No abstract

Load frequency control (LFC) is playing an indispensable role to achieve the secure and economic operation of power grids. However, the existing LFC schemes either may rely on a nonlinear grid model under perfect operating condition with nominal parameters, or they may adopt a complicated control structure of high order. These LFC schemes may have poor control performance or even loss of stability in real-time implementation due to the grid uncertainties and the change of system operation scenarios. Consequently, a hybrid control method with two control loops considering various practical scenarios is originally proposed in this paper. In the inner loop, variable universe fuzzy logic control is applied to mitigate the impact of load disturbances on control performance. In the outer loop, an incremental genetic algorithm is employed to online optimize the control parameters. The performance of the proposed control method is comprehensively tested on a MATLAB/Simulink-based LFC model and a real-time digital simulatorbased real-life 49-bus power system. The extensive results show that the proposed hybrid method exhibits comparatively better control performance than an adaptive fuzzy logic controller and an improved proportion integration controller.

Driven by inertial demand from the grid, the virtual synchronous generators (VSGs) are widely utilized in distributed generation systems. However, harmonic sources in the distributed generation systems with high grid impedance will cause grid voltage distortion. Distorted voltage greatly affects the power quality of VSG. Moreover, it is difficult to suppress different types of harmonics (such as subsynchronous harmonics and non-integer high-frequency harmonics) by one conventional solution in VSGbased grid-connected system. To solve the problem, this paper proposes a magnitude-reshaping strategy to increase the output impedance in all harmonic-frequency-bands, thereby suppressing the harmonics. The magnitude-reshaping strategy consists of a notch filter and harmonic regulator. The notch filter extracts harmonic components of grid current, whereas the harmonic regulator increases the equivalent harmonic impedance. The equivalent harmonic circuit of reshaped VSG-based system is equivalent to open circuit. Therefore, the power quality of grid-connected current can be guaranteed. To analyze the performance of the magnitude-reshaping method, frequency-coupling impedance model of VSG is established. Furthermore, Comparing with the conventional harmonic suppression methods, the frequency response characteristics of the equivalent harmonic impedance are analyzed. Finally, the effectiveness of proposed control strategy is validated by experiment results.

The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.