Optimal Energy Management Strategy Research Articles

ELM-based driver torque demand prediction and real-time optimal energy management strategy for HEVs

In hybrid electric vehicles, the energy economy depends on the coordination between the internal combustion engine and the electric machines under the constraint that the total propulsion power satisfies the driver demand power. To optimize this coordination, not only the current power demand but also the future one is needed for real-time distribution decision. This paper presents a prediction-based optimal energy management strategy. Extreme learning machine algorithm is exploited to provide the driver torque demand prediction for realizing the receding horizon optimization. With an industrial used traffic-in-the-loop powertrain simulation platform, an urban driving route scenario is built for the source data collection. Both of one-step-ahead and multi-step-ahead predictions are investigated. The prediction results show that for the three-step-ahead prediction, the 1st step can achieve unbiased estimation and the minimum root-mean-square error can achieve 100, 150 and 160 of the 1st, 2nd and 3rd steps, respectively. Furthermore, integrating with the learning-based prediction, a real-time energy management strategy is designed by solving the receding horizon optimization problem. Simulation results demonstrate the effect of the proposed scheme.

Optimal energy management of a fuel cell-battery-supercapacitor-powered hybrid tramway using a multi-objective approach

Achieving an optimal operating cost is a challenge for the development of hybrid tramways. In the past few years, in addition to fuel costs, the lifespan of the power source is being increasingly considered as an important factor that influences the operating cost of a tramway. In this work, an optimal energy management strategy based on a multi-mode strategy and optimisation algorithm is described for a high-power fuel cell hybrid tramway. The objective of optimisation is to decrease the operating costs under the conditions of guaranteeing tramway performance. Besides the fuel costs, the replacement cost and initial investment of all power units are also considered in the cost model, which is expressed in economic terms. Using two optimisation algorithms, a multi-population genetic algorithm and an artificial fish swarm algorithm, the hybrid system's power targets for the energy management strategy were acquired using the multi-objective optimisation. The selected case study includes a low-floor light rail vehicle, and experimental validations were performed using a hardware-in-the-loop workbench. The results testify that an optimised energy management strategy can fulfil the operational requirements, reduce the daily operation costs and improve the efficiency of the fuel cell system for a hybrid tramway.

Design and Experimental Comparison of Energy Management Strategies for Hybrid Electric Buses Based on Test-Bench Simulation

Energy management in hybrid and electric vehicles is a key factor to improve the operational performance and meet the efficiency objectives defined in the transport sector. Thus, optimized energy management strategies (EMSs), before being integrated in a real system, need to be experimentally validated in test-bench platforms in order to identify the possible deviations from the expected simulation-based performance while minimizing the implementation time and field-test on the real application. An economical and flexible mean of validating these strategies is the hardware-in-the-loop simulation. Therefore, this work aims to present the design approach and comparison, by means of experimental tests, of two optimized (simulation-based) EMSs proposed for a hybrid electric bus (HEB) focusing on the real-time operational performance. Both EMSs handle the proper power split behavior of the vehicle demand among a genset (internal combustion engine + electric generator) and a hybrid energy storage system (combining Li-ion batteries with supercapacitors). The experimental platform consists of a scaled test bench emulating the electrical dc grid of a HEB. This test bench, combined with software models to control the power electronic devices, allows us to emulate the real behavior of the genset, battery, supercapacitor, traction, and auxiliary loads.

Optimized Energy Management Strategy for Wind Plants with Storage in Energy and Reserve Markets

This paper addresses the joint operation of wind plants with energy storage systems in multiple markets to increase the value of wind energy from an economic and technical point of view. The development of an optimized energy management allows scheduling the wind generation in energy markets, as well as contributing to the system stability through the joint participation in frequency ancillary services. The market optimization maximizes market revenues considering overall storage costs, while avoiding energy imbalances and market penalties. Moreover, wind power fluctuations, forecast errors and real-time reserve requirements are controlled by the energy storage system and managed afterward through the participation in continuous intraday market. Furthermore, model predictive control approach enables a high compliance of reserve requirements and a huge reduction of energy imbalances in real-time operation. Different energy storage capacities are selected in order to evaluate their cost-effectiveness enhancing the wind plant operation under the considered study case.

