Energy Storage System and Load Shedding – Matlab Programming

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Electrical energy and Battery Storage

To help utilities move forward to achieve goals, several benefits are offered by the Energy Storage System. The primary benefit of ESS is the reliability of the system. As the interested customers are willing to pay to avoid any interruption in power, the costs are optimized concerning reliability. A methodology for the Improvement of system reliability is discussed in this article.

Cost-effective Improvement by Energy Storage Technology:

ESSs are devices or systems that store energy and supply electricity on demand. The most critical components of an ESS are energy storage devices, a battery management system (BMS), power converters, and a controller.

 ESS is one of the most optimistic technologies, which can bear smart grid incorporation due to its capacity. They can enable successful islanding and facilitate the integration of high penetration levels of Renewable Energy Sources (RES).
Simulation is done through the Matlab program.

Several Benefits Provided by ESS:

  1. Efficient expansion alternative
  2. Side Management
  3. Methods of Mitigating quality issues

When there is a network outage, Distributed Energy Resources (DERs) provide the system with the desired power. Therefore, system reliability is enhanced by preventing energy loss supplied to unaffected customers during aggravation. The island formation improves the reliability of the system when any disturbance occurs.

format shortG
%% CDFN Evaluation
CDFN_CI= (1604*2)/60 + (396.3*30)/60 + 282*1 + 298.9*4 + 206.1*8;
CDFN_R= (16.8*2)/60 + (3.5*1)/2 + 2.2*1 + 1.2*4 + 0.9*8;
% Taking 30% Small C & I, 70% Residential.
CDFN=0.3*CDFN_CI + 0.7*CDFN_R;

%% Interuption Cost

% Base Case Interuption Cost
Ny=30; PSH=1; PD=3715; 
BC_Cost= (CDFN_BC * PSH * PD )/Ny;
BC_Cost= BC_Cost/1000000;

% Battery Technology Case Interuption Cost
Ny=30; PSH=1; PD=3715; 
BT_Cost= (CDFN_BT * PSH * PD )/Ny;
BT_Cost= BT_Cost/1000000;

sprintf('Base Case Interuption Cost ($ million)=%d, Battery Technology Interuption Cost ($ million)=%d',BC_Cost,BT_Cost)

%% Battery Technology Comparison
% LA
Cp=10.07; Cm=15; Sds=100; Ce=17.55; Eds=100; ECOST=CDFN;
LA_Cost= ((Cp + Cm) * Sds) + (Ce * Eds) + ECOST;
Cp=57.55; Cm=28; Sds=100; Ce=14.39; Eds=100;
CAS_Cost= ((Cp + Cm) * Sds) + (Ce * Eds) + ECOST;
% Na/S
Cp=57.55; Cm=20; Sds=100; Ce=28.78; Eds=100;
NaS_Cost= ((Cp + Cm) * Sds) + (Ce * Eds) + ECOST;
% VR
Cp=20; Sds=100; Ce=42.59; Eds=100;
% Capital Power cost for VR battery is included in capital energy cost.
% See, Table 3 for further details
VR_Cost= (Cp * Sds) + (Ce * Eds) + ECOST;

Distributed Storage and Renewable Energy

As utilities and their customers increasingly strive to meet sustainability goals, the electric grid is evolving. In the past, both generation and consumption of electric energy were centralized on the grid. While traditional generators are still the most significant energy source, various technologies that enable energy generation and storage at or near consumption are changing how we use electric power.

These technologies – collectively called Distributed Energy Resources (DERs) – include solar photovoltaic panels, BESS or battery energy storage systems, demand response, energy efficiency, and electric vehicles. Demand response helps utilities manage peak demand by shifting electricity usage in high-demanding periods. Successful operation improves system readability too.

Due to its probabilistic nature, the generated power from Distributed Generation (DG), which includes wind turbines, is not dependable for PV arrays. Distributed storage (DS) can be used as a backup source.

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A model for allocating DS units in a distribution system is proposed to improve system reliability and reduce system costs. In this case study, two DS storage systems are evaluated against a base case with no data storage system. Each storage technology is assessed using a variety of metrics, including cost, total capacity, and average response time. 

The results show that integrating DS units with distribution systems reduces the utility’s annual costs because of their ability to enable islanding and reduce the number of interruptions, thus providing a more cost-effective means of improving system reliability. It is necessary to conduct a sensitivity analysis to determine the influence of interruption costs on the results of the proposed solution. 

ESS stores energy and provides us the energy when there is a demand. It includes battery management, converters, and controllers. As the power outage increases, new DER technologies are in high demand. 

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Looking into further assistance, hire Matlab expert here.
Other relevant articles are PSO Matlab Code, Microgrid Matlab Code.


A. S. A. Awad, T. H. M. EL-Fouly and M. M. A. Salama, “Optimal ESS Allocation and Load Shedding for Improving Distribution System Reliability,” in IEEE Transactions on Smart Grid, vol. 5, no. 5, pp. 2339-2349, Sept. 2014, doi: 10.1109/TSG.2014.2316197. 

73 thoughts on “Energy Storage System and Load Shedding – Matlab Programming”

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