More than a decade of research in the field of thermal, motion, vibration and electromagnetic radiation energy harvesting has yielded increasing power output and smaller embodiments. Power management circuits for rectification and DC-DC conversion are becoming able to efficiently convert the power from these energy harvesters. This paper summarizes recent energy harvesting results and their power management circuits.

Micropower energy harvesting

Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid

Recent progress on the utilization of waste heat for desalination: A review

Cyber-physical systems (CPSs) are vulnerable to cyber-attacks. Nowadays, the detection of cyber-attacks in microgrids as examples of CPS has become an important topic due to their wide use in various practical applications from renewable energy plants to power distribution and electric transportation. In this article, we propose a new artificial intelligence (AI)-based method for the detection of cyber-attacks in direct current (dc) microgrids and also the identification of the attacked distributed energy resource (DER) unit. The proposed method works based on the time-series analysis and a nonlinear auto-regressive exogenous model (NARX) neural network, which is a special type of recurrent neural network for estimating dc voltages and currents. In the proposed method, we consider the effect of cyber-attacks named false data injection attacks (FDIAs), which try to affect the accurate voltage regulation and current sharing by affecting voltage and current sensors. In the presented strategy, first, a dc microgrid is operated and controlled without any FDIAs to gather enough data during the normal operation required for the training of NARX neural networks. It is worth mentioning that in the data generation process, load changing is also considered to have distinguishing data sets for load changing and cyber-attack scenarios. Trained and fine-tuned NARX neural networks are exploited in an online manner to estimate the output dc voltages and currents of DER units in dc microgrid. Then, based on the error of estimation, the cyber-attack is detected. To show the effectiveness of the proposed method, offline digital time-domain simulation studies are performed on a test dc microgrid system in the MATLAB/Simulink environment, and the results are verified using real-time simulations using the OPAL-RT real-time digital simulator (RTDS).

Detection of False Data Injection Cyber-Attacks in DC Microgrids Based on Recurrent Neural Networks

The arriving optimum improvement of a thermodynamic system of energy conversion such as a combined cycle power plant (CCPP) is complicated due to the existence of different factors. Energy and exergy analysis is utilized as effective methods to determine both the quantity and quality of the energy sources. This paper reviews the latest thermodynamics analysis on each system components of a CCPP independently and determine the exergy destruction of the plant. A few layouts of the CCPP plant from different locations considered as case studies. In fact, the most energy losses occurred in the condenser compared with the plant components. It found that in the combustion chamber (CC) the highest exergy destruction occurred. The ambient temperature causes an evident decrement in the power production by the gas turbine (GT). The result has proved that besides energy, exergy analysis is an efficient way to the assessment of the performance of the CCPP by recommending a more advantageous configuration of the CCPP plant, which would lead to reductions in fuel required and emissions of air pollutants.

A comprehensive review on the exergy analysis of combined cycle power plants

Waste heat recovery system plays a pivotal role for heat extractions in every energy consuming sector. Thermo-Electric Module converts this waste heat into useful work done as “electric energy”. Electric energy thus produced possesses many promissory benefits, such as: (a) energy storage in DC batteries, (b) running various loads in residential, commercial and industrial sector, (c) exporting power to grid, thus earning valuable revenues, (d) maintain economic growth of plant, and (e) environment friendly system. Recently, among various renewable energy technologies, Waste Heat Recovery (WHR) is paid much consideration in commercial, residential, and industrial sectors. In past decade, a number of WHR technologies are developed and improved. In this paper, relevant research works are reviewed regarding existing technologies of WHR. Thermoelectric Generator (TEG) is one of extensively emerging WHR technique among existing technologies. TEG with promising features, such as: simpler structure, vast scalability, solid state operation, the absence of toxic residuals, a long life span of reliable operation, no noise or vibration, and lack of chemical reaction or moving parts. Basic principle of TEG with its series and parallel arrangement for voltage and current enhancement is also reviewed. Our work described a standalone thermoelectric module generate 1–125 W whose modular arrangement produces ~ 5 kW and the wattage improvement is defendant on array size. The potential application of TEG in various applications are comprehensively discussed and described. A detailed description to Pakistan energy status and WHR potential especially in Cement Industry is assessed in this survey. Finally, the TEGs model in Matlab/SimScape for direct heat energy harvesting with DC/DC converter is simulated, as a case study of “Officer Colony, Abbottabad, Pakistan”.

A survey on waste heat recovery: Electric power generation and potential prospects within Pakistan

Electrical load and wind power forecasting are a demanding task for modern electrical power systems because both are closely linked with the weather parameters, such as temperature, humidity, and air pressure. The conventional methods of electrical load and wind power forecasting are useful to handle dynamic and uncertainties in un-regulated energy markets. However, there is still need of relative improvement by incorporating weather parameter dependencies. Considering above, a genetic algorithm-based non-linear auto-regressive neural network (GA-NARX-NN) model for short- and medium-term electrical load forecasting is presented with relative degree of accuracy. Causality, a new modelling technique, is employed for monthly and yearly wind speed patterns predictions and long-term wind speed forecasting. Real-time historical electrical load and weather parametric data are used to critically observe the performance of the proposed models compared to various state-of-the-art forecasting schemes. Numerical simulations are conducted that validates the proposed models based on various error calculation methods, such as mean absolute percentage error, root mean-square error, and variance ( σ 2 ). The quantitative comparison with five traditional techniques for electrical load and wind speed forecasting reveals that the GA-NARX-NN method is more accurate and reliable.

Genetic algorithm-based non-linear auto-regressive with exogenous inputs neural network short-term and medium-term uncertainty modelling and prediction for electrical load and wind speed

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

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Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

Multi-correlated failures lead to severe power outages in an inter-connected power network. The critical loads of the data center, such as servers and chillers are highly dependent on the grid power. The service reliability and fault tolerance of the data center becomes challenging under multi-correlated failures. Data miss-management and service unavailability under such scenario will result in the massive revenue loss. To address this problem under multi-correlated failures, a fault tolerant model is proposed to minimize the revenue loss. Moreover, considering the system dynamics, our model maximizes throughput and operational time of the data center. Furthermore, we formulate a stochastic optimized system model that captures power consumption, power distribution, data center generation, data center workload, service deadline, and revenue loss. The effectiveness of the proposed fault-tolerant model is tested and validated using real-time data.

N. Tareen

Papers

A survey on waste heat recovery: Electric power generation and potential prospects within Pakistan

Genetic algorithm-based non-linear auto-regressive with exogenous inputs neural network short-term and medium-term uncertainty modelling and prediction for electrical load and wind speed

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

Fault Tolerance of Data Center under Multi-Correlated Failures