Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration
This work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. The outer-stage problem simultaneously minimizes the long-term expected pl...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Elsevier Ltd
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833352447198756864 |
|---|---|
| author | Ba-swaimi S. Verayiah R. Ramachandaramurthy V.K. ALAhmad A.K. Padmanaban S. |
| author2 | 58510833400 |
| author_facet | 58510833400 Ba-swaimi S. Verayiah R. Ramachandaramurthy V.K. ALAhmad A.K. Padmanaban S. |
| author_sort | Ba-swaimi S. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. The outer-stage problem simultaneously minimizes the long-term expected planning costs, power losses, and voltage deviations by determining the optimal sizing and placement of renewable energy resources (RESs), such as solar photovoltaic distributed generators (PV-DGS), wind-DGs, and battery energy storage systems (BESSs). In contrast, the inner-stage problem emphasizes the reduction of hourly operational expenses, power losses, and voltage deviations through the identification of optimal scheduling for demand response programs (DRPs) and network reconfiguration (NR). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to address the outer-stage optimization problem. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to address the inner-stage issue. In both phases, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is utilized at the conclusion of each iteration to identify the ideal solution from a collection of non-dominated solutions. Monte Carlo simulation (MCS) is utilized to model the system's unknown factors, including solar radiation, wind speed, load demand, and energy pricing. Subsequently, the backward reduction algorithm (BRA) is employed to streamline the resulting scenarios into a more feasible and representative subset, therefore mitigating excessive computational effort. The suggested model is validated utilizing the IEEE 33-bus DS developed in MATLAB R2023b. Simulation outcomes from various case studies indicate that incorporating optimal DRP and NR scheduling into a hybrid system of RESs and BESSs enhances renewable energy penetration by 17.39% compared to the case utilizing just BESSs. Moreover, the established model, featuring a wind-DG/PV-DG/BESS/DRP/NR configuration, achieves significant improvements in all objective functions, including a 31.14% reduction in total system cost, a 61.67% decrease in power loss, and a 58.11% improvement in voltage deviation, compared to the base case. ? 2024 The Author(s) |
| format | Article |
| id | my.uniten.dspace-36344 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | my.uniten.dspace-363442025-03-03T15:42:01Z Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration Ba-swaimi S. Verayiah R. Ramachandaramurthy V.K. ALAhmad A.K. Padmanaban S. 58510833400 26431682500 6602912020 59312509000 59219326900 Associative storage Asynchronous generators Battery storage Compact disks Depreciation Geophysical prospecting Geothermal fields Integer programming Mineral exploration Nonlinear programming Nuclear batteries Organs (musical instruments) Parallel architectures Phosphate deposits Solar energy Strategic planning Surface waters Water wells Wind power Battery energy storage systems Demand response programs Long-term optimal planning Networks reconfiguration Optimal planning Penetration level Powerloss Renewable energy penetration level Renewable energy penetrations Voltage deviations Stochastic programming This work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. The outer-stage problem simultaneously minimizes the long-term expected planning costs, power losses, and voltage deviations by determining the optimal sizing and placement of renewable energy resources (RESs), such as solar photovoltaic distributed generators (PV-DGS), wind-DGs, and battery energy storage systems (BESSs). In contrast, the inner-stage problem emphasizes the reduction of hourly operational expenses, power losses, and voltage deviations through the identification of optimal scheduling for demand response programs (DRPs) and network reconfiguration (NR). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to address the outer-stage optimization problem. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to address the inner-stage issue. In both phases, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is utilized at the conclusion of each iteration to identify the ideal solution from a collection of non-dominated solutions. Monte Carlo simulation (MCS) is utilized to model the system's unknown factors, including solar radiation, wind speed, load demand, and energy pricing. Subsequently, the backward reduction algorithm (BRA) is employed to streamline the resulting scenarios into a more feasible and representative subset, therefore mitigating excessive computational effort. The suggested model is validated utilizing the IEEE 33-bus DS developed in MATLAB R2023b. Simulation outcomes from various case studies indicate that incorporating optimal DRP and NR scheduling into a hybrid system of RESs and BESSs enhances renewable energy penetration by 17.39% compared to the case utilizing just BESSs. Moreover, the established model, featuring a wind-DG/PV-DG/BESS/DRP/NR configuration, achieves significant improvements in all objective functions, including a 31.14% reduction in total system cost, a 61.67% decrease in power loss, and a 58.11% improvement in voltage deviation, compared to the base case. ? 2024 The Author(s) Final 2025-03-03T07:42:01Z 2025-03-03T07:42:01Z 2024 Article 10.1016/j.ecmx.2024.100766 2-s2.0-85207794449 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207794449&doi=10.1016%2fj.ecmx.2024.100766&partnerID=40&md5=a9ce8160f5350c3ec3bdc720c718b7fd https://irepository.uniten.edu.my/handle/123456789/36344 24 100766 Elsevier Ltd Scopus |
| spellingShingle | Associative storage Asynchronous generators Battery storage Compact disks Depreciation Geophysical prospecting Geothermal fields Integer programming Mineral exploration Nonlinear programming Nuclear batteries Organs (musical instruments) Parallel architectures Phosphate deposits Solar energy Strategic planning Surface waters Water wells Wind power Battery energy storage systems Demand response programs Long-term optimal planning Networks reconfiguration Optimal planning Penetration level Powerloss Renewable energy penetration level Renewable energy penetrations Voltage deviations Stochastic programming Ba-swaimi S. Verayiah R. Ramachandaramurthy V.K. ALAhmad A.K. Padmanaban S. Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration |
| title | Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration |
| title_full | Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration |
| title_fullStr | Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration |
| title_full_unstemmed | Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration |
| title_short | Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration |
| title_sort | two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration |
| topic | Associative storage Asynchronous generators Battery storage Compact disks Depreciation Geophysical prospecting Geothermal fields Integer programming Mineral exploration Nonlinear programming Nuclear batteries Organs (musical instruments) Parallel architectures Phosphate deposits Solar energy Strategic planning Surface waters Water wells Wind power Battery energy storage systems Demand response programs Long-term optimal planning Networks reconfiguration Optimal planning Penetration level Powerloss Renewable energy penetration level Renewable energy penetrations Voltage deviations Stochastic programming |
| url_provider | http://dspace.uniten.edu.my/ |