• Energy Research

In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1–3 while Scenarios 4–6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is $\$ $ 1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to $\$ $ 0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to $\$ $ -0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be $\$ $ 1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to $\$ $ 1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to $\$ $ 0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems.

Due to their nonlinear behavior and the harsh environments to which batteries are subjected, they require a robust battery monitoring system (BMS) that accurately estimates their state of charge (SOC) and state of health (SOH) to ensure each battery’s safe operation. In this study, the interacting multiple model (IMM) algorithm is implemented in conjunction with an estimation strategy to accurately estimate the SOH and SOC of batteries under cycling conditions. The IMM allows for an adaptive mechanism to account for the decaying battery capacity while the battery is in use. The proposed strategy utilizes the sliding innovation filter (SIF) to estimate the SOC while the IMM serves as a process to update the parameter values of the battery model as the battery ages. The performance of the proposed strategy was tested using the well-known B005 battery dataset available at NASA’s Prognostic Data Repository. This strategy partitions the experimental dataset to build a database of different SOH models of the battery, allowing the IMM to select the most accurate representation of the battery’s current conditions while in operation, thus determining the current SOH of the battery. Future work in the area of battery retirement is also considered.

With increasing energy demands, fuel cells are a popular avenue for portability and low waste emissions. Hydrogen fuel cells are popular due to their potential output power and clean waste. However, due to storage and transport concerns, hydrogen peroxide fuel cells are a promising alternative. Although they have a lower output potential compared to hydrogen fuel cells, peroxide can act as both the oxidizing and reducing agent, simplifying the structure of the cell. In addition to reducing the complexity, hydrogen peroxide is stable in liquid form and can be stored in less demanding methods. This paper investigates chelated metals as electrode material for hydrogen peroxide fuel cells. Chelated metal complexes are ring-like structures that form from binding organic or inorganic compounds with metal ions. They are used in medical imaging, water treatment, and as catalysts for reactions. Copper(II) phthalocyanine, phthalocyanine green, poly(copper phthalocyanine), bis(ethylenediamine)copper(II) hydroxide, iron(III) ferrocyanine, graphene oxide decorated with Fe3O4, zinc phthalocyanine, magnesium phthalocyanine, manganese(II) phthalocyanine, cobalt(II) phthalocyanine are investigated as electrode materials for peroxide fuel cells. In this study, the performance of these materials is evaluated using cyclic voltammetry. The voltammograms are compared, as well as observations are made during the materials’ use to measure their effectiveness as electrode material. There has been limited research comparing the use of these chelated metals in the context of hydrogen peroxide fuel cells. Through this research, the goal is to further the viability of hydrogen peroxide fuel cells. Poly(copper phthalocyanine) and graphene oxide doped with iron oxides had strong redox catalytic activity for use in acidic peroxide single-compartment fuel cells, where the poly(copper phthalocyanine) electrode compound generated the highest peak power density of 7.92 mW/cm2 and cell output potential of 0.634 V.

With the increased energy demands of the 21st century, there is a clear need for developing a more sustainable method of energy generation, distribution, and transmission. The popularity of Smart Grid continues to grow as it presents its benefits, including interconnectivity, improved efficiency, the ability to integrate renewable energy sources, and many more. However, it is not without its challenges. This survey aims to provide an introductory background of smart grids, detail some of the main aspects and current challenges, and review the most recent papers and proposed solutions. It will also highlight the current state of implementation of the smart grid by describing various prototypes, as well as various countries and continents implementation plans and projects. Comment: Paper has been submitted for review to the journal Energy Reports (January 23, 2024). 58 pages, 7 figures, 7 tables

To ensure reliable operation of electrical systems, batteries require robust battery monitoring systems (BMSs). A BMS’s main task is to accurately estimate a battery’s available power, referred to as the state of charge (SOC). Unfortunately, the SOC cannot be measured directly due to its structure, and so must be estimated using indirect measurements. In addition, the methods used to estimate SOC are highly dependent on the battery’s available capacity, known as the state of health (SOH), which degrades as the battery is used, resulting in a complex problem. In this paper, a novel adaptive battery health estimation method is proposed. The proposed method uses a dual-filter architecture in conjunction with the interacting multiple model (IMM) algorithm. The dual filter strategy allows for the model’s parameters to be updated while the IMM allows access to different degradation rates. The well-known Kalman filter (KF) and relatively new sliding innovation filter (SIF) are implemented to estimate the battery’s SOC. The resulting methods are referred to as the dual-KF-IMM and dual-SIF-IMM, respectively. As demonstrated in this paper, both algorithms show accurate estimation of the SOC and SOH of a lithium-ion battery under different cycling conditions. The results of the proposed strategies will be of interest for the safe and reliable operation of electrical systems, with particular focus on electric vehicles.

The teaching-learning-based optimizer (TLBO) algorithm is a powerful and efficient optimization algorithm. However it is prone to getting stuck in local optima. In order to improve the global optimization performance of TLBO, this study proposes a modified version of TLBO, called teaching-learning-studying-based optimizer (TLSBO). The proposed enhancement is based on adding a new strategy to TLBO, named studying strategy, in which each member uses the information from another randomly selected individual for improving its position. TLSBO is then used for solving different standard real-parameter benchmark functions and also various types of nonlinear optimal power flow (OPF) problems, whose results prove that TLSBO has faster convergence, higher quality for final optimal solution, and more power for escaping from convergence to local optima compared to original TLBO.

This study focuses on the application of Coulomb’s and Franklin’s laws algorithm (CFA) to solving large-scale optimal reactive power dispatch (LS-ORPD) problems. The CFA optimizer acts on the basis of the charged particles interactions. The ever-increasing effects of ORPD problems for safe and reliable operation of electrical power grids is an important area of study. Such problems are classified as nonlinear optimization problems; the aim of which is to minimize the active power loss through tuning of several control variables. Firstly, the performance of CFA optimizer in solving high-dimensional problems is investigated using standard benchmark problems. Moreover, we apply the CFA optimizer for solving large-scale ORPD problems based on different constraints in three IEEE standard power systems. According to the results, the proposed optimizer offers a more accurate solution when compared with other methods found in the literature. Finally, an early attempt is carried out for improving CFA optimizer, which is tested on benchmark and ORPD problems and yields promising outcome in reaching a more powerful variant of CFA.