Temporal and Spatial Load Management Methods for Cost and Emission Reduction

Abstract

To address the challenge of climate change, reducing emissions due to electric power generation and consumption has received increasing attention worldwide. The previous research efforts have been focused on emission reduction on the generation side, but the positive impacts through demand side load management are often ignored. This paper explores the models of load management to reduce cost and emissions. Five different load management methods are studied and compared using the IEEE 14-bus system and the IEEE 57-bus system. Two of them are centralized methods, i.e., temporal and/or spatial load management schemes that need to have global system information to optimize the overall load management in a whole control area. The remaining three are decentralized methods focusing on the customer side, including a basic self-optimizing method and two advanced self-optimizing methods [sliding window self-optimizing load management and day-ahead self-optimizing load management (DA-SOLM)]. The advanced SOLM schemes need multiple communications between an independent system operator and customers. The results show that the temporal and spatial load management can achieve an effective reduction in cost and emission and the DA-SOLM, especially for a large power system, is able to mitigate locational marginal price spikes.