Conventional fossil-fuel energy resources are being drastically depleted; thus, the current shift towards renewable energy (RE) resources has become imperative. However, there are many impediments to the adoption of renewable power generation. These impediments can be overcome by enacting policies to encourage the acceptance of sustainable energy resources. For instance, the net-metering policy can provide the necessary incentives to promote the development of local distributed energy sources, primarily solar photovoltaic and wind generators. While there has been significant advancement and development in net-metering in Asia with the increased penetration of RE, at present there is a lack of systematic review in this area. This paper aims to present an in-depth review on net-metering advances and challenges, current RE shares, and future RE targets in the Asian region. Additionally, a case study is performed and an economic analysis of net-metering regulations in an Asian country is carried out. In this study, the monetary benefits of net-metering policies for residential consumers are proved. It is envisaged that the information gathered in this paper will be a valuable one-stop source of information for Asian researchers working on this topic.

Around the world, countries are integrating photovoltaic generating systems to the grid to support climate change initiatives. However, solar power generation is highly uncertain due to variations in solar irradiance level during different hours of the day. Inaccurate modelling of this variability can lead to non-optimal dispatch of system resources. Therefore, accurate characterization of solar irradiance patterns is essential for effective management of renewable energy resources in an electrical power grid. In this paper, the Weibull distribution based probabilistic model is presented for characterization of solar irradiance patterns. Firstly, Weibull distribution is utilized to model inter-temporal variations associated with reference solar irradiance data through moving window averaging technique, and then the proposed model is used for irradiance pattern generation. To achieve continuity of discrete Weibull distribution parameters calculated at different steps of moving window, Generalized Regression Neural Network (GRNN) is employed. Goodness of Fit (GOF) techniques are used to calculate the error between mean and standard deviation of generated and reference patterns. The comparison of GOF results with the literature shows that the proposed model has improved performance. The presented model can be used for power system planning studies where the uncertainty of different resources such as generation, load, network, etc., needs to be considered for their better management.

Optimal reactive power dispatch (ORPD) has a crucial impact to enhance safety, reliability, and economical operation of the electric power system. ORPD is a non-linear, non-convex and mixed variable problem, which has been solved by many researchers via different meta-heuristic algorithms during the last decade. In this work, a novel algorithm named sine-cosine algorithm (SCA) is utilized to solve ORPD problem by considering both dependent and independent control variable constraints. SCA has been tested and validated on standard 14, 30 and 57-bus power systems. To validate the superiority of proposed algorithm, the outcomes obtained through SCA are compared with recent published results attained through particle swarm optimization (PSO), modified Gaussian barebones teaching–learning based optimization (BBTLBO), ant bee colony optimization (ABCO), whale optimization algorithm (WOA) and backtracking search algorithms (BSA). The results attained using SCA show the improvement in the power losses minimization. Thus, with standard 14-bus system, the power losses are minimized from 0.04% to 4.78%. While, using standard 30-bus, the power losses are minimized from 0.4% to 3.4% and with standard 57-bus, power losses are reduced from 0.9% to 1.99%. Furthermore, a comparative analysis with 30 independent runs on the above-mentioned bus systems is performed to examine the functioning of the proposed method in terms of probability density function (PDF) and cumulative density function (CDF). For such analysis, well-known meta-heuristic algorithms such as PSO, WOA, differential evolution (DE) are compared with proposed SCA in solving the ORPD problem. The results of this analysis clearly show that proposed algorithm is robust, effective, and computationally easy in solving the ORPD problem compared to the existing meta-heuristic algorithms.

The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.