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.

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Summary Developing countries like India necessitates installation of new power plants to meet the elevating electricity demand. Several single‐objective mathematical formulations are modeled and solved previously for short‐term and long‐term power generation expansion planning aiding power system planners to ensure optimal decision. The input data, such as forecasted demands, economic and technical data of the generating units are numerous and unmanageable to be analyzed manually. Modern power systems have faced major challenge in solving multiple objectives such as least cost and minimal greenhouse gas emissions simultaneously. This paper presents an application of multiobjective differential evolution algorithm to multistage, multiobjective generation expansion planning problem for Tamil Nadu, a state in India. The multiobjective generation expansion planning problem has been solved for short‐term (6 years) and long‐term (12 years) planning horizon by considering 8 different cases and also compared with Nondominated Sorting Genetic Algorithm version II.

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