from a nonlinear data-driven model, which considers both the depth of discharge (DoD) and mean state of charge (MSOC). Then, in the second level, the obtained quadratic model is implemented into the shipboard generation scheduling that views the multibattery ESS as a “single battery pack. ”In the third level, a multibattery management strategy is implemented to split the “single battery pack’s” power to each battery group by iteratively limiting their MSOCs. To bring the proposed energy management method into practical usage, a real-time simulation experiment is designed to test its validity. The case study shows that the proposed method is suitable for real-time usage and reveals that iteratively regulating the MSOC of batteries will greatly facilitate their lifetime. As a result, the operating cost of AESs can be reduced due to battery lifetime extension.

Novel energy management method for suppressing fuel cell degradation in hydrogen and electric hybrid energy storage systems compensating renewable energy fluctuations

This research investigates an energy management method utilized in a hydrogen and electric hybrid energy storage system (HESS), which is utilized as an ancillary system for renewable energy electricity generation. To suppress the performance degradation of the fuel cell (FC), the newly proposed energy management method deals with main FC degradation causes, such as low humidification and frequent and rapid voltage changes. The entire HESS's performance is demonstrated using the proposed energy management method. In addition, a simulation is conducted to evaluate the proposed energy management method's performance in terms of both suppressing the FC's degradation and ensuring system efficiency. The results of the experiment and simulation show that the proposed energy management method can suppress the FC's harmful working states while maintaining high system efficiency.

Designing a financial-based energy management framework in smart public parking lot

This study develops an energy management framework for a large number of vehicle-to-grid (V2G)-capable electric vehicles (EVs) parked at a parking lot, and its main objective is to maximise the parking lot operator's total profit from electricity sales. Meanwhile, besides completely fulfilling the energy requirements of EV owners, the proposed method considers the operational requirements of the power grid. In this regard, the authors first propose an electrical energy exchange model in which the parking lot operator purchases electrical energy directly from the wholesale market and stores it in the batteries of EVs and feeds part of this stored energy back into the grid through V2G capability during some specific time periods. After that, based on the presented energy exchange model, a linear optimisation framework involving the necessary financial considerations is proposed for the energy management problem. Finally, numerical studies are carried out to show the efficiency of the proposed energy management method from the viewpoints of different players.

Energy Management System for Stand-Alone Wind-Powered-Desalination Microgrid

An energy management system for stand-alone microgrid consisting of a wind turbine (WT) generator, a diesel generator, an energy storage system (ESS), and a sea water desalination system is proposed in this paper. The coordinated control of the distributed generations and ESS is researched with two operation modes. Then, a real-time rolling horizon energy management method is presented based on hour-ahead wind speed forecast. The operation mode of the microgrid system and the reference output power of WT generator are determined according to the forecasted wind speed and state of charge of the ESS, which can achieve the goal of maximizing utilization of wind energy and minimizing utilization of diesel generator on the basis of system stable operation. The proposed energy management method has been tested on the real-time digital simulator system. The results clearly verify the effectiveness of the proposed method.