The rising cost and demand for energy have prompted the need to devise innovative methods for energy monitoring, control, and conservation. In addition, statistics show that 20% of energy losses are due to the mismanagement of energy. Therefore, the utilization of energy management can make a substantial contribution to reducing the unnecessary usage of energy consumption. In line with that, the intelligent control and optimization of energy management systems integrated with renewable energy resources and energy storage systems are required to increase building energy efficiency while considering the reduction in the cost of energy bills, dependability of the grid, and mitigating carbon emissions. Even though a variety of optimization and control tactics are being utilized to reduce energy consumption in buildings nowadays, several issues remain unsolved. Therefore, this paper presents a critical review of energy management in commercial buildings and a comparative discussion to improve building energy efficiency using both active and passive solutions, which could lead to net-zero energy buildings. This work also explores different optimum energy management controller objectives and constraints concerning user comfort, energy policy, data privacy, and security. In addition, the review depicts prospective future trends and issues for developing an effective building energy management system, which may play an unavoidable part in fulfilling the United Nations Sustainable Development Goals.

In the literature, there are many different photovoltaic (PV) component sizing methodologies, including the PV/inverter power sizing ratio, recommendations, and third-party field tests. This study presents the state-of-the-art for gathering pertinent global data on the size ratio and provides a novel inverter sizing method. The size ratio has been noted in the literature as playing a significant role in both reducing power clipping and achieving system optimization. The majority of researchers observed that due to varying irradiance distributions and operating temperatures at particular sites, the sizing ratios were dependent on geographic latitude. This study will identify the issue that makes it challenging to acquire dependable and optimum performance for the use of grid-connected PV systems by summarizing the power sizing ratio, related derating factor, and sizing formulae approach. The present study recommends a Deep Learning technique that might, due to the dynamic behavior of the PV technologies, provide fully automatic computation for the DC/AC sizing ratio, and effectively lower the whole return on investment (ROI) over a variety of circumstances and climatic changes.

The impact of high PV penetration into the grid particularly at the distribution side has been extensively studied. However, most of the available research focuses on North American style systems. This project aims to investigate the effect of high PV penetration at a residential area in a European-based distribution network, which is electricity supply system Malaysia is based on. The modeling is done using OpenDSS while the network model used is the IEEE European Low Voltage Test Feeder which consists of 55 loads representing a generic housing area. Each load point is then equipped with a 4 kW PV system-representing a typical size for a house installation. PV output variability is then introduced into the modeling using two sample days of actual irradiance variability obtained from UTeM Malaysia; one for clear day and another for a high variability day. Voltage unbalance, voltage rise and reverse power flow were analyzed. One significant finding of this project is that voltage rise exceeds the standard of 1.05 pu during noon. Besides that, the high variability days significantly affect the mitigation measures required to manage reverse power flow.