This paper considers options for a future Indian power economy in which renewables, wind and solar, could meet 80% of anticipated 2040 power demand supplanting the country’s current reliance on coal. Using a cost optimization model, here we show that renewables could provide a source of power cheaper or at least competitive with what could be supplied using fossil-based alternatives. The ancillary advantage would be a significant reduction in India’s future power sector related emissions of CO₂. Using a model in which prices for wind turbines and solar PV systems are assumed to continue their current decreasing trend, we conclude that an investment in renewables at a level consistent with meeting 80% of projected 2040 power demand could result in a reduction of 85% in emissions of CO₂ relative to what might be expected if the power sector were to continue its current coal dominated trajectory.

A data-based Stackelberg market strategy for a distribution market operator (DMO) is proposed to coordinate power dispatch among different virtual power plants (VPP), i.e., demand response aggregators (DRA). The proposed strategy has a two-stage framework. In the first stage, a data-driven method based on noisy inverse optimization estimates the complicated price-response characteristics of customer loads. The estimated load information of the DRAs is delivered to the second stage where a one-leader multiple-follower stochastic Stackelberg game is formulated to represent the practical market interaction between the DMO and the DRAs that considers the uncertainty of renewables and the operational security. The proposed data-driven game model is solved by a new penalty algorithm and a customized distributed hybrid dual decomposition-gradient descent (HDDGD) algorithm. Case studies on a practical demand response project in China and a distribution test system demonstrate the effectiveness of the proposed methodology.

The increasing integration of distributed renewable energy resources highlights the need to design new control strategies for hybrid wind turbine-photovoltaic (WT-PV)-battery microgrid (MG) clusters. This paper proposes a two-level optimization model for the coordinated energy management between distribution systems and clustered WT-PV-battery MGs. The upper level of the model deals with the operation of the distribution network, while the lower level considers the coordinated operation of multiple MGs. An interactive game matrix (IGM) is applied to coordinate the power exchange among multiple MGs, and between the distribution network and MGs. The model is solved by a modified hierarchical genetic algorithm. Case studies on a distribution system with MGs, as well as a practical multi-MG system, demonstrate the effectiveness of the proposed method in improving power quality, reliability, and environmental benefits.