Load Management Model Research Articles

An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment

An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment

A sustainable and secure load management model for green cloud data centres

The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual-Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed-forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using benchmark real-world Google Cluster VM traces. The proposed model is compared with state-of-the-arts which reveals its efficacy in terms of reduced carbon emission and energy consumption up to 46.9% and 43.9%, respectively with improved resource utilization up to 16.5%.

Automatic management of electrical loads in more electric aircraft

PurposeThis paper aims to automatically generate load shedding sequences due to insufficient power supply, to ensure flight safety and complete flight task.Design/methodology/approachIn this paper, a power allocation and load management model, including logical and physical submodels of the distribution system, is first established according to different requirements of the loads in different flight phase and the current total power supply. Then, an optimal load management scheme based on an improved ant colony algorithm is proposed to automatically generate load shedding sequences for both safety-critical and nonsafety critical loads, to achieve a reliable and safe power supply.FindingsTo verify the efficiency and feasibility of the algorithm, the proposed method is verified in a virtual simulation platform. Simulation result illustrates that the proposed algorithm is efficient and feasible.Practical implicationsThe proposed method can provide guidance on load power supply when the civil aircraft is under abnormal power supply situation.Originality/valueAn optimal load management scheme is proposed by considering different requirements of the loads in different flight phase and the current total power supply.

Peak power management of residential building using demand side management strategies

Peak power management is one of the demand-side management methods aiming at regulating energy usage throughout the day. This paper discusses peak load, which relates to a consumer's peak demand during specific hourly hours and how to manage it. Shifting loads from demand hours to non peak hours relieves pressure on utilities to meet demand and supply while also lowering the cost to consumers. The Peak Load Management Model offers a more effective framework for lowering peak loads and moving loads from peak to off-peak hours. A cascaded artificial neural network is utilised to construct a demand side management strategy for managing peak electricity in residential buildings in this paper. The peak load control model's results and discussion are highlighted in the simulation results and performance review.

Potential of Organic Rankine Cycle for waste heat recovery from piston engine-based power plants in isolated power systems

Waste Heat Recovery with Organic Rankine Cycle (ORC) in piston engine-based power plants placed in isolated systems is addressed in this paper. The power plant subjected to study is located in La Palma, Canary Islands, considered a representative example of small-to-medium isolated power system where the diesel engine is the solely or the main conventional technology for generation. In those systems, the power plant load control to cover the grid demand results in a noticeable variability of the available heat. This challenge is addressed in this paper through a performance and economic evaluation of an intermediate thermal energy storage system. A techno-economic analysis of the proposed power plant is performed taking as input the annual exhaust thermal energy available. The ORC-Diesel unit interaction is studied by means of an in-house developed plant load management model. An annual power plant fuel economy gain and pollutant emission reduction between 5% and 7% is estimated. In addition, the extra power and spinning reserve provided by the ORC allows the power plant to reduce its operational cost through the reduction of the number of diesel units starts. • Waste heat recovery assessment from power plants in isolated power systems. • Efficiency increase and emission abatement due to the additional energy generated. • Additional power and spinning reserve allows diesel units disconnection. • Economically attractive solution, with payback times lower than 5 years.

System contributions of residential battery systems: New perspectives on PV self-consumption

France aims to boost the share of solar power generation in its electricity mix by 2030. When PV self-consumption systems become economically competitive, end-users will be willing to switch to PV self-consumption instead of using power from the grid. However, high penetration of distributed solar PV provokes a significant impact on the stability of electricity. The major systemic issues concern seasonal back-up power system associated with variable PV integration. Policymakers thus need to work on systemic solutions (e.g. load management, peak shaving) to support the large-scale integration of variable solar power. In this regards, this study aims to propose an innovative load management model based on the secondary application of residential batteries already installed for PV self-consumption. We performed a prospective economic analysis to identify potential system contribution of French residential PV-battery systems in 2030. The aim is to reduce systemic impact of distributed solar PV system integration thanks to collective use of distributed residential batteries for load management when they are not in use in winter. A sensitivity analysis has been conducted based on different projections of residential PV self-consumption in 2030 (RTE, Enedis). Our study then concludes with key messages and policy recommendations.

A Novel Electric Vehicles Charging/Discharging Management Protocol Based on Queuing Model

High electric vehicles (EVs) penetration is expected to increase smart grid solicitation especially with various EV charging demands. As result, the EV charging process at the supply station has to be managed in the way to promote the EV satisfaction level while preserving smart grid stability. In this article, the bidirectional power flow between EV and grid; Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G), is exploited. We make a profit from the unused electric power of EVs and we present an EV load management technique based on EV charging and EV discharging coordination. We propose a peak load management model (PLM) used to schedule EVs for charging or discharging service according to the power demand with the timing and location where each EV need to be served. Also, we propose an Electric Vehicle Supply Equipment (EVSE) selection model to guide EVs to the supply station. We develop a mathematical formalism for handling requests for EV charging/discharging at EVSE based on queuing theory. Those models are evaluated while considering the mobility of vehicles in an urban scenario and time-of-use-pricing (TOUP). Finally, extensive matlab simulations are conducted to validate the proposed approach and demonstrate its effectiveness.

