Abstract
In order to reach better results for pulse width modulation (PWM)-based methods, the reference waveforms known as control laws have to be achieved with good accuracy. In this paper, three control laws are created by considering the harmonic components of modular multilevel converter (MMC) state variables to suppress the circulating currents under nonlinear load variation. The first control law consists of only the harmonic components of the MMC's output currents and voltages. Then, the second-order harmonic of circulating currents is also involved with both upper and lower arm currents in order to attain the second control law. Since circulating current suppression is the main aim of this work, the third control law is formed by measuring all harmonic components of circulating currents which impact on the arm currents as well. By making a comparison between the switching signals generated by the three proposed control laws, it is verified that the second-order harmonic of circulating currents can increase the switching losses. In addition, the existence of all circulating current harmonics causes distributed switching patterns, which is not suitable for the switches' lifetime. Each upper and lower arm has changeable capacitors, named "equivalent submodule (SM) capacitors" in this paper. To further assess these capacitors, eliminating the harmonic components of circulating currents provides fluctuations with smaller magnitudes, as well as a smaller average value for the equivalent capacitors. Moreover, the second-order harmonic has a dominant role that leads to values higher than 3 F for equivalent capacitors. In comparison with the first and second control laws, the use of the third control-law-based method will result in very small circulating currents, since it is trying to control and eliminate all harmonic components of the circulating currents. This result leads to very small magnitudes for both the upper and lower arm currents, noticeably decreasing the total MMC losses. All simulation results are verified using MATLAB software in the SIMULINK environment.

Abstract
Energy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise short-term LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.

Abstract
This study proposes a novel multi-agent method for electric vehicle (EV) owners who will take part in the electricity market. Each EV is considered as an agent, and all the EVs have vehicle-to-grid capability. These agents aim to minimise the charging cost and to increase the privacy of EV owners due to omitting the aggregator role in the system. Each agent has two independent decision cores for buying and selling energy. These cores are developed based on a reinforcement learning (RL) algorithm, i.e. Q-learning algorithm, due to its high efficiency and appropriate performance in multi-agent methods. Based on the proposed method, agents can buy and sell energy with the cost minimisation goal, while they should always have enough energy for the trip, considering the uncertain behaviours of EV owners. Numeric simulations on an illustrative example with one agent and a testing system with 500 agents demonstrate the effectiveness of the proposed method.

Abstract
There is a growing need for increased flexibility in modern power systems. Traditionally, this flexibility has been provided by supply-side technologies. There has been an increase in the research surrounding flexibility services provided by demand-side actors and technologies, especially flexibility services provided by prosumers (those customers who both produce and consume electricity). This work gathers 1183 peer-reviewed journal articles concerning the topic and uses them to identify the current state of the art. This body of literature was analysed with two leading textual and scientometric analysis tools, SAS (c) Visual Text Analytics and VOSviewer, in order to provide a detailed understanding of the current state-of-the-art research on prosumer flexibility. Trends, key ideas, opportunities and challenges were identified and discussed.

Abstract
During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.

Abstract