Optimal Operation of Greenhouses in Microgrids Perspective

An optimal energy management strategy for greenhouses is proposed in this paper for both grid-connected and islanded modes, i.e., microgrids perspective. Especially, the islanded mode is focused due to the scarcity of resources and inability to suffice all loads during the entire islanded period. In normal mode, the greenhouse can trade power with the utility grid to minimize its operation cost. In islanded mode, only local resources can be used, and thus precedence of different control parameters is defined to optimize the growth of plants. In addition, an algorithm is developed to avoid the acceptable range violation of the same parameter in consecutive intervals. The four major control parameters considered in this paper are indoor temperature, relative humidity, CO2 concentration, and indoor lighting. In order to enhance the sustainability of the greenhouse, renewable sources, energy storage systems, and combined heat and power units are incorporated into the model. The uncertainties associated with renewable generations and loads (electric, heat, and cooling) are realized via robust optimization method. The effect of uncertainties on operation cost and power trading with the utility grid in grid-connected mode along with operation cost and control parameters’ violation intervals in islanded mode are analyzed. Finally, the effect of event occurrence time on the violation intervals of the control parameters is analyzed. Numerical simulations have proved the effectiveness of the proposed method.

Heuristic Dynamic Programming Based Online Energy Management Strategy for Plug-In Hybrid Electric Vehicles

For the online energy optimization problem of plug-in hybrid electric vehicles (P-HEVs), this paper proposes a heuristic dynamic programming (HDP) based online energy management strategy, to minimize the fuel consumption of the P-HEV. First of all, considering the uncertain nonlinear dynamic process of a vehicle in the actual traffic environment, we adopt the back propagation neural network (BPNN) to construct the dynamic model of the P-HEV. Then, on this basis, we utilize the HDP to establish an energy management controller with the aim of minimizing energy consumption of the P-HEV. Moreover, the energy management controller is implemented by an online energy management strategy algorithm. To verify the effect of the controller, we employ a practical route in Beijing road network to simulate the BPNN model of the P-HEV and the proposed energy management strategy. The experimental results show several advantages of our strategy. First, compared to the analytic model, the BPNN model can reflect the real dynamic process of the P-HEV with a higher precision. Second, the assigned torques by the strategy can effectively make the vehicle track the desired vehicle-speeds, and the tracking accuracy of the vehicle-speed is higher than 98%. Besides, on the premise of ensuring the real-time performance, the proposed strategy can further reduce the fuel consumption and emissions of the P-HEV when compared with the existing online energy management strategies, although its fuel consumption is more than that of the offline global optimization energy management strategy by 4% approximately.

Price-Controlled Energy Management of Smart Homes for Maximizing Profit of a GENCO

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost.

Optimal Energy Management Strategy and Novel Control Approach for DPGSs Under Unbalanced Grid Faults

Recently, the use of distributed power generation systems (DPGSs) based on renewable energy resources is increasingly being pursued as a supplement and a reliable alternative to the large traditional energy sources. For it, power-electronic interface technologies and control have also emerged as the most important key elements in the area of energy management and integrating DPGSs. The specification of a power-electronic interface is subject to several requirements that are related not only to the DPGS itself but also to its interactions with the power system especially where the utility grid is subject to events that can potentially lead to large-scale disturbances or even to its collapse if it operates near its capacity without fault margin. This study deals, first, with an optimized energy management strategy and, second, with a newly-conceived control strategy called symmetrical components control algorithm (SCCA) that was proposed for four-leg three-phase grid-connected voltage source inverter (VSI) used for DPGSs with wind–solar–battery sources. A mechanism of negative and zero sequences injection based on the control of ([Formula: see text]) current coordinates has been introduced. The performance of entire control system, to enhance the unbalanced fault ride-through capability of DPGSs, has been evaluated by time domain simulations with MATLAB/Simulink. Advantages of the combined active–reactive control ensuring both current and voltage controls have been achieved compared to the majority of already published strategies. The distinct features of the proposed SCCA strategy prove that it allows to meet the requirements for grid interconnection and the new stricter standards with respect to power quality, safe running, and islanding protection.

Optimal schedule of grid-connected residential PV generation systems with battery storages under time-of-use and step tariffs

Abstract In recent years, grid-connected residential PV generation systems have been greatly encouraged in China. In a residential power system containing PV, three types of power sources, namely, PV generation, battery storage and the utility grid, need to be properly scheduled. The time-of-use tariff and step tariff have been widely accepted in China. Correspondingly, this paper proposes two optimal energy management strategies for residential power systems to adapt to the two tariffs towards largest benefits. The strategies are formulated as optimization problems, where minimizing the household energy cost is taken as the objective and the dispatching ratio of electricity sold to the grid and used locally is treated as the optimization variable. A genetic algorithm (GA) is then employed to solve the formulated nonlinear optimization problems. A typical residential power system, containing grid-connected PV panels and a battery storage system, is used for verification study. The results show that the proposed energy management strategies, along with a GA based solving method, are effective in scheduling the operation status of the three power sources optimally to achieve maximum economic benefits under the time-of-use tariff and step tariff, respectively.

