Power Management through Aging-Based Task Scheduling Algorithms for Smart Grids

In smart grid several scheduling techniques have been proposed for load management in commercial, industrial and residential areas to minimize electricity cost, Peak to Average ratio (PAR) and provide user comfort maximization. Demand Side Management (DSM) is necessary for optimized results. Smart grid is a digital technology with two-way communication between the utility company and electricity consumers. Energy Management Controller (EMC) are used to maintain record of all appliances, operation time of appliances and cost which we have to pay for it. Smart grid motivates users to shift the load in Off Peak Hours (OPH) form Peak Hours (PH) through providing incentive in OPH. By this act consumers save money against load shifting from high price hours to low price hours. In this paper, Genetic Algorithm (GA) and Earthworm Optimization Algorithm (EWA) based schemes is proposed to minimize electricity cost and Peak to Average Ratio (PAR) while maximizing User Comfort (UC) via appliances scheduling.

Charging performance of Plug-in Hybrid Electric Vehicles (PHEVs) under various charging approaches brings new challenges for the distribution grid, such as feeder overloading and increment of loss. In this paper, the impact of charging PHEVs on the load profile is evaluated considering Demand Response (DR) programs in order to minimize the impact of charging PHEVs on load profile. In the proposed DR strategy, all consumers can determine and control their loads. Also, the proposed DR strategy can satisfy the function of reducing peak with regard to different PHEV penetration, while preserving consumer comfort levels. For performing DR programs, time of use and real time pricing models are used to evaluate the impacts of charging of PHEVs on distribution system operations. A 33-node test feeder has been studied to assess different penetration schemes of PHEVs including 11.3%, 35% and 45% PHEV penetration in residential loads. Moreover, uncertainties associated with PHEV charging performances are handled with Monte Carlo simulation. Results validate that the amount of peak loss, and Peak to Average Ratio (PAR) increase owing to PHEVs penetration growth. Also, the results illustrate that scheduling for the operation of PHEVs with DR program reduces the distribution losses and PAR.

In this paper we present an adaptive application-driven power management (AADPM) strategy with online idle period length distribution learning capability for the IEEE 802.11b WLAN. We discuss its design and evaluate the performance in comparison with other power management strategies using the network simulator NS2. We simulated both the single user and multiple users scenarios. Experimental results have shown that, compared with other power management methods examined in this paper, AADPM achieved the highest energy saving in all cases and demonstrated strong adaptability to network congestion

Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). In SG the main focus of the researchers is on home energy management (HEM) system, that is also called demand side management (DSM). DSM includes all responses, which adjust the consumer’s electricity consumption pattern, and make it match with the supply. If the main grid cannot provide the users with sufficient energy, then the smart scheduler (SS) integrates renewable energy source (RES) with the HEM system. This alters the peak formation as well as minimizes the cost. Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. HEM benefits the end users by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances or shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are being used to meet aforementioned objectives. In this paper, three different types of heuristic algorithms are evaluated on the basis of their performance against cost saving, user comfort and peak to average ratio (PAR) reduction. Two techniques are already existing heuristic techniques i.e. harmony search (HS) algorithm and enhanced differential evolution (EDE) algorithm. On the basis of aforementioned two algorithms a hybrid approach is developed i.e. harmony search differential evolution (HSDE). We have done our problem formulation through multiple knapsack problem (MKP), that the maximum consumption of electricity of consumer must be in the range which is bearable for utility and also for consumer in sense of electricity bill. Finally simulation of the proposed techniques will be conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of minimum cost, reduced peak to average ratio (PAR), waiting time and equally distributed energy consumption pattern in each hour of a day to benefit both utility and end users.

In this paper, an advanced power management and control strategy is proposed to manage the operation of a short-route all electric battery powered ferry. The objective of this strategy is to maintain reliable power supply for both propulsion and ferry service loads under all operations and achieve suitable exploitation of the batteries-based energy storage systems (BESSs). For this aim, and in order to achieve these objectives a hierarchical control strategy. This strategy includes an inner voltage-current control level to maintain the desired DC voltage at the onboard DC bus and a V/P droop-based power sharing control level to accurately share the power between BESSs. Additionally, an external power management level is implemented in order to define the power participation amount of each BESSs according to the ferry load power demand and to their state of charge (SOC). This power management strategy handles also the SOC balancing of the onboard BESSs using optimized participation factors. The optimized SOCs balancing aims at improving the performance and life cycles of the BESSs and avoiding excessive BESS discharge and disconnection. The validity of the proposed control and management strategy is verified using Hardware-in-the-Loop Simulation.

In this paper, performance of energy management controller (EMC) based on meta-heuristic algorithms: Harmony Search Algorithm (HSA) and Firefly Algorithm (FA) are evaluated. Critical peak pricing (CPP) scheme is implemented to calculate the electricity cost. Appliances are categorized into three groups on the basis of power consumption. Electricity cost minimization and electricity load shifting from peak hours towards off peak hours are the main objectives of the paper. In simulation results, adopted approach reduces the Peak to Average Ratio (PAR) and total electricity cost. Furthermore, HSA shows better results than FA in terms of PAR and electricity cost.

