Apoorv Mathur

Apoorv is currently working as the Strategy Advisor for Tata Power Trading in India. At Tata Power Trading, he looks after several areas including engaging in Power Market Development, Energy Policy, and Regulatory advocacy efforts, leading efforts in securing Global Energy Resources, Energy Markets & Trading (Power/Coal/LNG/Carbon), as well as efforts in Power Market Analytics and Risk Management for the company. On the Market Development, Policy and Regulatory efforts, he has been closely involved in the FICCI Power Committee, the XIIth Working Plan for Power by CEA as well as in some of the efforts by CERC on the Market Development, Market Transparency, Regulatory and Tariff Issues. On the Analytics front, he has so far explored various models for forecasting of demand and power prices in India. He has been a speaker at various international conferences on matters related to Energy Risk in Indian Power Sector.

He has also worked in Strategy at Power Exchange of India briefly after he moved back to India just over two years ago from the US. He has broad exposure in Trading of Power and Derivatives based on Power having traded in both PJM and MISO Power Markets in the US where he worked in Energy Trading (Power and Natural Gas Derivatives) at Lehman Brothers, New York, and PSEG, a Power Company in the New York area. He also has exposure to Derivatives Risk Management at Deutsche Bank and Analytics at SAS Institute. One of the drivers of his move back to India was to help shape the Indian Power Market in the coming years towards a vibrant, competitive market resulting in improvement in the quality of supply and efficiency and economy in the market.

Apoorv has a Degree in Engineering from IIT Delhi and has a Masters Degree in Operations Research from North Carolina State University, USA. While one of his passions was pricing and risk management of Derivatives on various commodities, he got interested in Power Trading in the US after realizing the tremendous role of optimization in helping the system achieve the lowest cost while balancing the system constraints.