Dynamic optimal power flow for DC microgrids with distributed battery energy storage systems

2016 ◽  
Author(s):  
Thomas Morstyn ◽  
Branislav Hredzak ◽  
Vassilios G. Agelidis
Keyword(s):  
Energy Storage ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Dc Microgrids ◽  
Optimal Power ◽  
Battery Energy Storage Systems ◽  
Battery Energy

scholarly journals Pareto-optimal power flow control in heterogeneous battery energy storage systems

2022 ◽  
Vol 48 ◽  
pp. 103803
Author(s):  
Markus Mühlbauer ◽  
Fabian Rang ◽  
Herbert Palm ◽  
Oliver Bohlen ◽  
Michael A. Danzer
Keyword(s):  
Energy Storage ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Pareto Optimal ◽  
Battery Energy Storage ◽  
Power Flow Control ◽  
Optimal Power ◽  
Battery Energy Storage Systems ◽  
Battery Energy

scholarly journals Optimal Location-Reallocation of Battery Energy Storage Systems in DC Microgrids

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2289 ◽  
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Edwin Rivas-Trujillo
Keyword(s):  
Energy Storage ◽  
Storage Systems ◽  
Optimal Location ◽  
Mixed Integer ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Dc Microgrids ◽  
General Algebraic Modeling System ◽  
Battery Energy Storage Systems ◽  
Battery Energy

This paper deals with the problem of optimal location and reallocation of battery energy storage systems (BESS) in direct current (dc) microgrids with constant power loads. The optimization model that represents this problem is formulated with two objective functions. The first model corresponds to the minimization of the total daily cost of buying energy in the spot market by conventional generators and the second to the minimization of the costs of the daily energy losses in all branches of the network. Both the models are constrained by classical nonlinear power flow equations, distributed generation capabilities, and voltage regulation, among others. These formulations generate a nonlinear mixed-integer programming (MINLP) model that requires special methods to be solved. A dc microgrid composed of 21-nodes with existing BESS is used for validating the proposed mathematical formula. This system allows to identify the optimal location or reallocation points for these batteries by improving the daily operative costs regarding the base cases. All the simulations are conducted via the general algebraic modeling system, widely known as the General Algebraic Modeling System (GAMS).

scholarly journals Battery Energy Storage Systems (BESSs) and the Economy-Dynamics of Microgrids: Review, Analysis, and Classification for Standardization of BESSs Applications

2021 ◽  
Author(s):  
Mohsen Eskandari ◽  
Amin Rajabi ◽  
Andrey Savkin ◽  
Mohammad H. Moradi ◽  
Zhao Yang Dong
Keyword(s):  
Energy Storage ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Future Trend ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Time Constants ◽  
The Future ◽  
Battery Energy Storage Systems ◽  
Battery Energy

The literature on microgrid (MG) studies can be categorized as those that investigated the dynamics or economics of MG systems. Due to the important roles of battery energy storage systems (BESSs) in MGs, the BESSs have been involved in both economics and dynamics studies but mostly separately due to the different time constants of studies. Whereas, in the complicated modern MGs, the BESS is the joint point that bridges these two studies. Thus, studying merely one aspect of economy-dynamic without considering the other aspect would not be accurate. To bridge this gap, this paper reviews, analysis and classifies the BESSs applications, based on their time constants, by merging of which helps researchers to develop joint economy-dynamics models that would be the future trend in the field. The classified BESS applications are 1) inertia synthesis, 2) primary frequency response to compensate slow response time of micro-sources (MSs) for load tracking, 3) real-time energy management for the integration of intermittent renewables, 4) economic dispatch and optimal power flow for improving steady-state performance, and 5) slack bus realization. The research gaps and future trends have been discussed throughout the paper and are summarized in the future trend section.

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