Home energy management within the traditional grid is difficult, so Smart Grid (SG) is introduced by upgrading the traditional grid, i.e., adding the Information Technology (IT) and Sensors Network (SN) to traditional grids. SG manages the Demand of electricity and help in solving the electricity load management problem. Demand Side response has two parts monitoring the electricity and notify consumers about its pricing scheme and bill, this can be done using smart meters. In smart metering system homes are integrated with Energy Management Controller (EMC) which uses Demand Side Management (DSM) systems based on a optimization technique. In this paper a system is proposed which manages the load by shifting from peak hours to off peak hours, reduce electricity bill, reduce waiting time and reduce Peak to Average Ratio (PAR). For simulations we use classification consist of 3 classes of appliances, Time of Use (ToU) as our pricing signal and Social Spider Optimization (SSO) our technique. The simulations results show the achievements of the system.

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

This paper discusses the performance improvement of a green building by optimization procedures and the influences of load characteristics on optimization. The green building is equipped with a self-sustained hybrid power system consisting of solar cells, wind turbines, batteries, proton exchange membrane fuel cell (PEMFC), electrolyzer, and power electronic devices. We develop a simulation model using the Matlab/SimPowerSystemTM and tune the model parameters based on experimental responses, so that we can predict and analyze system responses without conducting extensive experiments. Three performance indexes are then defined to optimize the design of the hybrid system for three typical load profiles: the household, the laboratory, and the office loads. The results indicate that the total system cost was reduced by 38.9%, 40% and 28.6% for the household, laboratory and office loads, respectively, while the system reliability was improved by 4.89%, 24.42% and 5.08%. That is, the component sizes and power management strategies could greatly improve system cost and reliability, while the performance improvement can be greatly influenced by the characteristics of the load profiles. A safety index is applied to evaluate the sustainability of the hybrid power system under extreme weather conditions. We further discuss two methods for improving the system safety: the use of sub-optimal settings or the additional chemical hydride. Adding 20 kg of NaBH4 can provide 63 kWh and increase system safety by 3.33, 2.10, and 2.90 days for the household, laboratory and office loads, respectively. In future, the proposed method can be applied to explore the potential benefits when constructing customized hybrid power systems.

In this paper, we propose enhanced power management techniques for low-power UWB communications. The proposed power management scheme is performed by using a real-time beacon parsing method and a time slot assignment technique based on the WiMedia MAC standard. It includes normal hibernation mode features and also includes a dynamic power management scheme using the hardware-controlled sleep mode switching method. By adopting several hardware-based power management features, we can easily decrease the power consumption while maintaining the overall system performance unchanged.

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Static task scheduling in distributed computing systems is a very complex problem and known to be NP-hard. This problem is even harder when the module execution times become probabilistic. In this paper we study the effect of probabilistic module execution times on the performance of task-scheduling algorithms. We show that in static task scheduling, for probabilistic module execution times, and in the existence of some factors there is no need to use an expensive task-scheduling algorithm. Given any two static task-scheduling algorithms that use deterministic module execution times in assigning task modules to the distributed system, the performance of these two algorithms will not remain the same when these module execution times become probabilistic rather than deterministic. We also study the effects of some factors an our results.

Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. This paper reviews the concepts, types, objectives, advantages, challenges, and research status of task scheduling in the multi-cloud environment. Task scheduling strategies proposed in the existing related references are analyzed, discussed, and summarized, including research motivation, optimization algorithm, and related objectives. Finally, the research status of the two kinds of task scheduling is compared, and several future important research directions of multi-cloud task scheduling are proposed.

This paper's investigation is based on a solid state transformer (SST) interfaced DC microgrid (MG) system. Correspondingly, a hierarchical power and energy management strategy is proposed for this system. The proposed power management strategy includes three control levels: 1) primary control, distributed control for system power balance without communication; 2) secondary control for system seamless switching from islanding mode to SST-enabled mode; 3) tertiary control for system energy management. Different from the previous methods, the multiple control objectives control is implemented in the tertiary control layer. Thus, the tertiary control can not only control the DC MG system output power, but manage the battery state (charge or discharge) in DC MG based on its state of charge (SOC). Lastly, several typical case studies are carried out and the simulation results verify the proposed power and energy management strategy.

Bandar udara Internasional Minangkabau memiliki satu apron dan satu hanggar. Apron tersebut memiliki 7 parking stand dengan number of parking stand adalah 1, 2, 3, 4, 5, 6A, dan 7. Parking stand 1 dan 7 di khususkan untuk pesawat light aircraft. Parking stand 2 dikhususkan untuk penerbangan Internasional. Keberadaan parkir 1 dan 7 kurang optimal karena jarang pesawat jenis light aircraft yang beroperasi. Jika terjadi jam puncak jumlah pesawat yang akan parkir di apron kurang mampu dilayani berdasarkan parking stand yang tersedia sehingga pesawat sering di holding di taxiway alpha, hal ini berdampak terhadap nilai waktu bagi penumpang dan nilai biaya bagi maskapai penerbangan. Tujuan penelitian ini adalah untuk menganalisis optimal kapasitas apron di bandar udara Internasional Minangkabau Padang.
 Metode penelitian dilakukan melalui analisis parkir stand terhadap pengaturan traffic di movement area melalui deskripsi analisis indeks parkir, durasi parkir, serta akumulasi parkir pada jam puncak dibandingkan dengan kapasitas apron yang tersedia serta slot time yang ditetapkan.
 Optimalisasi parkir pesawat (apron) Bandar udara internasional Minangkabau Padang dapat dilakukan melalui pengurangan parkir stand dari 7 menjadi 6 dengan dimensi pesawat medium, serta menekankan kepada pihak operator penerbangan agar tetap konsisten pada slop time yang ditetapkan atau mengusulkan revisi slop time pada waktu yang cukup luang