Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions

Abstract Advancedd distribution management systems (ADMSs) integrate distributed generation (DG) units and battery energy storage systems (BESSs), and they are a promising solution to reduce the greenhouse gas emissions in the energy sector. This study addresses the sustainable ADMS (SADMS) design problem by determining the power flow optimal economic dispatches from traditional power plants, DG units, and BESSs to residential areas while maximizing the total profit. The designed SADMS provides a demand response programs with various energy pricing schemes that correspond to different customers and energy consumption loads. Three carbon emission policies (carbon tax, carbon cap, and carbon trade) are considered in the model. An advancedd two-phase (ATP) approach is proposed to solve the described problem. In the first phase, an artificial neuro-fuzzy system (ANFS) is developed based on self-learning and self-adjusting processes to determine the customer demand response loads and DG unit output energies in uncertain environments. A combined ANFS and optimization solver is proposed in the second phase to determine the optimal SADMS economic dispatch. The application of the proposed approach is examined using an empirical case study in Taiwan. The results demonstrate that the proposed ATP approach can determine the optimal economic dispatch with an extremely small deviation in demand response load of 0.92%. In addition, our approach increases the total profit (improving total profit by approximately 1.1%, 0.8%, and 1.9% for the three different carbon emission policy objective functions) and reduces the computational time (by 3.0–6.0 times) compared to those of the genetic algorithm. Finally, the proposed model illustrates that carbon trade is the best policy for improving the total SADMS profit while satisfying the given environmental constraints.