An Information Theory-Based Locational Marginal Pricing Solution for Low-Carbon Power Systems

The transition of the power system into a low-carbon power system (LCPS) with a high penetration of renewable energy resources addresses several issues related to energy and climate. However, due to the uncertainty associated with renewable power generation (RPG), deriving an accurate effective locational marginal pricing (LMP) for an LCPS remains a challenge. To address this challenge, we propose a novel information theory-based framework for LMP calculation that quantifies the fluctuations in the LMP due to uncertainty associated with RPG and random loads in an LCPS. First, based on the information entropy levelized cost of energy, we introduce the equivalent cost of RPG to ensure that the cost of RPG is not zero under the LMP mechanism so that it can bid reasonably in the market to provide accurate price signals. We, then, design a security-constrained economic dispatch model incorporating the RPG equivalent cost to balance uncertainty and energy demand in the LCPS electricity market. Furthermore, we propose an uncertainty-constrained model of buses and branches in LCPS based on information theory that is developed to clarify the physical significance of the information that reduces generation and load uncertainty within the LMP framework.