Hierarchical Energy Management Research Articles

Energy Scheduling Framework of Micro-Grids Considering Battery Lifetime

The stochastic and intermittent nature of renewable energy poses grave challenges to the energy scheduling of micro-grids. The exploitation of batteries has been considered as an effective way to cope with such challenges. However, fluctuation in renewable process causes batteries to charge/discharge frequently, reducing the lifetime and increasing the maintenance cost. This paper presents a hierarchical two-layer energy management, a combination of battery and supercapacitor, to lessen daily energy costs. The upper layer is equipped with a battery to minimize the economic cost, optimizing the energy usage by employing a mixed-integer nonlinear programming (MINLP) model; the lower layer is equipped with a supercapacitor to treat the uncertain nature of renewable energy. The optimization process includes load shifting and battery degradation costs. The upper layer determines the optimal schedule of interruptible appliances and the profile for energy storage for the next 24 hours. The schedule results are then passed to the lower layer, which deals with the forecast uncertainties with a supercapacitor with a 15 minutes interval. The effectiveness of the proposed algorithm is examined by a single-layer scheduling system without forecast errors and a two-layer scheduling system with forecast errors. The obtained results show the capability and effectiveness of the proposed scheduling system to reduce operating costs and forecast errors.

Adaptive Power Management of Hierarchical Controlled Hybrid Shipboard Microgrids

Shipboard microgrids (SMGs) are distinguished by the heavy propulsion system that can vary largely in a short time. Consequently, this variation shifts the optimum operating points of the diesel engines that leads to increase the overall emissions and operational costs. Moreover, power fluctuations caused by the dynamic loads such as propulsion motors along with the lack of cold-ironing facilities at both ends of seaports make it even worse. Therefore, the application of energy storage systems (ESSs) with proper coordination is becoming very popular for ships to improve the energy management, and thus decreases the fuel consumption. The aim of this paper is to firstly highlights different architectures of SMGs and the benefits the ESS can brings into them, then proposes an enhanced hierarchical control-based energy management scheme that is suitable for SMGs operations during an islanded and grid-connected operation. The proposed method based on the ESSs supports the diesel generators to enable them to operate in the optimum window recommended by the diesel engines company, which significantly decreases fuel consumption, operational costs, and emissions. Furthermore, to provide a linkage between SMG and the grid during port stays, conventional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P-f$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q-V$ </tex-math></inline-formula> droop control strategy is adopted to import and export power to the seaport load or the grid for emergency purposes referred to in this study as Ship-to-X operation. The enhanced hierarchical control is capable of optimally shifting the modes for efficient and reliable operation and reducing specific fuel consumption. The performance of the proposed scheme is adopted and validated with satisfactory results of a practical hybrid SMG in a MATLAB/SIMULINK environment.

Hierarchical Energy Management System With a Local Competitive Power Market for Inter-Connected Multi-Smart Buildings

The energy management in new distribution paradigms are amongst one of core research dimension, particularly in smart grids. This paper proposes a hierarchical energy management system for inter-connected multi-smart buildings with an inclusion of local Power Market. As home appliances have huge contribution in load of buildings, the appliances are scheduled in order to minimize operational cost while taking into account the user comfort and other system constraints. The objectives of this paper aim to minimize operational cost, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, grid dependency while maximize user comfort and revenue. The proposed technique enables a prosumer with two options, either they can sell excess energy to the utility or can bid and sell in market with high price compare to utility. Besides increase in revenue, the consumer is enabled to buy electricity from utility or from local market with low prices compare to utility grid aiming at reducing operational cost. The proposed framework is evaluated across three algorithms namely, JAYA, teacher learning based optimization (TLBO) and Rao1, respectively. As per comparative analysis, the JAYA algorithm outperforms the others in achieving the aimed objectives in-terms of favorable achieved numerical values. Different cases are created in order to test the effectiveness of proposed system. The overall simulation results validate the proposed approach with highest operational cost reduction of 151.48%, peak load reduction 76.76%, grid dependency reduction 95.61%, and minimum emission of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is 3.70 Kg/Day as compare to base case.

