Multi-objective optimal scheduling considering low-carbon operation of air conditioner load with dynamic carbon emission factors

Abstract

As global temperatures rise and climate change becomes more severely. People realize that air conditioning systems as a controllable resource and play an increasingly important role in reducing carbon emissions. In the past, the operation optimization of air conditioning systems was mainly oriented to user comfort and electricity costs ignoring the long-term impact on the environment. This article aims to establish a multi-objective model of air-conditioning load to ensure user temperature comfort performance and reduce the total cost (i.e., electricity cost and carbon emission cost) simultaneously. Multi Sand Cat Swarm Optimization (MSCSO) algorithm combined with gray target decision-making (GTD) is used to explore optimal solution. Meanwhile four competitive strategies are applied to validate the effectiveness of the proposed method, i.e., genetic algorithm (GA), MSCSO-comfort objective, MSCSO-total electricity cost objective and unoptimization. The simulation results show that the MSCSO-GTD based objective method can significantly reduce total costs while taking into account appropriate indoor temperature comfort.

Two-Layer Optimization Strategy of Electric Vehicle and Air Conditioning Load Considering the Benefit of Peak-to-Valley Smoothing

“…where P t sell is the amount of electricity sold after the demand response, and c t sell is the tariff of electricity sold by the distribution network operator to the customer: R t grid = P t bgrid c t bgrid − P t sgrid c t sgrid (14) where P t bgrid is the amount of electricity purchased from the grid, P t sgrid is the amount of electricity sold to the grid, c t bgrid is the price of electricity purchased from the grid, and c t sgrid is the price of electricity sold to the grid.…”

“…This allows our approach to deal with the uncertain availability of electric vehicles. Reference [14] introduces a dynamic carbon emission factor to integrate the total cost of the system (including electricity and carbon emission costs) and user comfort as multiple objectives to optimize air conditioning operation with a single objective. Reference [15] establishes a polymeric model of air conditioning to increase the potential of users to participate in demand response by changing the temperature rise compensation factor.…”