Stackelberg game‐based energy management for a microgrid with commercial buildings considering correlated weather uncertainties

This paper proposes a Stackelberg game-based optimal energy management model for a microgrid with commercial buildings (CBs), which include a cluster of flexible loads, such as a heating, ventilation, and air conditioning (HVAC) system and a lighting system. In particular, the microgrid operator (MGO) determines the optimal energy management scheme while the CBs enjoy a dynamic pricing tariff to adjust their consumption patterns for cost saving. The interactions between MGO and CBs are formulated as a bi-level optimisation problem where the MGO behaves as a leader and CBs act as followers. The proposed model is transformed into a mixed integer linear programming (MILP) problem by jointly using the Karush–Kuhn–Tucker (KKT) condition and the strong duality theory. Besides, the effects of correlated solar irradiance and solar illuminance uncertainties on the load profile are taken into account through invoking the Nataf transformation-based 2 M + 1 point estimate method (PEM). Finally, case studies are served for demonstrating the feasibility and efficiency of the proposed method.

Size optimization of a stand-alone micro-grid integrated with flexible load and the complementary of solar and wind

With the development of micro-grids, the large-scale use of solar and wind energies, and the high growth of load demands, it is of great significance to improve the utilization ratio of renewable energy and economy for a micro-grid system. By making full use of the complementary of solar and wind energies or realizing the interaction between supply and demands, this situation can be effectively improved. Thus, regarding the ability of these two factors, this paper is devoted to analysing and modelling a stand-alone micro-grid with a hybrid PV/wind/battery power generation system. Also, an optimal energy management strategy, which emphasizes the coordination of sources/batteries/loads, is proposed based on the existing strategies. Furthermore, the effect of the complementary of solar and wind energies is studied. Aiming at minimizing the annualized cost of the system (fACS), flexible load dispatching cost (ffl), and unutilized renewable energy probability (fUREP), a multi-objective size optimization problem is efficiently solved using the particle swarm optimization algorithm. The simulation results depict the effectiveness of the proposed method.

Optimal energy management strategies for energy Internet via deep reinforcement learning approach

This paper investigates the energy management problem in the field of energy Internet (EI) with interdisciplinary techniques. The concept of EI has been proposed for a while. However, there still exist many fundamental and technical issues that have not been fully investigated. In this paper, a new energy regulation issue is considered based on the operational principles of EI. Multiple targets are considered along with constraints. Then, the practical energy management problem is formulated as a constrained optimal control problem. Notably, no explicit mathematical model for power of renewable power generation devices and loads is utilized. Due to the complexity of this problem, conventional methods appear to be inapplicable. To obtain the desired control scheme, a model-free deep reinforcement learning algorithm is applied. A practical solution is obtained, and the feasibility as well as the performance of the proposed method are evaluated with numerical simulations.

Fast Planning of Global SoC Trajectory for Hierarchical Predictive Energy Management in PHEVs

This paper proposes a novel hierarchical predictive energy management algorithm with fast global SoC planning speed in PHEV. The algorithm consists of two layers：the upper fast global SoC planning level and the lower short-term vehicle speed predictive and model predictive control (MPC) level. The upper level strategy is driven by traffic data and formulates an optimal global SoC trajectory as the constraints of MPC. In the lower level, the short-term vehicle speed is predicted as the disturbance, and via associating with the obtained global SoC, the optimal energy management strategy can be developed to satisfice the fuel consumption requirements in real time. To evaluate the control strategy, we compare the SoC trajectories obtained from fast global planning (FP) and dynamic programming (DP). Then, the curve is applied to the MPC control level (FP-MPC) and compared with the control effect of the MPC based on the CDCS global SoC (CDCS-MPC). Numerical results illustrate the effectiveness of the fast global SoC planning method and the significant impact via introducing the method into the predictive energy management of PHEV.

Research on PHEV logic threshold energy management strategy based on Engine Optimal Working Curve

The main purpose of energy management strategy design is to satisfy the requirements of power performance; to make sure that the engine can work in the high-efficiency working area as far as possible; to optimize the economic performances and environmental performance of the vehicle, and to avoid overcharging and discharging of battery packs. The advantages and disadvantages of energy management strategy directly determine the economy and environmental protection of parallel hybrid electric vehicles. In this paper, a logic threshold energy management strategy based on the engine’s most working curve is designed, and then a real-time optimal energy management strategy is proposed based on the equivalent fuel consumption minimization algorithm. The strategy is correct and has good real-time performance.

