Zulfiqar Ali Memon received the PhD degree in Automation and Robotics from Brunel University London, United Kingdom, in May 1991. He was a Postdoctoral Research Associate between December 1994 and October 1996 at Brunel University of London, United Kingdom. Dr. Zulfiqar Ali Memon holds BEng in Electrical Engineering with Distinction. Dr. Zulfiqar Ali has 28 years of research and teaching experience in higher education, his main area of teaching is Electrical Power System and Electrical Machines, currently he is active in Grid tied PV system renewable energy research area.
In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users’ unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables.
A multi-objective energy management and scheduling strategy for a microgrid comprising wind turbines, solar cells, fuel cells, microturbines, batteries, and loads is proposed in this work. The plan uses a fuzzy decision-making technique to reduce pollution emissions, battery storage aging costs, and operating expenses. To be more precise, we applied an improved honey badger algorithm (IHBA) to find the best choice variables, such as the size of energy resources and storage, by combining fuzzy decision-making with the Pareto solution set and a chaotic sequence. We used the IHBA to perform single- and multi-objective optimization simulations for the microgrid’s energy management, and we compared the results with those of the conventional HBA and particle swarm optimization (PSO). The results showed that the multi-objective method improved both goals by resulting in a compromise between them. On the other hand, the single-objective strategy makes one goal stronger and the other weaker. Apart from that, the IHBA performed better than the conventional HBA and PSO, which also lowers the cost. The suggested approach beat the alternative tactics in terms of savings and effectively reached the ideal solution based on the Pareto set by utilizing fuzzy decision-making and the IHBA. Furthermore, compared with the scenario without this cost, the results indicated that integrating battery aging costs resulted in an increase of 7.44% in operational expenses and 3.57% in pollution emissions costs
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In this paper we use the reinforcement learning techniques for optimal control of linear-quadratic systems, and consider two situations: when controllers cooperate (Pareto strategies) and when they do not cooperate (Nash strategies). These situations are encountered in optimal control and reinforcement learning of several engineering systems (especially in energy systems), economics, and in general machine learning. We compare the corresponding optimal costs using normalized data assuming that the system initial conditions are uniformly distributed on the unit sphere, and provide an estimate how much cooperation helps in learning optimal controllers for these kind of problems. The theoretical results are demonstrated on an example of an electric power system. © 2023 IEEE.
Sustainable induction of Electric vehicles (EV) adoption in developing countries requires a comprehensive policy and planning. Among several challenges, one of the biggest challenges is a sustainable charging station infrastructure integration to the existing weak distribution power network. Based on technological advancements, EV owners enjoy better milage in one charge. This has given a liberty to charge EVs as per the desire of user. There are two kinds of charging preferences, one is home charging and the other is fast charging. Both charging choices have distinct economic and technical aspects that need to be quantified in line with the available power infrastructure so as to develop a policy regulation. This study aims to quantitatively assess home charging, level 3 fast charging and their techno-economic aspects using social demographics of car owners. To analyze the demographics of local vehicle mobility practices in Pakistan, a comprehensive survey comprising online questionnaire was conducted. >100 car owners were surveyed about their daily driving practices to understand the driving distances and their preference of charging vehicles. The driving owners from distinct occupations ranging from cab drivers, job holders and businessmen participated in the survey. The survey facilitated to identify the demographics of the car owners which helped in conducting simulations and quantification analysis. Monte Carlo simulation was used for 1000 cars samples to estimate the level 1 and level 3 charging profiles in Python. In second step, HOMER Grid software was used to economically assess the impact of home charging and fast charging. It was revealed that charging choices have significant impact on the daily load profile of EVs. While having home charging, 59.9 % higher units are purchased from the grid. Whereas 65.5 NPC is increased for the project. On the other hand, the daily profile of preferred home charging is 61.4 % higher compared to the fast charging due to much concentrated charging impact during the limited time of the day. Results highlighted that Solar PV plays an important role for fast and home charging. Without solar PV charging stations will consume 60 % more grid units and 58 % higher Net present cost. The study concludes with a set of policy recommendations that must be taken into account to support the weak grid and lessen the EV charging impact on main utility.
Due to the importance of the allocation of energy microgrids in the power distribution networks, the effect of the uncertainties of their power generation sources and the inherent uncertainty of the network load on the problem of their optimization and the effect on the network performance should be evaluated. The optimal design and allocation of a hybrid microgrid system consisting of photovoltaic resources, battery storage, and a backup diesel generator are discussed in this paper. The objective of the problem is minimizing the costs of power losses, energy resources generation, diesel generation as backup resource, battery energy storage as well as load shedding with optimal determination of the components energy microgrid system include its installation location in the 33-bus distribution network and size of the PVs, batteries, and Diesel generators. Additionally, the effect of uncertainties in photovoltaic radiation and network demand are evaluated on the energy microgrid design and allocation. A Monte Carlo simulation is used to explore the full range of possibilities and determine the optimal decision based on the variability of the inputs. For an accurate assessment of the system’s reliability, a forced outage rate (FOR) analysis is performed to calculate potential photovoltaic losses that could affect the operational probability of the system. The cloud leopard optimization (CLO) algorithm is proposed to optimize this optimization problem. The effectiveness of the proposed algorithm in terms of accuracy and convergence speed is verified compared to other state-of-the-art optimization methods. To further improve the performance of the proposed algorithm, the reliability and uncertainties of photovoltaic resource production and load demand are investigated.
Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases.
In this paper, three distinct distributed energy resources (DERs) modules have been built based on demand side management (DSM), and their use in power management of dwelling in future smart cities has been investigated. The investigated modules for DERs system are: incorporation of load shedding, reduction of grid penetration with renewable energy systems (RES), and implementation of home energy management systems (HEMS). The suggested approaches offer new potential for improving demand side efficiency and helping to minimize energy demand during peak hours. The main aim of this work was to investigate and explore how a specific DSM strategy for DER may assist in reducing energy usage while increasing efficiency by utilizing new developing technology. The Electrical Power System Analysis (ETAP) software was used to model and assess the integration of distributed generation, such as RES, in order to use local power storage. An energy management system has been used to evaluate a PV system with an individual household load, which proved beneficial when evaluating its potential to generate about 20–25% of the total domestic load. In this study, we have investigated how smart home appliances’ energy consumption may be minimized and explained why a management system is required to optimally utilize a PV system. Furthermore, the effect of integration of wind turbines to power networks to reduce the load on the main power grid has also been studied. The study revealed that smart grids improve energy efficiency, security, and management whilst creating environmental awareness for consumers with regards to power usage.