Optimal power dispatch considering dispatchable solar and wind generation using particle swarm optimization | |
| Author | Wannakorn Supingklad |
| Call Number | AIT Thesis no.ET-16-10 |
| Subject(s) | Solar energy--Decision making Wind power--Decision making |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy |
| Publisher | Asian Institute of Technology |
| Abstract | A thesis proposes dynamic combined economic and emission dispatch (DCEED) considering dispatchable renewable energy sources (RESs) consist of solar, wind and battery energy storage (BESs) generation using Stochastic Weight Trade-off Particle Swarm Optimization (SWT-PSO). The DCEED is a combined multi-objective function to minimize a combined emission, generator fuel cost, wind cost, solar cost and BESs cost functions over a 24-hourtime horizon subject to power balance constraint and other generator and system operating constraints including generator limits, renewable energy penetration limit, prohibited operating zones, and ramp rate limit constraints. Solar generation of each unit is dispatched as (ON or OFF) status known as solar unit commitment, and its solar cost function is given in unit currency/MWh. On the other hand, the sum of wind and BESs generation is dispatched as power generation (MW) within its hourly forecast value. The Weibull probability density function(PDF) is used to characterize the wind speed profiles to forecast values. The wind cost function consists of direct, overestimation, and underestimation cost function. To show the effectiveness of wind, BESs and solar integration on total generation cost and emission of microgrid, nine scenarios are considered including (i) without RESs, (ii) with dispatchable wind and BESs generation, (iii) with non-dispatchable wind generation, (iv) with solar unit commitment, (v) with non-dispatchable solar generation, (vi) with solar unit commitment, dispatchable wind and BESs generation, (vii) with non-dispatchable wind generation and solar unit commitment, (viii) with non-dispatchable solar and dispatchable wind generation and (ix) with non-dispatchable RESs. Test results indicate that the proposed method gives better solution than Basic Particle Swarm Optimization (BPSO), Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC)and Adaptive Particle Swarm Optimization (APSO), leading to substantial cost savings. |
| Year | 2016 |
| Type | Thesis |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Energy Technology (ET) |
| Chairperson(s) | Weerakorn Ongsakul |
| Examination Committee(s) | Singh, Jai Govind;Dhakal, Shobhakar; |
| Scholarship Donor(s) | Royal Thai Government;Asian Institute of Technology Fellowship |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2016 |