No abstract

The availability of sustainable, efficient electricity access is critical for rural communities as it can facilitate economic development and improve the quality of life for residents. Isolated microgrids can provide a solution for rural electrification, as they can generate electricity from local renewable energy sources and can operate independently from the central grid. Residential load scheduling is also an important aspect of energy management in isolated microgrids. However, effective management of the microgrid’s energy resources and load scheduling is essential for ensuring the reliability and cost-effectiveness of the system. To cope with the stochastic nature of RERs, the idea of an optimal energy management system (EMS) with a local energy transactive market (LETM) in an isolated multi-microgrid system is proposed in this work. Nature-inspired algorithms such as JAYA (Sanskrit word meaning victory) and teaching–learning based optimization algorithm (TLBO) can get stuck in local optima, thus reducing the effectiveness of EMS. For this purpose, a modified hybrid version of the JAYA and TLBO algorithm, namely, the modified JAYA learning-based optimization (MJLBO), is proposed in this work. The prosumers can sell their surplus power or buy power to meet their load demand from LETM enabling a higher load serving as compared to a single isolated microgrid with multi-objectives, resulting in a reduced electricity bill, increased revenue, peak-average ratio, and user discomfort. The proposed system is evaluated against three other algorithms TLBO, JAYA, and JAYA learning-based optimization (JLBO). The result of this work shows that MJLBO outperforms other algorithms in achieving the best numerical value for all objectives. The simulation results validate that MJLBO achieves a peak-to-average ratio (PAR) reduction of 65.38% while there is a PAR reduction of 51.4%, 52.53%, and 51.2% for TLBO, JLBO, and JAYA as compared to the unscheduled load.

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

The new millennium has witnessed a pervasive shift of trend from AC to DC in residential sector. The shift of trend is predominantly due to independent residential solar PV systems at rooftops and escalating electronic loads with better energy saving potential integrated with diminishing prices as well as commercial availability of DC based appliances. DC has ousted AC in generation, transmission, and utilization sectors with the advent of DC based generating systems (e-g solar PV), high voltage DC (HVDC) transmission and the utilization of DC based loads respectively. However, the war of currents (AC vs DC) is still ON as regards to distribution sector. Efficiency is the parameter that once wiped DC out of the power systems scenario as compared to AC-at the time of Tesla and Edison. Therefore, the same parameter is utilized to determine which is better for distribution sector under current conditions; AC or DC? A comprehensive sensitivity analysis considering real load profile is missing in the present body of knowledge. In order to fill that gap, this paper is an attempt to include comprehensive sensitivity analysis of DC distribution system and its simulation-based comparison with AC counterpart considering real load profile. The paper uses Monte Carlo technique and probabilistic approach to add diversity in residential loads consumption and in turn to obtain instantaneous load profile. The paper also presents a futuristic perspective of power electronic converter (PEC) efficiency variation on the efficiency comparison of both AC and DC distribution systems. Since the present body of knowledge generally compares AC and DC distribution based upon assumptions and limited scenarios which results in conflicting outcomes; in contrast, the discoveries of the current examination are useful to reduce the confusions and conflictions regarding which is better at distribution scale; AC or DC?

The new millennium has witnessed a pervasive shift of trend from AC to DC in the residential load sector. The shift is predominantly due to independent residential solar PV systems at rooftops and escalating electronic loads with better energy saving potential integrated with diminishing prices as well as commercial availability of DC-based appliances. Comprehensive sensitivity analysis considering the real load profile is missing in the present body of knowledge. In order to fill that gap, this paper is an attempt to include a comprehensive sensitivity analysis of the DC distribution system and its simulation-based comparison with its AC counterpart, considering the real load profile. The paper uses the Monte Carlo technique and probabilistic approach to add diversity in residential loads consumption to obtain an instantaneous load profile. Various possible scenarios such as variation of standard deviation from 5% to 20% of mean load value, PV capacity variation from 1000 W to 9000 W, and variation in power electronic converter (PEC) efficiencies are incorporated to make the system realistic as much as possible maintaining a fair comparison between both systems. The paper concludes with the baseline efficiency advantage of 2% to 3% during the day for the case of the DC distribution system as compared to the AC distribution system.