Peak Time Load Management in Domestic Sector

The electricity demand is increasing day by day and leads do increase in generation and power transmission. India consumes about 3.4% of world energy consumption. The energy demand has grown at the average of 3.6% per annum over the past 30 years. Usually the demand for the electricity increases abruptly during peak hours. So it is necessary to monitor the loads during peak hours in order to provide energy sufficiently. The government has taken steps to reduce power consumption in industrial sector. Demand reduction can also possible in domestic sector. This Paper presents a load management model for domestic houses. Every house has a maximum demand limit for essential loads. During peak hours, if the consumer consumes power more than the preset limit,, based on the priority of load the power supply to the load will be turned off automatically and by using the cloud, the data can be visualized and analyzed. The power consumption during peak time is updated in cloud using NODEMCU and Thingspeak. The information can also be used for load forecasting

A real-time decision model for industrial load management in a smart grid

The potential impacts of evolving industrial load management into demand response (DR) programs have been widely acknowledged. This paper proposes a real-time decision model for the load management of an industrial manufacturing process in the face of ever-changing real-time prices (RTPs). Due to the inherent dependence between consecutive tasks in a manufacturing process, the decision model must take future load management into consideration. The challenge lies in the uncertainty that future RTPs cannot be known in advance. In view of this, robust optimization was adopted to deal with future price uncertainties, such that the proposed model is able to make timely decisions for industrial load control when receiving the RTP for the current time slot, while considering load scheduling in future time slots. The case study was conducted on a steel powder manufacturing process; simulation results validated the effectiveness of the proposed real-time decision approach from various perspectives.

A systematic approach towards developing prototype of AMI based DSM model for load management

Participating in demand response has significant advantages for both consumers and electricity producers, i.e., saving on high electricity prices for the user and helping utilities in peak load curtailment. Further increased the use of renewable energy and energy storage, along with the need to increase energy efficiency are the main driver for the implementation and utilization of DSM. In DSM to monitor and control of the low voltage power grid, a computer system (a combination of hardware and software) is provided which is advance controlled by SCADA (Supervisory Control and Data Acquisition) System. Next generation application and DSM concept are important with smart grid to give the answer to the need of the changing power system. This research paper insights the development of advanced metering infrastructure based demand side management model for load Management. The main objective of this application is consumption optimization and automation in operation. This paper proposes a fixed load control strategy with fixed forecasted photo voltaic generation, battery storage system and priority based load curtailment mechanism.

Demand Side Management Technique for Domestic Level by Using Fuzzy Logic Controller

Electrical energy is critical resources for all nation building activities in order to keep prosper economy side for the nation. Therefore, demand for electricity is on the ever increasing side in both industrial and domestic levels. This paper presents a basic technique using a fuzzy logic technique for managing the power consumption at residence level by focusing on water heater (WH) load control. It aims to increase the utilization of electricity supply system. The proposed algorithm in this paper is shown to design a model for load management during peak hours and off peak hours at domestic level by developing the preference setting. Then, simulation results are expected to show the effectiveness of the fuzzy logic controller in demand side management (DSM) strategy.

Research of Interruptible Load Management Model via Incentive Compatibility

In this paper interruptible load management model via incentive compatibility is researched. Simulated the interruptible load capacity and disruption of economic compensation of the interruptible load management with using MATLAB and compare the cost of interruptible load management and construction of standby units, find that interruptible load management can reduce the cost of electricity companies. But it should be noted that when the interrupt the load is too large, the implementation of interruptible load management will increase the operating costs of the utilities.

Models of domestic occupancy, activities and energy use based on time-use data: deterministic and stochastic approaches with application to various building-related simulations

Time-use data (TUD) have a large potential for improving occupancy and load modelling and for introducing realistic behavioural patterns into various simulations. In this article, previously developed models of occupancy, activities and energy use based on TUD are extended and described in a general framework. Two extensions are studied: deterministic conversion of empirical TUD is extended into a complete thermal load model encompassing both occupancy and various end-uses and a Markov-chain approach for generating synthetic TUD sequences is extended to include a model for load management. Three examples of building-related applications are presented: simulation of indoor climate in a low-energy building, household electricity load management in response to time-differentiated electricity tariffs and simulations of load matching in a net zero energy building. The main conclusion is that the extended model framework can generate detailed and realistic behavioural patterns that allow diversity and correlations between end-uses to be taken into account.

A new long term load management model for asset governance of electrical distribution systems

Long term load management (LTLM) is one of the key factors in making decisions regarding new investments in distribution systems. However, none of the previous studies have investigated the effect of external factors such as governance, urban planning and social behavior factors on LTLM. In this paper, a new LTLM model is proposed to determine the influence of external factors on LTLM. Distribution system development indices have been used to obtain asset governance targets; these indices can help Distribution Companies (DISCOs) compromise between reliability and running the system economically. Capacity utilization and the number of maneuver points are used here to do LTLM and improve asset governance. Numerical studies on a real distribution system (city with population about 200,000) have been conducted and sensitivity analysis of maneuver points and capacity utilization level with respect to external factors is studied and analyzed. The results show the feasibility of the proposed LTLM to obtain higher efficiency from the viewpoint of cost and quality service compared to conventional LTLM.