Optimal Energy Management of an improved elevator with energy storage capacity based on Dynamic Programming

2012 ◽  
Author(s):  
Endika Bilbao ◽  
Philippe Barrade ◽  
Ion Etxeberria-Otadui ◽  
Alfred Rufer ◽  
Sergio Luri ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Energy Storage ◽  
Energy Management ◽  
Storage Capacity ◽  
Optimal Energy ◽  
Energy Storage Capacity ◽  
Optimal Energy Management

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 Optimal Energy Management of Railroad Electrical Systems with Renewable Energy and Energy Storage Systems

2019 ◽  
Vol 11 (22) ◽  
pp. 6293 ◽  
Author(s):  
Seunghyun Park ◽  
Surender Reddy Salkuti
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Storage Systems ◽  
Solar Pv ◽  
Energy Storage Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Optimal Energy ◽  
Optimal Energy Management

The proposed optimal energy management system balances the energy flows among the energy consumption by accelerating trains, energy production from decelerating trains, energy from wind and solar photovoltaic (PV) energy systems, energy storage systems, and the energy exchange with a traditional electrical grid. In this paper, an AC optimal power flow (AC-OPF) problem is formulated by optimizing the total cost of operation of a railroad electrical system. The railroad system considered in this paper is composed of renewable energy resources such as wind and solar PV systems, regenerative braking capabilities, and hybrid energy storage systems. The hybrid energy storage systems include storage batteries and supercapacitors. The uncertainties associated with wind and solar PV powers are handled using probability distribution functions. The proposed optimization problem is solved using the differential evolution algorithm (DEA). The simulation results show the suitability and effectiveness of proposed approach.

Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage

2018 ◽  
Vol 9 (3) ◽  
pp. 2065-2075 ◽  
Author(s):  
Xiaohua Wu ◽  
Xiaosong Hu ◽  
Xiaofeng Yin ◽  
Scott J. Moura
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Smart Home ◽  
Optimal Energy ◽  
Optimal Energy Management

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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