scholarly journals A Mobile Energy Storage Unit Serving Multiple EV Charging Stations

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2969
Author(s):  
Mohamed M. Elmeligy ◽  
Mostafa F. Shaaban ◽  
Ahmed Azab ◽  
Maher A. Azzouz ◽  
Mohamed Mokhtar
Keyword(s):  
Energy Storage ◽  
New Technologies ◽  
Storage System ◽  
Geographical Area ◽  
Operating Cost ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Storage Unit ◽  
Charging Stations ◽  
Ev Charging

Due to the rapid increase in electric vehicles (EVs) globally, new technologies have emerged in recent years to meet the excess demand imposed on the power systems by EV charging. Among these technologies, a mobile energy storage system (MESS), which is a transportable storage system that provides various utility services, was used in this study to support several charging stations, in addition to supplying power to the grid during overload and on-peak hours. Thus, this paper proposes a new day-ahead optimal operation of a single MESS unit that serves several charging stations that share the same geographical area. The operational problem is formulated as a mixed-integer non-linear programming (MINLP), where the objective is to minimize the total operating cost of the parking lots (PLs). Two different case studies are simulated to highlight the effectiveness of the proposed system compared to the current approach.

scholarly journals Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1124 ◽  
Author(s):  
Byeong-Cheol Jeong ◽  
Dong-Hwan Shin ◽  
Jae-Beom Im ◽  
Jae-Young Park ◽  
Young-Jin Kim
Keyword(s):  
Energy Storage ◽  
Scheduling Algorithm ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Operating Cost ◽  
Optimal Schedule ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Actual Case

Optimal operation scheduling of energy storage systems (ESSs) has been considered as an effective way to cope with uncertainties arising in modern grid operation such as the inherent intermittency of the renewable energy sources (RESs) and load variations. This paper proposes a scheduling algorithm where ESS power inputs are optimally determined to minimize the microgrid (MG) operation cost. The proposed algorithm consists of two stages. In the first stage, hourly schedules during a day are optimized one day in advance with the objective of minimizing the operating cost. In the second stage, the optimal schedule obtained from the first stage is repeatedly updated every 5 min during the day of operation to compensate for the uncertainties in load demand and RES output power. The ESS model is developed considering operating efficiencies and then incorporated in mixed integer linear programming (MILP). Penalty functions are also considered to acquire feasible optimal solutions even under large forecasting errors in RES generation and load variation. The proposed algorithm is verified in a campus MG, implemented using ESSs and photovoltaic (PV) arrays. The field test results are obtained using open-source software and then compared with those acquired using commercial software.

Determination of an Optimal Operating Schedule for Thermal Units with an Energy Storage System

2008 ◽  
Vol 9 (3) ◽  
Author(s):  
Tomonobu Senjyu ◽  
Shantanu Chakraborty ◽  
Ahmed Yousuf Saber ◽  
Atsushi Yona ◽  
Toshihisa Funabashi
Keyword(s):  
Energy Storage ◽  
Unit Commitment ◽  
Storage System ◽  
Operating Cost ◽  
Energy Storage System ◽  
Optimal Operation ◽  
Total Production ◽  
Priority List ◽  
Hourly Output

This paper presents a determination methodology for finding optimal operation schedules of thermal units (namely unit commitment) integrated with an energy storage system (ESS) to minimize total operating costs. A generic ESS formulation along with a method for solving unit commitment (UC) of thermal units with ESS is proposed to serve this purpose. The problem of unit commitment with an ESS is solved using the Priority List method. Intelligent Genetic algorithm (GA) is included in the algorithm for generating new and potential solutions. The proposed method consists of two steps. The first step is to determine the schedule of ESS and the schedule of thermal units. The second step is to dispatch the hourly output of thermal units and the ESS which comply a minimized total production cost. The proposed method is applied to a power system with ten thermal units and a large ESS. The presented simulation results show that the schedule of thermal units with an ESS of a particular life cycle, achieved by the proposed method, minimizes the operating cost. The discussion regarding the determination of schedule thermal units (TU) along with the integrated ESS may interest many types of ESS due to their generalized formulations.

Thermal Unit Commitment Problem with Wind Power and Energy Storage System

2013 ◽  
Vol 347-350 ◽  
pp. 1455-1461 ◽  
Author(s):  
Rui Wang ◽  
Yu Guang Xie ◽  
Kai Xie ◽  
Ya Qiao Luo
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Branch And Bound ◽  
Unit Commitment ◽  
Storage System ◽  
Operating Cost ◽  
Peak Load ◽  
Energy Storage System ◽  
Mixed Integer ◽  
Unit Commitment Problem

This paper presents a methodology for solving unit commitment (UC) problem for thermal units integrated with wind power and generalized energy storage system (ESS).The ESS is introduced to achieve peak load shaving and reduce the operating cost. The volatility of wind power is simulated by multiple scenarios, which are generated by Latin hypercube sampling. Meanwhile, the scenario reduction technique based on probability metric is introduced to reduce the number of scenarios so that the computational burden can be alleviated. The thermal UC problem with volatile wind power and ESS is transformed to a deterministic optimization which is formulated as the mixed-integer convex program optimized by branch and bound-interior point method. During the branch and bound process, the best first search and depth first search are combined to expedite the computation. The effectiveness of the proposed algorithm is demonstrated by a ten unit UC problem.

scholarly journals Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 230 ◽  
Author(s):  
Akhtar Hussain ◽  
Van-Hai Bui ◽  
Ju-Won Baek ◽  
Hak-Man Kim
Keyword(s):  
Performance Evaluation ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Performance Ratio ◽  
Cost Index ◽  
Stationary Energy ◽  
And Performance ◽  
Charging Stations ◽  
Ev Charging

In order to minimize the peak load of electric vehicles (EVs) and enhance the resilience of fast EV charging stations, several sizing methods for deployment of the stationary energy storage system (ESS) have been proposed. However, methods for assessing the optimality of the obtained results and performance of the determined sizes under different conditions are missing. In order to address these issues, a two-step approach is proposed in this study, which comprises of optimality analysis and performance evaluation steps. In the case of optimality analysis, random sizes of battery and converter (scenarios) are generated using Monte Carlo simulations and their results are compared with the results of sizes obtained from sizing methods. In order to carry out this analysis, two performance analysis indices are proposed in this study, which are named the cost index and the power index. These indices respectively determine the performance of the determined sizes in terms of total network cost and performance ratio of power bought during peak intervals and investment cost of the ESS. During performance evaluation, the performance of the determined sizes (battery and converter) are analyzed for different seasons of the year and typical public holidays. Typical working days and holidays have been analyzed for each season of the year and suitability of the determined sizes is analyzed. Simulation results have proved that the proposed method is suitable for determining the optimality of results obtained by different sizing methods.

scholarly journals Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 522
Author(s):  
Rajitha Udawalpola ◽  
Taisuke Masuta ◽  
Taisei Yoshioka ◽  
Kohei Takahashi ◽  
Hideaki Ohtake
Keyword(s):  
Energy Storage ◽  
Power System ◽  
Solar Irradiance ◽  
Storage System ◽  
Energy Storage System ◽  
Mixed Integer ◽  
Battery Energy Storage System ◽  
Large Power ◽  
Battery Energy Storage ◽  
Battery Energy

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

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