Least cost generation expansion planning with solar power plant using Differential Evolution algorithm

Abstract

a b s t r a c tThe issue of balance between the benefits of greater renewable penetration with the cost of adapting conventional base load systems is drawing the attention of power system planners across the globe. The study region, State of Tamilnadu, India, faces acute power shortages and frequent power cuts, though the installed capacity of the state is higher than the peak demand and is planning more solar additions. The impact of the inclusion of solar power plants is analyzed, for 6-year and 14-year planning horizons, using the model formulated, integrating all critical elements of the system, employing Differential Evolution (DE) algorithm. A balanced approach is adopted to understand the long term impact of solar additions by realistically imposing Total Emission Reductions Constraints (TERC), and Emission Treatment Penalty Costs (ETPC) on the remaining portion of pollution. The sensitivity of the system generation mix and the system reliability, to different solar power development and emissions reduction scenarios is also studied. The resulting variations in different cost components are also reported. The study will have greater utility for the planners who are currently involved in the long term planning of systems expected to have increasing proportion of RET plants.

Least‐cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources

“…The technical and economic specification of the SwnS is given in Table . The load data are the same as that of Case Study 2 …”

“…There are also three emission reduction cases which include emission reduction by 10%, 20%, and 30% (ER10, ER20, and ER30). The emissions coefficients for RES are zero …”

Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission

“…Based on the previous researches by the authors, the following factors are considered in the research: The power plants proposed in “The Vision Tamil Nadu 2023” by Tamil Nadu government are North Chennai Thermal Power Station Stage III and Uppur Thermal Power Plant, Udangudi Thermal Power Station Stage I and II, PPN Gas Power Generation Pvt. Ltd., GMR Vasavi Oil Power Plant, Kudankulam Nuclear Power Plant, Bhavani Barrage, wind, Adani Solar, Biomass, and Kundah pumped storage.…”

“…Moreover, GEP problem is also solved by metaheuristic optimization techniques such as ant colony, tabu search, genetic algorithm, honey bee algorithm, artificial immune system, evolutionary programming, and PSO . Also, GEP studies are carried out by considering various single objectives such as minimizing planning cost, maximizing planning profit in deregulated electricity market, improving reliability, reducing environmental pollution, and considering demand‐side management programs . Eventually, GEP problem has been solved subject to several constraints such as reliability, environmental pollution, investment cost, and security …”

Multistage generation expansion planning incorporating large scale energy storage systems and environmental pollution

“…In addition, Meta-heuristic optimization techniques such as ant colony [12], tabu search [12], genetic algorithms [13], honey bee algorithm [14], artificial immune system [15], evolutionary programming [12], and PSO [16] have also been used to solve GEP. GEP has also been studied considering several objective functions such as minimizing planning cost [2,17], maximizing planning profit in deregulated electricity market [18], maximizing reliability [13], minimizing environmental pollution [19], and considering demand-side management programs [20]. As well, GEP has been investigated subject to various constraints such as reliability [3], environmental pollution [21], investment cost [22], and security [23].…”