Velocity Optimization and Robust Energy Management of Connected Power-Split Hybrid Electric Vehicles

AbstractThis paper focuses on an eco-driving-based hierarchical robust energy management strategy for connected and automated hybrid electric vehicles in the presence of uncertainty. The proposed control strategy includes a velocity optimizer, which evaluates the optimal vehicle velocity, and a powertrain energy manager, which evaluates the optimal power split between the engine and the battery in a hierarchical framework. The velocity optimizer accounts for regenerative braking and minimizes the total driving power and friction braking over a short control horizon. The hierarchical powertrain energy manager employs a long- and short-term strategy where it first approximately solves its problem over a long time horizon (the whole trip time in this paper) using the traffic data obtained from vehicle-to-infrastructure (V2I) connectivity. This is followed by a short-term decision maker that utilizes the velocity optimizer and long-term solution, and solves the energy management problem over a relatively short time horizon using robust model predictive control (MPC) methods to factor in any uncertainty in the velocity profile due to uncertain traffic. We solve the long-term energy management problem using pseudo-spectral optimal control method, and the short-term problem using robust tube-based MPC method. Simulation results with standard driving cycle velocity profile and real-world traffic data show the competence of our proposed approach. Our proposed co-optimization approach with long- and short-term solution results in ≈12% more energy efficiency than a baseline co-optimization approach.

Hierarchical energy management for community microgrids with integration of second‐life battery energy storage systems and photovoltaic solar energy

It is recognized by academia and industry that second-life batteries retired from electric vehicles still have use values and can be effectively used for supporting less demanding applications. At present, there lacks investigation on the applications of re-using retired batteries on serving residential sector's energy management. Motivated by this, this paper studies the scenario of assembling retired batteries to be second-life battery energy storage systems (SL-BESSs) and using them to serve the energy demand of residential communities in an affordable manner. Based on an established SL-BESS model, a two-level community energy management framework is proposed, which optimizes the schedules of a SL-BESS and other energy resources in a community subjected to a variety of short-term operational objectives. In the upper level, a many-objective optimization model is formulated, which comprehensively integrates four objectives covering the community's multi-scale operational considerations. A NSGA-III-based solving approach is developed to find the non-dominated solutions of the model. In the lower level, the optimal community scale load reshaping decisions and energy costs obtained from the upper level are allocated to individual houses. Extensive numerical case studies are conducted, and the results show that the proposed system can realize better trade-off among the different operational considerations with less computational cost.

A decentralized non-linear dynamic droop control of a hybrid energy storage system bluefor primary frequency control in integrated AC-MTDC systems

Power-sharing between energy storage systems (ESSs) is one of the significant challenges in a hybrid energy storage system (HESS). For primary frequency control through multi-terminal DC (MTDC) systems interfacing renewable resources, a decentralized control method based on non-linear dynamic droop control (NLDDC) is proposed in this study for a hybrid energy storage system. Battery energy storage systems (BESS) and super capacitor (SC) make up the hybrid energy storage device in this article. The proposed strategy, which employs dynamic droop gain to distribute power between the SC and the battery, outperforms the previous droop regulation. The SC will be in charge of compensating the high-frequency demand, whereas the BESS will be in charge of compensating the low-frequency power demand, thanks to a decentralized hierarchical energy management process. As a result, the proposed approach improves system transient stability following grid faults, while enhancing primary frequency response under net load variability and generation outage. Finally, simulation results utilizing the Cigre MTDC benchmark and the IEEE 39-bus test system are provided to validate the proposed structure. The NLDDC parameters are fine-tuned using krill herd (KH), an intelligence algorithm with biological inspiration.

