Stochastic multi-objective optimization to design optimal transactive pricing for dynamic demand response programs: A bi-level fuzzy approach

2021 ◽  
Vol 125 ◽  
pp. 106487
Author(s):  
Hamid Karimi ◽  
Ramin Bahmani ◽  
Shahram Jadid
Keyword(s):  
Demand Response ◽  
Fuzzy Approach ◽  
Multi Objective Optimization ◽  
Dynamic Demand ◽  
Multi Objective ◽  
Demand Response Programs

Multi-Objective Optimization to Minimize Battery Degradation and Electricity Cost for Demand Response in Datacenters

2015 ◽  
Author(s):  
Abdullah-Al Mamun ◽  
Iyswarya Narayanan ◽  
Di Wang ◽  
Anand Sivasubramaniam ◽  
Hosam K. Fathy
Keyword(s):  
Lithium Ion Battery ◽  
Demand Response ◽  
Optimization Problem ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Battery Pack ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Electricity Cost ◽  
De Algorithm

This paper presents a Lithium-ion battery control framework to achieve minimum health degradation and electricity cost when batteries are used for datacenter demand response (DR). Demand response in datacenters refers to the adjustment of demand for grid electricity to minimize electricity cost. Utilizing batteries for demand response will reduce the electricity cost but might accelerate health degradation. This tradeoff makes battery control for demand response a multi-objective optimization problem. Current research focuses only on minimizing the cost of demand response and does not capture battery transient and degradation dynamics. We address this multi-objective optimization problem using a second-order equivalent circuit model and an empirical capacity fade model of Lithium-ion batteries. To the best of our knowledge, this is the first study to use a nonlinear Lithium-ion battery and health degradation model for health-aware optimal control in the context of datacenters. The optimization problem is solved using a differential evolution (DE) algorithm and repeated for different battery pack sizes. Simulation results furnish a Pareto front that makes it possible to examine tradeoffs between the two optimization objectives and size the battery pack accordingly.

scholarly journals Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage

2016 ◽  
Vol 7 ◽  
pp. 258-269 ◽  
Author(s):  
A. Mamun ◽  
I. Narayanan ◽  
D. Wang ◽  
A. Sivasubramaniam ◽  
H.K. Fathy
Keyword(s):  
Lithium Ion Battery ◽  
Demand Response ◽  
Lithium Ion ◽  
Multi Objective Optimization ◽  
Battery Storage ◽  
Multi Objective

scholarly journals A Multi-Objective Optimization Approach towards a Proposed Smart Apartment with Demand-Response in Japan

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 127 ◽  
Author(s):  
Yuta Susowake ◽  
Hasan Masrur ◽  
Tetsuya Yabiku ◽  
Tomonobu Senjyu ◽  
Abdul Motin Howlader ◽  
...  
Keyword(s):  
High Voltage ◽  
Demand Response ◽  
Low Voltage ◽  
Heat Pumps ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective ◽  
Trade Offs ◽  
To Receive

In Japan, residents of apartments are generally contracted to receive low voltage electricity from electric utilities. In recent years, there has been an increasing number of high voltage batch power receiving contracts for condominiums. In this research, a high voltage batch receiving contractor introduces a demand–response in a low voltage power receiving contract, which maximizes the profit of a high voltage batch receiving contractor and minimizes the electricity charge of residents by utilizing battery storage, electric vehicles (EV), and heat pumps. A multi-objective optimization algorithm calculates a Pareto solution for the relationship between two objective trade-offs in the MATLAB ® environment.

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