Forecasting of Electricity Demand by Hybrid ANN-PSO under Shadow of the COVID-19 Pandemic

Here in the research paper, we did not use smart methods to predict the future but rather to show the impact of the pandemic, we used the hybrid method using the PSO-ANN algorithm to demonstrate the impact of COVID-19 on electricity consumption and to demonstrate that we used two basic steps. The first step is to demonstrate that the hybrid method is effective for prediction. We showed that the prediction for 2019 was good, and that was before the onset of COVID-19. As for the second step, we applied the same hybrid algorithm after the emergence of COVID-19, i.e. for 2020, to note the difference between the prediction and the current pregnancy, which represents the impact of this epidemic, and this prediction in the short term. A short-term role in the operation of a power system in terms of achieving an economical electrical output and avoiding losses or outages. We've collected four consecutive years of data that is downloaded every quarter-hour of the day. Electricity consumption in Algeria is used as an input to the PSO-ANN learning algorithm. The results of the PSO-ANN pregnancy prediction algorithm have better accuracy than the ANN prediction. In the future but with the emergence of a pandemic that has had a clear difference and represents economic losses in the field of electricity, the epidemic should be viewed as a short-term variable to reduce the level of energy loss and generation cost.

Electrical Power Generation System: Optimal Design for Medium-Load Industries with High-Rated Generators

The demand for electrical power is rapidly increasing due to the rise of industries in developing countries. Power generation stations are having troubles to strike a balance between demand and generation. In this situation, it is urged that appropriate remedial action be taken. Rising power demand can be met by designing an efficient electric power generation system which will also help lowering the generation cost. It is shown that while high rated electric power generators are connected in parallel the value of neutral current is rising and the cooling temperature is also increased. Here, the goal of this experimental work is to present a new model for designing an efficient power production system for average-load (ranging up to 8000 Amp, 440 V) industries to minimize the demand on centralized interconnected grid. A scheme is proposed with four generators (2500 kVA, 2000 kVA, 2000 kVA and 1250 KVA) in parallel and enough cooling arrangement is provided with minimal cost. The coolant temperature is maintained 61 °C to 61.5 °C and at that time diesel temperature is not more than 38.5 °C. The amount of neutral-current is also optimized (up to 8.5 Amp.) which was more than 12 Amp. At the morning and afternoon, the neutral current is almost constant, but it is bit fluctuating between 7.5 Amp to 8.2 Amp at mid-day. The final outcome shows, the suggested system is efficiently stable with the change of load and generates optimal electricity.

Consensus-Based Distributed Optimization of Generation Dispatch of Multi-Area AC Systems Interconnected by DC Lines

This paper is dedicated to solving the distributed optimization of generation dispatch of multi-area AC systems interconnected by DC lines, which aims at minimizing the total generation cost while satisfying the power supply demand balance and generation capacity constraints. A novel nodal loss formula which derived from the branch active power flow equation is proposed based on phase angle and impedance to improve the system economy. A distributed algorithm based on consensus is built to solve the generation dispatch problem. It has a great effect on improving convergence effect and rate of the system. The control strategy is used on the structure of multi-area interconnection, which improves the reliability of power supply and guarantee the power quality. The study was conducted using three area AC systems interconnected by DC lines. The simulation results show that the proposed generation dispatch method is reliable in convergence. It provides an effective tool for distributed optimization of generation dispatch of multi-area AC systems interconnected by DC lines.

An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.

Optimal Placement of Wind Power Plants in Transmission Power Networks by Applying an Effectively Proposed Metaheuristic Algorithm

In this paper, a modified equilibrium algorithm (MEA) is proposed for optimally determining the position and capacity of wind power plants added in a transmission power network with 30 nodes and effectively selecting operation parameters for other electric components of the network. Two single objectives are separately optimized, including generation cost and active power loss for the case of placing one wind power plant (WPP) and two wind power plants (WPPs) at predetermined nodes and unknown nodes. In addition to the proposed MEA, the conventional equilibrium algorithm (CEA), heap-based optimizer (HBO), forensic-based investigation (FBI), and modified social group optimization (MSGO) are also implemented for the cases. Result comparisons indicate that the generation cost and power loss can be reduced effectively, thanks to the suitable location selection and appropriate power determination for WPPs. In addition, the generation cost and loss of the proposed MEA are also less than those from other compared methods. Thus, it is recommended that WPPs should be placed in power systems to reduce cost and loss, and MEA is a powerful method for the placement of wind power plants in power systems.

Preventive Control Strategy of Cascading Fault considering Safety and Economy

The operation and structure of the power system are becoming increasingly complex, and the probability of cascading fault increases. To this end, this paper proposes a cascading fault preventive control strategy that considers safety and the economy. First is to give a mathematical form to discriminate the cascading fault according to the action characteristics of the current-type backup protection. Second, the safety and economy of the system are evaluated in terms of power grid safety margin and generation operation cost, respectively, the initial faults are selected based on the power grid vulnerability and safety margin, and a cascading fault preventive control model is constructed for different initial faults’ scenarios. The model is a two-layer optimization mathematical model, with the inner model being solved by particle swarm optimization to minimize the power grid safety margin. The outer model is solved by the multiobjective algorithm to minimize generation cost and maximizing power grid safety margin. Finally, the calculated Pareto set is evaluated using fuzzy set theory to determine the optimal generator output strategy. The feasibility of the proposed method is verified by conducting a simulation study with the IEEE39 node system as an example.

Economic load dispatch solutions considering multiple fuels for thermal units and generation cost of wind turbines

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost.