scholarly journals Optimal energy management of microgrid based on multi-parameter dynamic programming

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093714
Author(s):  
Xuejie Wang ◽  
Yanchao Ji ◽  
Jianze Wang ◽  
Yuanjun Wang ◽  
Lei Qi
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Distributed Generation ◽  
Economic Benefits ◽  
Stable Operation ◽  
Power Sources ◽  
Parametric Optimization Problem ◽  
Optimal Energy Management ◽  
Microgrid Energy Management ◽  
Economic Objective

With the wide application of microgrid system, fluctuation and randomness are the characteristics of distributed generation output. The traditional energy management system can’t meet the requirements to ensure the security and stability of the grid. The microgrid energy management is of great significance to the stable operation of power grid. In order to obtain higher economic benefits and pay less environmental costs, reasonable scheduling of various distributed power sources is able to achieve this goal. In this article, microgrid energy management including distributed generation is studied. The objective function includes the economic objective and the environmental objective. The model of energy management is considered as a multi-objectives and multi-parametric optimization problem. The multi-parameter dynamic programming is used to optimize the energy management of microgrid. Finally, the efficiency of the proposed method is examined by the simulation studies.

scholarly journals A Critical Assessment of Demand Response Programs Applied for Optimal Energy Management in Microgrids

2019 ◽  
Vol 8 (2S7) ◽  
pp. 344-349
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Power Flow ◽  
Renewable Energy Sources ◽  
Weather Condition ◽  
Optimal Energy ◽  
Autonomous Mode ◽  
Optimal Energy Management ◽  
Demand Response Programs ◽  
Microgrid Energy Management

Microgrid Energy Management is done to optimize microgrid performance. Power from Wind Turbines (WT) and Photo Voltaic (PV) modules into a microgrid addresses both factors of environmental concerns as well as sustainable energy production. Point of coupling with utility main grid is disconnected when microgrid functions in autonomous mode and it enhances steady microgrid operation when traditional grids face blackouts. Clean and renewable energy sources being easily affected by variation in weather condition, so taking into account of this uncertainty is essential while formulating power flow problem which can be done through demand response programs. This paper aims to investigate results obtained from research of several researchers scrutinizingly and analyzed critically for optimal energy management in microgrids using demand response programs. This paper also highlights the worthy findings of possible areas of research that would enhance the use of demand side management through demand response programs in microgrids.

Optimal energy management strategy for fuel cell/battery/supercapacitor vehicles using wavelet transform and equivalent consumption minimization strategy

2021 ◽  
pp. 095440702110697
Author(s):  
Pengfei Zou ◽  
Fazhan Tao ◽  
Zhumu Fu ◽  
Pengju Si ◽  
Chao Ma
Keyword(s):  
Wavelet Transform ◽  
Fuel Cell ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Low Frequency ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Optimal Energy ◽  
Optimal Energy Management

In this paper, the hybrid electric vehicle is equipped with fuel cell/battery/supercapacitor as the research object, the optimal energy management strategy (EMS) is proposed by combining wavelet transform (WT) method and equivalent consumption minimization strategy (ECMS) for reducing hydrogen consumption and prolonging the lifespan of power sources. Firstly, the WT method is employed to separate power demand of vehicles into high-frequency part supplied by supercapacitor and low-frequency part allocated to fuel cell and battery, which can effectively reduce the fluctuation of fuel cell and battery to prolong their lifespan. Then, considering the low-frequency power, the optimal SOC of battery is used to design the equivalent factor of the ECMS method to improve the fuel economy. The proposed hierarchical EMS can realize a trade-off between the lifespan of power sources and fuel economy of vehicles. Finally, the effectiveness of the proposed EMS is verified by ADVISOR, and comparison results are given compared with the traditional ECMS method and ECMS combining the filter.

Optimal Energy Management of Hybrid Power System With Two-Scale Dynamic Programming

2011 ◽  
Author(s):  
Lei Zhang ◽  
Yaoyu Li
Keyword(s):  
Dynamic Programming ◽  
Wind Energy ◽  
Energy Management ◽  
Electricity Price ◽  
Short Term ◽  
Optimal Energy ◽  
Macro Scale ◽  
Optimal Energy Management ◽  
The Impact

Energy management is one of the main issues in operating the HPS, which needs to be optimized with respect to the current and future change in generation, demand, and market price, particularly for HPS with strong renewable penetration. Optimal energy management strategies such as dynamic programming (DP) may become significantly suboptimal under strong uncertainty in prediction of renewable generation and utility price. In order to reduce the impact of such uncertainties, a two-scale dynamic programming scheme is proposed in this study to optimize the operational benefit based on multi-scale prediction. First, a macro-scale dynamic programming (MASDP) is performed for the long term period, based on long term ahead prediction of hourly electricity price and wind energy (speed). The battery state-of-charge (SOC) is thus obtained as the macro-scale reference trajectory. The micro-scale dynamic programming (MISDP) is then applied with a short term interval, based on short term-hour ahead auto-regressive moving average (ARMA) prediction of hourly electricity price and wind energy. The nodal SOC values from the MASDP result are used as the terminal condition for the MISDP. The simulation results show that the proposed method can significantly decrease the operation cost, as compared with the single scale DP method.

scholarly journals Dynamic Programming for Optimal Energy Management of Hybrid Wind–PV–Diesel–Battery

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3039 ◽  
Author(s):  
Luu An ◽  
Tran Tuan
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Hybrid System ◽  
Electricity Market ◽  
Environmental Benefits ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Diesel Generator ◽  
Optimal Energy ◽  
Optimal Energy Management

With the dramatic development of renewable energy resources all over the world, Vietnam has started to apply them along with the conventional resources to produce the electrical power in recent years. Visually, the aim of this action is to improve the economic as well as the environmental benefits. Therefore, a vast of hybrid systems that combine Wind turbine, Photovoltaic (PV), Diesel generator and battery have been considered with different configurations. According to this topic, there are lots of research trends in the literature. However, we aim to the optimal energy management of this hybrid system. In particular, in this paper, we propose an optimization method to deal with it. The interesting point of the proposed method is the usage of the information of sources, loads, and electricity market as an embedded forecast step to enhance the effectiveness of the actual operation via minimizing the operation cost by scheduling distributed energy resources (DER) while regarding emission reduction in the hybrid system is considered as the objective function. In this optimization problem, the constraints are determined by two terms, namely: the balance of power between the supply and the load demand, and also the limitations of each DER. Thus, to solve this problem, we make use of the dynamic programming (DP) to transform a system into a multi-stage decision procedure with respect to the state of charge (SOC), resulting in the minimum system cost (CS). In order to highlight the pros of the proposed method, we implement the comparison to a rule-based method in the same context. The simulation results are examined in order to evaluate the effectiveness of the developed methodology, which is a so-called global optimization.

scholarly journals On Implementing Optimal Energy Management for EREV Using Distance Constrained Adaptive Real-Time Dynamic Programming

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 228 ◽  
Author(s):  
Aman Kalia ◽  
Brian Fabien
Keyword(s):  
Dynamic Programming ◽  
Energy Consumption ◽  
Real Time ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Control ◽  
Time Dynamic ◽  
Optimal Energy ◽  
Optimal Energy Management ◽  
Driving Range

Extended range electric vehicles (EREVs) operate both as an electric vehicle (EV) and as a hybrid electric vehicle (HEV). As a hybrid, the on-board range extender (REx) system provides additional energy to increase the feasible driving range. In this paper, we evaluate an experimental research EREV based on the 2016 Chevrolet Camaro platform for optimal energy management control. We use model-in-loop and software-in-loop environments to validate the data-driven power loss model of the research vehicle. A discussion on the limitations of conventional energy management control algorithms is presented. We then propose our algorithm derived from adaptive real-time dynamic programming (ARTDP) with a distance constraint for energy consumption optimization. To achieve a near real-time functionality, the algorithm recomputes optimal parameters by monitoring the energy storage system’s (ESS) state of charge deviations from the previously computed optimal trajectory. The proposed algorithm is adaptable to variability resulting from driving behavior or system limitations while maintaining the target driving range. The net energy consumption evaluation shows a maximum improvement of 9.8% over the conventional charge depleting/charge sustaining (CD/CS) algorithm used in EREVs. Thus, our proposed algorithm shows adaptability and fault tolerance while being close to the global optimal solution.

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