Search results

Load forecasting is important to any electrical grid, but for the developing and third-world countries with power shortages, load forecasting is essential. When planed load shedding programs are implemented to face power shortage, a noticeable distortion to the load curves will happen, and this will make the load forecasting more difficult.

In this paper, a new load forecasting model is developed that can detect the effect of planned load shedding on the power consumption and estimate the load curve behavior without the shedding and with different shedding programs. A neuro-Fuzzy technique is used for the model, which is trained and tested with real data taken from one of the 11 KV feeders in Najaf city in Iraq to forecast the load for two days ahead for the four seasons. Load, temperature, time of the day and load shedding schedule for one month before are the input parameters for the training, and the load forecasting data for two days are estimated by the model.

To verify the model, the load is forecasted without shedding by the proposed model and compared to real data without shedding and the difference is acceptable.

The proposed model provides acceptable forecasting with the load shedding effect available and better than other models. The proposed model provides expected behavior of load with different shedding programs an issue helps to select the appropriate shedding program. The proposed model is useful to estimate the real demands by assuming load shedding hours to be zero and forecast the load. This is important in places suffer from grid problems and cannot supply full loads to calculate the peak demands as the case in Iraq.

The voltage stability is a basic principle in the power system operation. Different short circuits, load growth, generation shortage, and other faults which disturb the voltage stability are serious threats to the system security. The voltage instability causes dispersal of a power system into sub-systems, and leads to blackout as well as heavy damages of the system equipments. Optimum load shedding during contingency situations is one of the most important issues in power system security analysis.

In this paper, a New Binary Particle Swarm Optimization technique (NB-PSO) is proposed for solving the integer-valued modeling of under-voltage load shedding (UVLS) problem. The updating mechanisms for the position and velocity of binary particles are amended in the proposed NB-PSO by using a new velocity definition, which has an excellent efficiency for solving complex binary optimization problems.

The effectiveness and capability of the proposed NB-PSO optimization algorithm were illustrated according to the simulation results of applying it to the IEEE 118-bus test system. In addition, the performance of the proposed NB-PSO based method was compared with other optimization algorithms, such as the Binary Particle Swarm Optimization (BPSO) and Hybrid Discrete Particle Swarm Optimization (HDPSO) techniques. It was perceived that the NB-PSO performs superior than the BPSO and HDPSO for determining the best location and the minimum level of load shedding in order to prevent voltage instability.

The proposed NB-PSO has novel modifications and techniques to enhance both exploration and exploitation capabilities to find the optimal feasible solution. The simulation results confirmed the effectiveness of the proposed method in determining the best location and the minimum amount of load shedding for voltage collapse prevention.

The study examined perceptions and beliefs that influence the intention to adopt solar energy solutions in the Zambian Housing Sector. This is important because empirical evidence for measures aimed at improving the adoption of solar energy solutions with SSA in general or a Zambian context, in particular, are largely missing from the literature.

Hierarchical multiple linear regression was used to analyse quantitative data collected through an online questionnaire survey. A total of 947 valid responses were obtained from a convenient sample of household heads.

The results show that attitude towards solar energy solutions, subjective norms, perceived benefits, perceived trust, knowledge about solar energy solutions, load-shedding and social norms, in that order of magnitude, influence the intention to adopt solar energy solutions. Perceived behavioural control, perceived risk and perceived cost did not influence the intention to adopt solar energy solutions.

The results provide empirical evidence of important factors to drive the adoption of solar energy solutions in Zambia. The results further show that knowledge about available solar energy solutions, rather than general knowledge about renewable energy, influence the adoption intention of solar energy solutions.

This paper describes the on‐going work done by Hydro‐Québec to optimize the settings of automatic devices installed in its main power plants to maintain secure operation under extreme contingencies. The automatic generator tripping and load shedding system (RPTC) described in this paper is installed at the Churchill Falls hydroelectric power plant (5,500 MW) in Labrador. Data mining techniques such as decision trees and regression trees have been used. Real time snapshots of the Hydro‐Québec power system collected over a 5 year period have been used to generate large amounts of results by transient stability simulations. The processing of these data has been done using software developed by the University of Liege. This approach gives the most relevant parameters and finds optimal settings for the RPTC system, minimizing the number of tripped generator units while maintaining the same performance in terms of security coverage. New operation rules can thus be established.

The researchers and policy makers worldwide have proposed many ideas for smart cities and homes in urban areas. The extensive work done for urban smart homes neglects the unique constraints of homes at remote mountain tops and deserts and rural village homes. The purpose of this paper is to propose a smart energy management system for a self-sustained home of any type situated in any geographical location with the availability of renewable energy sources like solar, etc. The purpose is mainly to highlight the importance and advantages of direct current (DC) homes with DC loads rather than a conventional alternating current (AC) home with both AC and DC loads. An attempt has been made to evolve a multi-agent coordinated control for the low voltage direct current (LVDC) smart home system.

LVDC supply systems with in situ power generation are providing an efficient solution for the energy needs of a DC smart home. The individual sub-systems of the LVDC system have their unique functions and priorities and hence require both coordinated and independent control. The entire DC smart home system is modeled in the Matlab and codes are implemented for each agent of the home. LVDC grid is operating either in battery connected mode or utility grid-connected mode, and the DC link voltage is held constant in both the cases. Energy imported from the utility grid is minimized by load shedding during the rectifier mode of the bidirectional converter. In addition, load shedding is also done when the battery is discharging to increase the discharge time of the battery. Load shedding is done on the basis of a fixed priority of loads. A 48 s simulation is performed on the Matlab model to bring out the 24-hour operation of the proposed system. Various modes are simulated and the corresponding actions of the agents are tested.

A new control strategy with agents for each sub-system of the LVDC system is presented. Each individual agent works in tandem with other agents and meets its own control imperatives without compromising the requirements of the overall system. Unlike the centralized control system, the proposed control strategy is a distributed control system. The control algorithm for each of the agents is developed, and the pseudo code is presented. The results of the simulation of the proposed scheme are presented to confirm the usefulness of the new control approach.

The multi-agent concept for an energy management system is less addressed and thus its potential for efficient home energy management is presented. The proposed multi-agent strategy for a complete DC smart home with exclusive DC loads is not done earlier and is reported for the first time. The success of this strategy can be extended to other DC micro-grid systems like telecom power systems, ships, aircraft, datacentres, server rooms, residential complexes and commercial malls.

This paper aims to automatically generate load shedding sequences due to insufficient power supply, to ensure flight safety and complete flight task.

In 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.

To 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.

The proposed method can provide guidance on load power supply when the civil aircraft is under abnormal power supply situation.

An optimal load management scheme is proposed by considering different requirements of the loads in different flight phase and the current total power supply.