Multi-Objective Unit Commitment With Renewable Energy Using GSA Algorithm

Abstract

With increasing concerns of global warming and environmental change caused by the burning of fossil fuels, the operating paradigm of power system is shifting from minimum cost to minimum cost with minimum emission approach. Due to conflicting nature of economic and emission objectives, the generation scheduling becomes a multi-objective problem. In this paper, a multi-objective gravitational search algorithm (MOGSA) is used to provide Pareto optimal solutions which present the possible tradeoff between the cost and emission objectives of unit commitment (UC) problem. The MOGSA technique is used for solving the environmental and economic dispatch problem considering hydro, pump storage plant, wind farm and solar farm, with and without ramp rates. The simulation results demonstrate the effectiveness of the proposed approach in solving multi-objective UC problem.Cost coefficients of the ith generating unitEmission coefficients of the ith generating unitSystem load demand at hour t F T /E T Cost and emission over the scheduling horizon G t Forecast solar radiation at hour t GstSolar radiation in the standard environmentSchedule state of ith thermal unit at hour t NT/NH Number of thermal/hydro units Ph k (t)Generation of kth hydro unit at hour t P i (t)Generation of ith thermal unit at hour t P (max, min),iMax and min output power of unit i P (max, min),P,G Max and min output power of PSP P i max (t) Max generation of ith thermal unit at hour t P i min (t) Min generation of ith thermal unit at hour t P PG (t)Pumping and generation of PSP unit at hour t P Sn (t) Rated power output of the PV generator P WT (t)Total actual wind generation at hour t P WN Rated wind power output TO k (t)Turbine out flow for reservoir k during hour t TO k(max/min) Max/min turbine water out flow for reservoir k SR(t)

A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

“…Although the GSA approach has demonstrated excellent performance when compared to other state-of-the-art optimization methods [25][26][27][28][29], it is known to suffer from several problems commonly found in such population-based approaches. In particular, the dependence on the fitness function for calculating the mass of search agents often causes GSA search speed to get deteriorated as agents become heavier, essentially manifesting a slow converge rate which worsens as the iterations increase.…”

Opportunities vis-à-vis threat to thermal genera-tion due to rising renewable energy penetration

“…The wind power generation model is explained in Equations (10-15) [7][8][9]. [8], Psn is maximum generation capability of solar system taken as 500 MW.…”

Investigation of PBUC problem with RES and EV in restructured environment

“…These studies cover both cost-based as well as restructured unit commitment problems. Further, there are research studies that have considered the fluctuation in wind speed using an improved gravitational search algorithm (IGSA) (Ji et al, 2014;Shukla and Singh, 2016a), ambiguity in wind power generators considering the gray wolf optimization (Shahbazitabar and Abdi, 2018), ambiguity in wind speed and uncertainty in SOC levels of BESS (Alqunun et al, 2020), the addition of wind that acts as reserve power and plug-in electric vehicles that are used for storage (Mousavi-Taghiabadi et al, 2020), penetration in wind power on different loads (Alqunun, 2020). The uncertainty in solar irradiance using the binary version real genetic algorithm (BCRGA) (Sediqi et al, 2019), fluctuation in wind speed, solar irradiance and EVs by teacher learning-based optimization (TLBO) in (Govardhan and Roy, 2015), ambiguity in PV and electric vehicles (mainly used as storage) using cuckoo search algorithm (Maghsudlu and Mohammadi, 2018), fluctuation of wind and solar power considered by lasso regression and multiple regression analysis (Nishiura and Matsuhashi, 2019), fluctuation in wind speed and solar irradiance using linear integer programing toolbox (INTLINPROG) (Matin Ibrahimi et al, 2019), hybridized gravitation search algorithm considering wind turbine (Dahiya et al, 2015(Dahiya et al, , 2017.…”