Utility demand response operation considering day-of-use tariff and optimal operation of thermal energy storage system for an industrial building based on particle swarm optimization algorithm

2016 ◽  
Vol 127 ◽  
pp. 920-929 ◽  
Author(s):  
H. Molavi ◽  
M.M. Ardehali
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Demand Response ◽  
Particle Swarm Optimization Algorithm ◽  
Storage System ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Industrial Building ◽  
Swarm Optimization ◽  
Thermal Energy Storage System

Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm

2021 ◽  
pp. 002072092098369
Author(s):  
Jijun Liu ◽  
Yuxin Bai ◽  
Yingfeng He
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Optimal Allocation ◽  
Storage System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Economic Cost ◽  
Energy Storage System ◽  
Swarm Optimization ◽  
Active Distribution Network

This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

scholarly journals Optimal Allocation of Energy Storage System Considering Price-Based Demand Response and Dynamic Characteristics of VRB in Wind-PV-ES Hybrid Microgrid

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 483
Author(s):  
Qingwu Gong ◽  
Jintao Fang ◽  
Hui Qiao ◽  
Dong Liu ◽  
Si Tan ◽  
...  
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Dynamic Characteristics ◽  
Demand Response ◽  
Storage System ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Battery Energy Storage ◽  
Hybrid Microgrid ◽  
Battery Energy

Studying the influence of the demand response and dynamic characteristics of the battery energy storage on the configuration and optimal operation of battery energy storage system (BESS) in the Wind-Photovoltaic (PV)-Energy Storage (ES) hybrid microgrid. A demand response model that is based on electricity price elasticity is established based on the time-of-use price. Take the capital-operating cost and direct economic benefit of the BESS and the loss of abandoned photovoltaic and wind power as the optimization objective, an optimal configuration method that considers the dynamic characteristics of the BESS and the maximum absorption of photovoltaic and wind power is proposed while using particle swarm optimization to solve. The results show that the configuration results considering the demand side response of the microgrid BESS can obtain better economy and reduce the storage capacity requirement, and the result shows that the efficiency of BESS relates to the load of the system, the distributed generation (DG) characteristics, and the dynamic characteristics of BESS. Meanwhile, the capacity and power of the energy storage configuration increase as the DG permeability increases due to the reverse load characteristic of the wind power.

scholarly journals Optimal Control Strategy for a Marine Current Farm Integrated with a Hybrid PV System/Offshore Wind/Battery Energy Storage System

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
Adel Elgammal ◽  
Miguel Jagessar
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Demand Response ◽  
Control Strategy ◽  
Storage System ◽  
Offshore Wind ◽  
Swarm Optimization ◽  
Battery Energy Storage ◽  
Multi Objective ◽  
Battery Energy

This paper suggests an automated control technique constructed on the Multi-Objective Particle Swarm Optimization to enhance the operation of a wind farm, a marine power plant and a photovoltaic array with a battery energy storing system. due to changes in PV / wind / tide, and to boost the efficiency of offshore wind farms and marine power stations connected to the battery-powered storage system, with a view to smoothing power production, the aim of projected automatic control strategy is to minimize power fluctuations and voltage variations. The battery energy storage network was used with an optimized demand response strategy based on the real-time pricing model to improve stability and power efficiency, reduce the power fluctuations and variations in bus voltage and address renewable energy generation instability. The multi-objective particle swarm optimization-based energy management programming model would be used to minimize running costs, pollutant emissions, increase the demand response benefits of micro grid operators and, at the same time, meet the load demand constraints from customers of all sorts, such as domestic, commercial and industrial users.

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