Robust Mixed H 2 / H ∞ Controller Design for Energy Routers in Energy Internet

In this paper, a class of mixed H 2 / H ∞ controller is designed for an energy router (ER) within the scenario of an energy Internet (EI). The considered ER is assumed to have access with photovoltaic panels, wind turbine generators, micro-turbines, fuel cells, diesel engine generators, battery energy storage devices, flywheel energy storage devices, loads, and other ERs. Two types of control targets are considered. First, due to the access of large-scale renewable energy sources, the DC bus voltage deviation within the ER system shall be regulated. Second, an optimal energy management strategy shall be achieved, such that the autonomous power supply-demand balance within each ER is achieved with priority and the rational utilization of controllable power generation devices and energy storage devices are realized. When these objectives are considered simultaneously, the control issues with respect to ER is formulated as a mixed robust H 2 / H ∞ control problem with analytical solutions provided. Several numerical examples are given, and the feasibility and effectiveness of the proposed method are demonstrated.

Comprehensive analysis of risk-based energy management for dependent micro-grid under normal and emergency operations

Micro-grids provide opportunities to enhance reliability and flexibility in power systems. In recent years, resiliency of power grids has attracted many research interests. Moreover, the optimal energy scheduling of micro-grids can satisfy resiliency improvement of the system. In this paper, a novel two-stage risk-constrained stochastic framework is proposed, which can optimally schedule a dependent micro-grid in both normal and emergency situations. Dependent micro-grid is introduced as a new group of micro-grids with additional interconnection point. It can use energy storage systems, DGs and demand response resources to guarantee resilient and economic operation. In the proposed model, linearized AC grid constraints are added to the mathematical formulation to improve computational efficiency. Predominant uncertainties such as loads, prices, wind, and unplanned islanding events are managed based on risk constraints. The optimal energy management strategy is formulated as an MILP and implemented in the IBM CPLEX® Software. Several case studies are carried out to demonstrate the application of the proposed framework. The sensitivity of energy storage system scheduling and the operator's participation in the electricity market are examined by adjusting risk parameters. Furthermore, it is shown that energy procurement by an extra interconnection point would mitigate the economic losses during emergency condition.

Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm

To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a dual-mode power-split hybrid electric vehicle (HEV) based on an explicit model predictive control (EMPC) method is proposed in this paper. The proposed strategy includes an accurate control-oriented model and a dynamic process coordination control algorithm. The energy management optimal control problem is formulated as a multiparameter quadratic programming optimization problem, and the EMPC control laws are obtained by solving the multiparameter quadratic programming problem offline. The laws are then used online to realize real-time control. A traditional model predictive control (MPC)-based control strategy, DP-based control strategy and rule-based control strategy are considered benchmark strategies for verification of the proposed EMPC-based energy management strategy. The simulation results indicate the EMPC controller has far lower microprocessor hardware costs than the MPC controller but equivalent control performance. As the prediction horizon increases, fuel consumption remains nearly the same between the MPC-based control strategy and EMPC-based control strategy. The consumption time of the MPC-based control strategy increases significantly, while the consumption time of the EMPC-based control strategy is nearly unchanged. Compared with the benchmark algorithms, the elapsed time of the EMPC controller maximum reduced by 97.46%, and the fuel economy improved by 23.37%.

Multi-Objective Optimization of Energy Management Strategy on Hybrid Energy Storage System Based on Radau Pseudospectral Method

In this study, a multi-objective optimization method based on the Radau pseudospectral method is proposed for the energy management strategy in the hybrid energy storage system (HESS). In the proposed method, by approximating state and control variables in the system with global interpolating polynomials, the optimal control problem (OCP) is transformed into a nonlinear programming problem (NLP) and solved by a sparse nonlinear optimizer. Further, the Pareto solution set is obtained by taking the energy consumption of the HESS and the equivalent life of the battery as objective functions. Three solutions representing different tradeoffs were selected for comparative analysis: minimum system energy consumption (5819.60 kJ), with battery life 68368 cycles; maximum battery life (76227 cycles), with energy consumption 5865.68 kJ; and the balanced tradeoff optimal solution with battery life 72488 cycles and energy consumption 5841.96 kJ. The results showed that for every additional 5 kJ in system energy consumption, the battery Ah-throughput was reduced by 0.053 Ah and its equivalent life extended by 876 cycles. Further, compared with the single-cell energy source, the balanced tradeoff optimal solution increased the battery life by 29.92% and decreased the system energy consumption by 1.79%. Thus, this work provides a fast and stable multi-objective optimization method for the energy management strategy of HESS and lays the foundation for obtaining optimal system parameters.

Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

A novel optimal energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch-wise iterative learning control (ILC) and time-wise model predictive control (MPC), referred to as 2D-MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time-wise feedback control within the current batch. Then, the MPILC method is applied for the PHEV with the ability to make continuous period-to-period improvements. Its performances will approach dynamic programming (DP)-based method after a learning process with satisfying real-time processing capacity. The results in real-world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of the PHEV.

Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.