Adaptive hierarchical energy management design for a novel hybrid powertrain of concrete truck mixers

Due to the characteristics of a significant difference between vehicle no-load and full load, time-varying double driving conditions, two-energy-source and two-energy-consumption-unit, and high coupling electromechanical-hydraulic system, this work studies two critical technologies, including powertrain design and energy management strategy development, to solve the tricky energy optimization problem of concrete truck mixers. A novel hybrid powertrain with a single-shaft parallel for the upper-part system and extended-range for the propulsion system is proposed. On that basis, an adaptive hierarchical energy management strategy is developed including two efforts. First, a rule-based hierarchical strategy with auxiliary power unit multi-mode switching for the upper and power splitting of system power units for the lower is designed. Second, a driving condition recognizer constructed by a hybrid-optimization-based random forest algorithm and a genetic algorithm optimization-based differentiated controller is designed, which can automatically switch to the optimal strategy under current driving conditions according to recognition results. Simulation results manifest that the designed optimized driving condition recognizer performance outperforms unoptimized by 2.50%. Compared with dynamic programming and the presented rule-based hierarchical strategy, the proposed adaptive hierarchical strategy with terminal SOC converging to the lowest level has better adaptability, economy, and real application performance under a real-world composite driving condition.

Techno economic analysis of microgrid with an efficient energy management system and inverter control strategies

A microgrid comprises of distributed energy resources with the capability of operating independently as an islanded mode or in a grid connected mode. The efficacy of a microgrid is based on the performance of the control strategy and the energy management strategy. Therefore, in this paper the feasibility of an efficient inverter control strategy and energy management strategy for microgrid are studied. The proposed microgridis implemented with master-slave energy management control and battery management system for effective power flow control in an islanded and grid connected mode. A three-layer hierarchical energy management strategy comprising of master-slave system to provide continuous supply at all conditions and effective switchover operations between grid connected and islanded modes of operation is proposed. The voltage-frequency control under standalone mode of operation and P-Q control using hysteresis current control under grid-connected mode of operation are developed and the system parameters like real power, reactive power and voltage at the PCC are analyzed after the implementation of proposed controllers under islanded and grid connected modes of operation. The proposed model achieves voltage and frequency regulation under varying system operating conditions. Also, a techno-economic analysis is performed in HOMER software where the cost of energy and return on investment are studied for the proposed microgrid system by which levelized cost and payback period is reduced. The proposed control algorithms are implemented through MATLAB simulation and tested in a real-time for 1 kW grid-connected solar PV system.

Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine

The dedicated hybrid engines (DHEs) with dual-mode combustion technology can drastically reduce the fuel consumption and emissions while guarantee the power density. This paper aims to investigate the optimal control of such DHE-based plug-in hybrid electric vehicles (PHEVs) under real driving conditions, with minimum fuel penalties caused by transient engine dynamics. For this purpose, the benefits brought by artificial intelligent control and traffic preview in terms of energy efficiency can be combined with the advantages of advanced combustion engine. This paper presents a hierarchical energy management strategy (HEMS) to realize the synergy of global and instantaneous optimization. At the cloud level of HEMS, dynamic programming is applied to obtain optimal combustion mode and state of charge reference trajectories in a receding horizon. At the powertrain level, deep reinforcement learning with a ranking-prioritized experience replay algorithm is used to output optimal engine power and combustion mode for the energy management. To evaluate the proposed strategy, a dual-mode engine with homogeneous charge compression ignition and spark ignition systems is tested and mapped, with which the PHEV is modeled in GT-Suite and Matlab/Simulink. Comprehensive experiments are carried out to verify the optimality, generalization and robustness based on a standard driving cycle and a real-world driving cycle in China with GPS data recorded. The results show that the HEMS avoids frequent switching of combustion modes and outperforms the conventional methods by more than 4% and 10% in terms of fuel economy and NOx emissions, respectively, with random initial and terminal conditions.

Intelligent hierarchical energy and power management to control the voltage and frequency of micro-grids based on power uncertainties and communication latency

This paper presents an intelligent energy management method to control the voltage and frequency at the primary and secondary control levels of micro-grids. The proposed model is based on the model predictive control (MPC) at the primary and intelligent neural network (INN) at the secondary. The purpose of this paper is to control the voltage and frequency based on the uncertainties of power generation resources and loads. In order to validate, the results of the proposed model are compared in different scenarios with other methods such as Fuzzy and PID controllers. The performance of the proposed method in primary and secondary as well as the coordination of this method with a high degree of sensitivity along with HESS and diesel generator units, in addition to increasing stability and reliability, protects the operation of the micro-grid against fluctuations based on uncertainties. The results show that this method, in addition to accuracy, has a higher operating speed than other controllers. As a result, the voltage and frequency restoration in the primary and the compensation of deviations from the reference in the secondary are associated with acceptable results. In order to validate the simulation results and experimental results are also presented.

