Multi-period energy planning model under uncertainty in market prices and demands of energy resources: A case study of Korea power system

• Published in: Chemical engineering research & design Vol. 114; pp. 341 - 358
• Main Authors: Choi, Go Bong, Lee, Seok Goo, Lee, Jong Min
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2016
• Subjects: Disposal of waste; Energy planning; Learning curve; Mixed integer linear programming; Price and demand forecasting; Mixed integer linear programming; Learning curve; Price and demand forecasting; Disposal of waste; Energy planning
• ISSN: 0263-8762
• DOI: 10.1016/j.cherd.2016.08.033

[Display omitted] •Price and demand forecasting as well as technical improvement are reflected for the energy planning.•Mathematical models are formulated considering spatial granularity and renewable standard portfolio.•Coal and photovoltaic energy are mainly used to generate electricity.•Disposal of waste and CO2 emission cost can affect the optimal plan of energy supply. This paper presents a mathematical framework for planning an energy supply system. The proposed model takes into account important factors affecting the total cost of supplying commercial energy such as market prices and waste disposal costs. Forecasting models are employed to predict future prices and demand levels. Given the renewable energy portfolio standard that promotes energy generation from renewable sources, a large-scale nonlinear planning problem is decomposed into a mixed integer linear program and a nonlinear program for traditional and renewable energy sectors, respectively. Nonlinearity arises from the learning curve that describes cost changes through future advances in technologies for exploiting renewable energy sources. The suggested approach can provide insights for crafting long-term policies, which can then be revised with updated information. The modeling framework is illustrated using public data from South Korea, interpreted in light of country's policies. Results based on various scenarios indicate that uncertainty and the cost of waste disposal facilities significantly affect the optimal policy choice.