MING-CHE HULu S.-Y.Chen Y.-H.2020-01-142020-01-1420160306-2619https://scholars.lib.ntu.edu.tw/handle/123456789/449047In the electricity market, demand response programs are designed to shift peak demand and enhance system reliability. A demand response program can reduce peak energy demand, power transmission congestion, or high energy-price conditions by changing consumption patterns. The purpose of this research is to analyze the impact of a demand response program in the energy market, under demand uncertainty. A stochastic–multiobjective Nash–Cournot competition model is formulated to simulate demand response in an uncertain energy market. Then, Karush–Kuhn–Tucker optimality conditions and a linear complementarity problem are derived for the stochastic Nash–Cournot model. Accordingly, the linear complementarity problem is solved and its stochastic market equilibrium solution is determined by using a general algebraic modeling system. Additionally, the case of the Taiwanese electric power market is taken up here, and the results show that a demand response program is capable of reducing peak energy consumption, energy price, and carbon dioxide emissions. The results show that demand response program decreases electricity price by 2–10%, total electricity generation by 0.5–2%, and carbon dioxide emissions by 0.5–2.5% in the Taiwanese power market. In the simulation, demand uncertainty leads to an 2–7% increase in energy price and supply risk in the market. Additionally, tradeoffs between cost and carbon dioxide emissions are presented. © 2016 Elsevier LtdDemand response; Multiobjective; Nash–Cournot model; Stochastic; Uncertainty analysis[SDGs]SDG7[SDGs]SDG13Carbon dioxide; Commerce; Costs; Energy utilization; Global warming; Power markets; Stochastic models; Stochastic systems; Carbon dioxide emissions; Cournot model; Demand response; Demand response programs; Linear complementarity problems; Multi objective; Stochastic; Stochastic market equilibriums; Uncertainty analysis; carbon dioxide; carbon emission; demand analysis; electrical power; electricity generation; energy market; energy use; numerical model; price dynamics; stochasticity; trade-off; uncertainty analysis; Taiwanjournal article10.1016/j.apenergy.2016.08.1122-s2.0-84984670516https://www2.scopus.com/inward/record.uri?eid=2-s2.0-84984670516&doi=10.1016%2fj.apenergy.2016.08.112&partnerID=40&md5=6d9dce1a6091ce3d8743d0d730bb1373