Risk-averse real-time dispatch of integrated electricity and heat system using a modified approximate dynamic programming approach

Coordinated operation of integrated electricity and heat system can improve operation flexibility and reduce cost. However, multiple uncertainties challenge its optimal operation. This paper aims at developing a risk-averse and computationally efficient policy for real-time stochastic dispatch of integrated electricity and heat system, which improves the economy as well as avoiding the risk of high costs in critical scenarios. First, real-time dispatch of integrated electricity and heat system is formulated as a multistage risk-averse stochastic sequential optimization problem with dynamic risk measure, where combined heat and power unit, energy storage, flexible electricity and heat load are jointly utilized to minimize the risk-adjusted total costs. Next, a risk-averse dynamic programming formulation of the original problem is presented, upon which a data-driven risk-averse approximate dynamic programming is employed to address computational challenge, and develop almost optimal and computationally efficient policy. By exploiting information from training samples in off-line learning, the proposed algorithm can efficiently responses to the stochastic exogenous information. Comparative simulations with different risk-aversion preferences and different methods verify the effectiveness of the proposed algorithm.