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An improved Reinforcement Learning (RL) agent with a Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to control the frequency of hybrid power systems. Weighted signals of system frequency, frequency deviation, integration of frequency deviation, and differentiation of frequency deviation represent the inputs to the RL system. The reward equation used is a discrete equation considering a study period of 400 s with a sampling time of 0.25 s (i.e., 1600 samples per run). The reward equation allows the RL method to consider the solutions where the system operates with a frequency between 49.8 and 50.2 Hz, which is the allowable operating range of frequency for a 50 Hz-based power system under normal load changes in several countries. To enhance the performance of the proposed RL controller further, the input weights (i.e., observation weights) are optimized by the Gorilla Troops Optimizer (GTO) algorithm, which comprises high convergence and robustness features. The proposed control technique is verified using a two-area system with various combinations of renewable energy sources, conventional sources, and energy storage units. The energy storage units include battery energy storage and superconducting magnetic energy storage. This article's main contribution is applying a novel GTO-based optimal RL controller to enhance the frequency control of hybrid power systems. The proposed load frequency controller is compared with optimally tuned PI and PID controllers. The results of this comparison are in favor of the proposed controller. All studies of this research are carried out using MATLAB environment.

On-board batteries from electric vehicles can be used in domestic installations as an auxiliary source of energy through their vehicle-to-home features. To properly manage this kind of assets, Home Energy Management systems are expected to be essential tools in future smart grids, enabling a more efficient use of energy in residential applications. However, these tools normally perform under severe uncertain environments caused by intermittent renewable generation, uncertain prices, uncertain demand as well as uncertain behaviour of electric vehicles. In this regard, further research should be conducted in order to develop advanced robust energy management tools that account for the effect of uncertainties. So far, most of the existing literature focused on simple stochastic approaches, that may pose various issues, while other more sophisticated uncertainty models have not been sufficiently studied. In this regard, this paper proposes a fully robust Home Energy Management model, which accounts for all the inherent uncertainties that may arise in domestic installations. In contrast to previous researches, robust optimization is applied to all the uncertainties, resulting in a tractable, but robust yet paradigm model which allows to adapt the degree of robustness. A case study is conducted on a benchmark prosumer paradigm to validate the new proposal as well as highlight the benefits of flexible appliances. Results reveal the usefulness of the new tool, which allows to handle very unfavourable uncertain profiles, especially in case of the electric vehicles, for which a very low initial state-of-charge and early departure time can be assumed, leading to more conservative results that suppose incrementing the electricity bill by 38 %.

Energy storage will play a vital role in the decarbonization of the electricity sector, especially in domestic installations. In such kind of systems, home energy management applications are becoming essential. These kinds of tools enable active control of domestic appliances and storage systems to pursue a more efficient use of energy in household installations. However, the emergence of renewable generators and electric vehicles makes the operation of residential assets more difficult, as multiple uncertainties should be managed on a whole. These unknowns have a different character, in fact, some of them can be easily predicted while others are subject to a high level of randomness. This paper addresses this issue by developing a novel home energy management tool that accounts for the different levels of the randomness of the uncertainties involved in home operation. To this end, a novel Lexicographic-Interval formulation of the home energy management problem is presented, by which the uncertainties can be easily modelled using interval notation. Unlike conventional tools, the new proposal sorts the uncertainties according to their level of randomness. Then, the energy management mechanism performs the scheduling plan according to the predefined classification so that the more random parameters rule the impact of others, thus giving more importance to those uncertainties that are hardly predictable. A benchmark case study is performed to validate the new proposal and illustrate its capabilities. Different tariffs are compared, showing that the Time-of-Use mechanism is normally more expensive than Real-Time-Pricing tariffs, increasing the electricity bill by 12 % in some cases. However, the level of robustness achieved with Time-of-Use tariffs seems higher, allowing to assume an abruptly unexpected reduction of photovoltaic generation.

Load Frequency Control (LFC) has become a more challenging issue, especially with the increases in generation's unpredictability, inconsistency, and load variations leading to reduced system stability and reliability. This paper presents a novel application of the transient search optimization (TSO) upon Model Predictive Control (MPC) based regulators to solve the LFC problem for multiple zones power networks with hybrid renewable generators and storage devices. An exact model with a governor's dead band, generator's rate constraint, and measurement communication delay are employed. Renewable energy sources (RESs), such as wind and solar systems, in addition to fuel cell generators with different storage elements, such as superconducting magnetic energy storage (SMES) and battery energy storage (BES), are incorporated into the power system investigated in this study. To assess the effectiveness of the TSO-MPC controllers in nonlinear conditions due to renewable energy generation and storage ambiguities, actual wind speed measurements are incorporated into the wind generation model and real solar irradiance and temperature data for the photovoltaic system to achieve an extra credible analysis. Furthermore, the performance of the TSO-MPC regulator is compared with the performance of other optimization techniques based on proportional-integral-derivative (PID) controllers under different conditions, and the results of the proposed controller outperform other controllers by >10 % of the transient specifications. The transient response of the power system is significantly enhanced with the controller proposed in this study.

This article introduces a novel hybrid adaptation algorithm comprising both continuous mixed p-norm (CMPN) strategy and the block-sparse Bayesian (BSB) technology to online adapt all the proportional plus integral (PI) controller gains of the power converters of flywheel energy storage units (FESUs). The FESU is based on the doubly fed induction machine (DFIM). The principal target is to improve the transient stability of grid-connected wind farms. To obtain a realistic study, the two-drive train model is utilized in the wind farm modeling affecting the transient analyses. The cascaded control scheme is implemented on controlling both the generator side converter and the grid side inverter of the FESU using the proposed adaptive CMPN-BSB PI controller. The FESU is tied to the point of common coupling of the wind farm, which is tied to the standard IEEE 39 bus system. The performance of the proposed technology is tested under subject the system to different severe faults. The validity of adaptive CMPN-BSB PI controlled FESU is tested by comparing the numerical results with that achieved by least mean square adaptive controlled FESU. The numerical results are performed by PSCAD software. The proposed controlled FESUs will help in improving the transient stability of wind farms and their power quality concerns will be enhanced.

•This paper presents a novel adaptive algorithm to control the SMES devices.•The goal is to smooth the real power output from grid-tied wind farms.•Detailed models of the system are implemented in this paper.•Real wind speed data and practical SMES units are proposed in the study.•The proposed model is verified by comparing its results with other model results.

•Hydrogen produced from renewable sources (green hydrogen).•Green hydrogen can be effectively stored from surplus renewable energy.•A novel optimal scheduling model for robust optimal scheduling of isolated microgrids.•Logical rules are incorporated into the conventional optimal scheduling tool.•A case study serves to validate the model and highlight the paper.