Due to the surge in load demand, the scarcity of fossil fuels, and increased concerns about global climate change, researchers have found distributed energy resources (DERs) to be alternatives to large conventional power generation. However, a drastic increase in the installation of distributed generation (DGs) increases the variability, volatility, and poor power quality issues in the microgrid (MG). To avoid prolonged outages in the distribution system, the implementation of energy management strategies (EMS) is necessary within the MG environment. The loads are allowed to participate in the energy management (EM) so as to reduce or shift their demands to non-peak hours such that the maximum peak in the system gets reduced. Therefore, this article addresses the complication of solutions, merits, and demerits that may be encountered in today’s power system and encompassed with demand response (DR) and its impacts in reducing the installation cost, the capital cost of DGs, and total electricity tariff. Moreover, the paper focuses on various communication technologies, load clustering techniques, and sizing methodologies presented.

Global warming, environmental degradation, clean energy production, intermittent, volatile, and unpredictable renewable energy sources (RES’s), occasional peak demand on the system necessitates energy management (EM). Demand response (DR) programs in the distribution network can be seen as one of the foundation stones in the future of EM. This article illustrates the need for EM using DR, its benefits, types of loads, clustering techniques, price-based demand response (PBDR) etc. To accomplish the EM goals and to attain the economic benefit, DR employs peak shifting, peak clipping, valley filling and load growth. However, the accumulation of large loads at low electricity prices creates local peaks, this phenomenon is referred to as payback or rebound effect (RE). The occurrence of RE at low price zone heightens the volatility of market clearing price (MCP) and the operational cost of the microgrid. Inherently, the scheduled inelastic consumers at low price zone suffer from increased MCP and therefore, the total consumer tariff (TCT). The occurrence of RE depends on the load curve, peak to average ratio, electricity price and the percentage of interruptible loads present in the system. Unclear pricing methods impede the participation of customers in DR events. Moreover, majority of techniques presented in literature are of centralized frameworks that needs complex communication technologies. To fill these glitches the proposed work uses a simple distributed scheduling approach based on alternating direction method of multipliers (ADMM) to alleviate the energy management using an IEEE-18 bus system. The load factor increases from 0.79 to 0.83. Using DR lowers the peak power demand on the MG from 82 to 78 kW without compromising customer comfort or satisfaction. The TCT was lowered from scenario 1 to scenario 4 from 3058 to 2254 euros. The system's average demand dropped from 65.54 kW to 64.8 kW. IEEE-33 bus system was considered to assess the impact of RE on the MCP and TCT. Additionally, the marginal generator provides 72.51 kW of electricity in sub case 3 and 166.26 kW of power in sub case 2. Due to a decrease in power dispatch from the marginal generator, TCT increased from sub case 2 to sun case 3 by 11,046.41 rupees to 12,912.75 rupees. In contrast, TOC decreased from 6495.45 rupees to 6150.75 rupees from sub case 2 to sun case 3.

Power generation using distributed generation become the potential solution to augment the conventional grid issues and mitigate global environmental impact. However, the problems associated with distributed generation are sporadic in nature, stochastic, and pose a threat to the effective functioning of the power system. Ungoverned supply demand balance in autonomous microgrid (MG) compels to incorporate battery energy storage system (BESS). Moreover, the installation of BESS enhances the reserve capacity and frequency stability of the MG as well. Non-optimal selection of size of BESS fetches meagre results. The proposed work highlights a new hybrid Lagrangian relaxation and improved class topper optimization which is a cost-based approach for the optimal sizing of BESS considering the operational, emission cost of MG and aims to reduce the total cost of the system. A hybrid method has been developed for robust scenario reduction by combining K-means and density based spatial clustering applications with noise (DBSCAN) for WT scenario reduction and DBSCAN and principal component analysis for PV and load demand scenario reduction. This hybrid approach offers advantages in handling clusters of varying shapes and densities, providing a more accurate representation of the underlying data structure. Extensive investigation has been carried out in order to get the optimal size of BESS by comparing various optimization algorithms such as particle swarm optimisation, grey wolf optimisation, modified grey wolf optimisation, whale optimisation algorithm and sine cosine algorithm with the proposed method. Three case studies are executed on sizing of BESS to analyse the reduction in emission cost and operational cost. From the results, it is found that the proposed algorithm is outperforming. From case 1 to case 2, the operational cost is reduced from 1620.4 to 1512 which is a reduction of 7.1%, case 1 to case 3 it is 1620.4 to 1422.84 a reduction of 13.8% and from case 2 to case 3 it is reduced from 1512 to 1422.84 which is a reduction of 6.2%.