LOAD MODEL DRIVEN OPTIMIZATION AND RELIABILITY EVALUATION OF RENEWABLE HYBRID MICROGRIDS USING PSO

DESIGN DETAILS
The growing integration of distributed energy resources (DERs) including rooftop photovoltaic (PV) systems, wind turbines (WT), battery energy storage systems (BESS), and electric vehicles (EVs) presents both significant opportunities and challenges for the reliability and stability of smart grids. When multiple, complementary renewable energy sources (RESs) are combined into a single configuration, the system is referred to as a renewable energy hybrid system. The resulting network, characterized by its relatively smaller scale and localized control compared to the main grid, is known as a microgrid (MG).

This study presents a multi-objective optimization framework aimed at determining the optimal placement and sizing of PV, WT, BESS, and EV charging stations (EVCS) in combination. The proposed framework considers four load models such as Constant Power, Constant Current, Constant Impedance, and the composite ZIP model and targets objectives such as minimizing power losses and enhancing voltage stability.

An additional critical aspect addressed in the design is network reliability, which has become a key parameter in modern power systems for ensuring high levels of security and adequacy. To evaluate reliability, the framework incorporates three essential metrics: the customer composite damage function (CCDF), average load, and failure rate. A comprehensive reliability assessment approach is introduced using the Particle Swarm Optimization (PSO) algorithm to effectively manage the placement and sizing of DERs.
The proposed methodology is validated using MATLAB on 33- and 69-bus test systems, with variations in power factors: PV, WT, and BESS operate at unity power factor, while EVs operate at 0.8 power factor. The approach not only enhances overall system reliability but also accounts for the inherent uncertainties associated with renewable energy sources.

OBJECTIVE FUNCTION
The objective function,F(k)=min{w_1 f_1 (k)+w_2 f_2 (k)+w_3 (1/(f_3 (k)))}
where,
f_1 (k)=min∑_(i=1)^br▒〖R_i*I_i^2 〗 , Power Loss
f_2 (k)=1/b ∑_(k=1)^b▒|1-V_k |^2 , Average Voltage Deviation Index
f_3 (k)=[|V_k |^4-4(P_x x_jk+Q_k r_jk )^2-4(P_k r_jk+Q_k_kx_jk)|V_k |^2 ],Voltage Stability Index

Scenarios
1. Basecase
2. Optimal allocation of Roof-Top PV
3. Optimal allocation of ESS
4. Optimal allocation of EVCS
5. Simultaneous allocation of Roof Top PV, BESS and EVCS

Matlab Simulation Results
1. Active Power Loss (kW)
2. Reactive Power Loss (kVAr)
3. Minimum Voltage (PU) @ Bus
4. Maximum Voltage (PU) @ Bus
5. Optimal EVCS, Roof Top PV, and BESS Location
6. Optimal EVCS, Roof Top PV, and BESS Size
7. Execution Time

Matlab Simulation Figures
1. Voltage Profile
2. Convergence graph
3. Active and Reactive Power Loss

REFERENCES
Reference Paper-1: Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network with Randomly Distributed Rooftop Photovoltaic Systems
Author’s Name: Willy Stephen Tounsi Fokui, Michael J. Saulo, And Livingstone Ngoo
Source: IEEE
Year: 2021

Reference Paper-2: Optimal placement and sizing of photovoltaics and battery storage in distribution networks
Author’s Name: Riad Chedid and Ahmad Sawwas
Source: Wiley
Year:2019

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Request source code for academic purpose, fill REQUEST FORM below,
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If you need Matlab p-code(encrypted files) to check the results, contact us by email to info@verilogcourseteam.com

You may also contact +91 7904568456 by WhatsApp Chat, for paid services. We are also available on Telegram and Signal.

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