Hierarchical optimal energy management strategy of hybrid energy storage considering uncertainty for a 100% clean energy town

In a 100% clean energy town, to meet the energy balance and reduce the impact of power fluctuations on the main grid, in this paper, a hierarchical optimal energy management strategy (EMS) for a hybrid energy storage system (HESS) is proposed. The EMS consists of three layers. To meet the requirement of 100% clean energy, in the upper layer, a HESS economic operation model based on the mixed integer non-linear programming (MINLP) is presented. Then, considering the uncertainty of renewable energy and load, a power prediction model is presented in the middle layer. In addition, to reduce the power disturbance on the main grid caused by the stochastic power, a stochastic model predictive control (SMPC) strategy is implemented to optimize the power allocation of a HESS. In the lower layer, to reduce the power fluctuations of the lithium-ion battery (LiB) when mitigating minute-scale power fluctuations, a HESS optimal power allocation strategy based on Pontryagin's minimum principle (PMP) is proposed. In every control period, each layer is optimized based on the results of the previous layer. Finally, a simulation study is provided to validate the effectiveness of the proposed EMS. The results show that the proposed strategy has good performance in typical scenarios.

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

Development and experimental validation of hierarchical energy management system based on stochastic model predictive control for Off-grid Microgrids

Abstract In this study, a hierarchical energy management system (EMS) is proposed, to coordinate different energy sources in an islanded multi-good microgrid. The first layer deals with the daily scheduling problem, while the second layer balances the generation in real-time. A novel second layer formulation, relying on model predictive control under a scenario-based stochastic approach (sMPC), is introduced and it is compared to a reference formulation, based on a central proportional-integral controller following the indications set by the first layer. The proposed sMPC explicitly accounts for uncertainty considering several scenarios of very-short term forecast errors, that act as disturbances for the system. The sMPC evaluates the control actions and the correction rules required to guarantee optimal operations through disturbance-feedback. The EMS is implemented in an experimental setup and tested for daily operations under a rolling horizon approach. The accuracy of the numerical system simulation is evaluated, resulting in an average discrepancy of 1.7%, in terms of operation cost, with respect to the experimental operations. Then, a test case comparing the proposed EMS with the reference approach shows that the adoption of sMPC allows to approach the lowest possible operation cost achievable by a second layer with an advantage of 2.7 % against the reference case. Finally, the developed sMPC leads to only 0.5% additional costs than an ideal controller working on the same control layer.

Adaptive energy management for hybrid power system considering fuel economy and battery longevity

The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs.

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

An Automatic Aggregator of Power Flexibility in Smart Buildings Using Software Based Orchestration.

This paper presents a software-based modular and hierarchical building energy management system (BEMS) to control the power consumption in sensor-equipped buildings. In addition, the need of this type of solution is also highlighted by presenting the worldwide trends of thermal energy end use in buildings and peak power problems. Buildings are critical component of smart grid environments and bottom-up BEMS solutions are need of the hour to optimize the consumption and to provide consumption side flexibility. This system is able to aggregate the controls of the all-controllable resources in building to realize its flexible power capacity. This system provides a solution for consumer to aggregate the controls of ‘behind-the-meter’ small loads in short response and provide ‘deep’ demand-side flexibility. This system is capable of discovery, status check, control and management of networked loads. The main novelty of this solution is that it can handle the heterogeneity of the installed hardware system along with time bound changes in the load device network and its scalability; resulting in low maintenance requirements after deployment. The control execution latency (including data logging) of this BEMS system for an external control signal is less than one second per connected load. In addition, the system is capable of overriding the external control signal in order to maintain consumer coziness within the comfort temperature thresholds. This system provides a way forward in future for the estimation of the energy stored in the buildings in the form of heat/temperature and use buildings as temporary batteries when electricity supply is constrained or abundant.

