The field of energy market research is moving towards the development of innovative market platforms and optimization methods to address the complexities of distributed energy resources and consumer energy resource aggregators. Researchers are exploring the use of machine learning techniques, stochastic optimization, and risk-aware approaches to improve the efficiency and profitability of energy markets. A key focus area is the development of intuitive and transparent market structures that can handle complex preferences and uncertainties. Notable papers in this area include:

• A paper that introduces a multi-product market with an iterative mechanism for prosumers to express complex preferences, which demonstrates convergence to clearing prices in approximately 15 clock iterations.
• A paper that presents efficient two-stage stochastic optimization methods for aggregators of consumer energy resources, which can mitigate uncertainty and enhance profitability.
• A paper that proposes a conformal risk-aware approach for energy storage arbitrage, which can control downside risks arising from imperfect price forecasts.