The increasing popularity and number of electric vehicles (EVs) globally have resulted in a growing demand for efficient, reliable, and effective electric vehicle charging station (EVCS) infrastructure. However, the development and implementation of this infrastructure involves various challenges, including the variety of EVs, charging and battery technologies, high installation costs, limited grid capacity, and uncertainties regarding future demand, and raises issues regarding the quality, security, and stability of the power system. This paper presents a holistic understanding of the challenges, mitigation approaches, and available technologies and protocols related to EVCS network deployment. Moreover, optimization strategies such as location planning, network integration, scheduling charging time, and planning price, which involve maximizing the utilization of charging stations and minimizing associated costs, and their modeling techniques are highlighted. This review aims to provide insights to develop sustainable, efficient EVCS infrastructure while overcoming the challenges and optimizing the benefits.

The optimization of handover (HO) parameters for in-building systems is investigated in this paper. We proposed a novel methodology that provides in-building base stations with the flexibility to customize HO parameters to specific radio frequency conditions at the cell-edge for different loading scenarios. We propose the use of machine learning and data mining techniques to allow the base stations to autonomously learn and identify characteristic patterns in the received signal strength values (reported by users during the HO process), and apply optimal HO parameters for each case. Our optimization strategy jointly considers the radio frequency conditions at the cell-edge and the load levels of the base stations, to determine optimal HO parameters that maximize the quality of service and guarantee the continuity of service at the cell-edge. We evaluated our methodology with experimental data collected from two fully operational LTE in-building systems deployed in a university campus. Our results show that with our methodology the spectral efficiency at the cell-edge can be greatly improved. Downlink data rate gains at the cell-edge reached a value close to 150% for a certain loading scenario compared to the traditional approach of selecting a unique set of HO parameters for the entire in-building system.

In an era characterized by severe climate change, dwindling resources, and a growing world population, the agricultural industry is facing unprecedented challenges. On the other hand, overuse of natural resources has emerged as a major concern worldwide. Greenhouses (GHs) have been developed as central environments capable of growing a diverse range of high-quality agricultural products throughout the year, regardless of external weather conditions. However, conventional GHs often impose significant costs on energy resources for their heating and cooling operations, thus presenting sustainability challenges. To address these pressing concerns, using new smart technologies as well as the integration and development of renewable energy sources, including photovoltaics (PVs), wind turbines (WT), and geothermal systems, have gained momentum. This integration not only increases the ecological footprint of GHs but also reduces their dependence on conventional energy sources. Furthermore, the adoption of smart GH technologies, characterized by advanced control and automation systems, holds significant promise in energy optimization and efficiency. Hence, this systematic review attempts to carefully examine the optimal and practical methods that include the design, fabrication, control, energy management, and operation of smart GHs. This review includes an in-depth analysis of GH structures, building materials, cooling and heating systems, new dark GH concepts, and smart lighting systems. In addition, it addresses effective strategies to curb energy consumption in smart GHs. By synthesizing and synthesizing existing research and practical experiences, this paper seeks to provide valuable insights and recommendations to facilitate the efficient and sustainable design, construction, and operation of smart GHs. Ultimately, this work aims to promote resource-efficient and environmentally conscious practices in the agricultural sector.

As compensation to power generation dispatch, demand response (DR) enables demand controllability by changing the consumers' electricity usage patterns, which can be used to reduce electricity cost, integrate renewable energy, and provide ancillary services. To reveal the benefits from residential DR, this study develops two approaches: 1) optimal load aggregation under augmented time-of-use (TOU) pricing; and 2) active DR participation in unit commitment (UC) under rewards. We have shown that plain TOU pricing is not a promising DR policy if residential customers are equipped with home energy management systems (EMS). We, therefore, propose an augmented TOU by radial basis functions (RBF). With a 60% participation level, the proposed optimal load aggregation model under the augmented TOU can reduce the power generation cost by 24% and decrease the standard deviation of the load profile by 42%. However, these results can be affected by the customer's participation level, which is also quantitatively studied. Specifically, when the participation level exceeds 80% this method becomes less efficient. The second proposed approach, a two-stage stochastic UC model with DR flexibility, reduces the power generation cost by 20% and decreases the standard deviation of the load profile by 77%. In addition, the inconvenience of DR participation is quantitatively evaluated, and a Pareto surface is developed, which can be used as a baseline for residential customers to set up the home EMS for DR implementation. Both the proposed mechanisms can be used to improve energy efficiency by uncovering the residential DR potential.

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.