Multi-Level Energy Management Systems Toward a Smarter Grid: A Review

Home Energy Management Systems (HEMSs) may not be able to solve network issues, especially in the presence of high penetration level of Electric Vehicles (EVs) and decentral renewable energy. To solve the problem, Grid Energy Management Systems (GEMSs) were introduced. However, because of the contradictory nature of the main objectives of HEMS which are economical oriented on end-users, e.g., cost minimization, and GEMS which are technical oriented on system operators, e.g., maximization of system stability and power quality cannot be satisfied simultaneously. Hence, a multi-level energy management system seems to be necessary to improve the techno-economic performance of the distribution system while satisfying end-users, electricity retailers, and the system operator. Because of the significance of the subject, this paper presents the state-of-the-art regarding different energy management systems at home, aggregator, and network levels. The advantages and disadvantages of each system are discussed and compared, considering their main elements such as objective functions, constraints, optimization algorithms, communication protocols, and impact of EVs. The challenges and limitations in hierarchical energy management are explained. Finally, some future research directions are suggested to improve the multi-level energy management system.

Hierarchical Energy Management of a Hybrid Propulsion System Considering Speed Profile Optimization

This paper proposes a hierarchical optimization energy management strategy (EMS) considering speed profile to explore energy-saving potential and achieve reasonable power distribution for the hybrid propulsion system of a tramway composed of the catenary, a battery pack and an ultracapacitor pack. For the higher layer, a speed profile is optimized in the space domain through a particle swarm optimization algorithm, which balances energy consumption, running time and passenger comfort. For the lower layer, based on the optimized speed profile, an EMS is designed, using dynamic programming. The boundary range calculation of state variables for the dynamic programming is carried out in advance, based on the working mode analysis to reduce the computation load. In addition, a supervisory controller is developed to realise real-time control. Finally, to verify the proposed method, real rail line data in China is used as a case study, and the results show that the proposed method offers better performance.

A hierarchical transactive energy management framework for optimizing the reserve profile in district energy systems

District energy systems (DESs) have become a popular form of satisfying comprehensive energy demands for different types of loads in multiple local buildings. For DESs, the operational flexibility could be maintained by energy conversion and storage facilities. This paper proposes a hierarchical optimization framework for leveraging and aggregating the DES flexibility to provide contingency reserves. To characterize and quantify the flexibility in individual DESs, the concept of available reserve profile, which is measured by a set of indices, is established. A two-stage robust optimization (RO) model is developed for calculating the indices, which considers the uncertainties associated with wind power and ambient temperature. The lower stage of the two-stage model is managed by district energy system operators (DESOs) which submit reserve profiles to the district energy system coordinator (DESC) at the upper stage, which is responsible for the coordination process. Correspondingly, information privacy is preserved using a coordinated data-sharing strategy. Using reserve profiles submitted by multiple DESOs, the DESC applies the proposed coordination model to provide a certain reserve capacity schedule to DESs, which satisfies the stated objectives. The coordination model is formulated and solved based on the special ordered set (SOS) technique and particle swarm optimization (PSO) algorithm. A test system is developed to illustrate the technical viability and economic feasibility of the proposed technique.

Hierarchical energy management strategy for hybrid electric vehicle under connective scenarios

Abstract The development of connected vehicle technology presents opportunity for hybrid electric vehicle (HEV) to further improve its energy efficiency. Within this horizon, the energy management strategy (EMS) considering connective information is an enabling technology. In this paper, a hierarchical EMS combining velocity planning and power split management is proposed for HEV under connective scenarios. Particularly, the driving route is separated into several segments, and the length of single segment is adaptively adjusted according to the possibility pre-judgment for preceding vehicle crossing the upcoming intersection. Furthermore, the velocity trajectory is optimized by incorporating the preceding vehicle pre-judgment and traffic information with dynamic programming. The equivalent consumption minimization strategy (ECMS) is used to realize the power split optimal distribution in lower level. Finally, the simulation results demonstrate the effectiveness of proposed method.