Abstract
Recent developments in the field of smart grid have led to renewed interest in load monitoring strategies for achieving effective energy management schemes. There are vast amount of published studies describing the role of non-intrusive load monitoring (NILM) system based on various learning algorithms. It is widely known that the accuracy of load identification depends strongly on utilized methods and its features. Thus, the main aim of this study is to investigate the comparative accuracy of machine learning algorithms which have the same training data with different feature subsets. Afterwards, a low-cost data acquisition system for NILM using bagged tree ensemble algorithm is developed and demonstrated in detail. The proposed structure is tested on the ThingSpeak IoT platform to reveal the effectiveness of the evaluated concept.

Abstract
Virtual power plants (VPPs) are defined as an aggregator of different types of energy resources and flexibility, coordinated by VPP owner through a smart control system. A correct establishment of a VPP will result in reduced electricity costs for the consumers within the VPP. One of the key aspect of VPP's success is the consumer engagement in order to manage their flexibilities effectively. Gamification is an efficient way of learning and engagement, which can efficiently change the behavior of consumers towards participating in programs provided by VPPs for energy cost reduction. In this paper, a gamification-based approach for consumer engagement is proposed and a methodology based on Fogg's behavior model and Kim's model on player types is developed to examine the suitability of available gamification applications for energy saving/efficiency in the context of a VPP. Seven gamification applications are analyzed and evaluated based on the developed methodology and the results are provided. © 2020 IEEE.

Abstract
The tendency to use renewable energies in DC microgrids (MGs) has been increased in the past decades. Due to the unpredictable behavior of renewable resources, it is vital to utilize energy storage resources in the MG structure. The generation sources and storages in DC MGs should be chosen in order to meet the maximum demand in both grid-connected and islanded mode. Also, penetration of power electronic based devices is essential to connect these resources to the network. The control of these devices are another challenge in this regard. So, a proper configuration along with an efficient control approach is needed for development of DC MGs. In this paper, a new structure for DC MG is presented which includes solar photovoltaic (PV) as generation sources and supercapacitor and battery as storages. Furthermore, an innovative control method based on voltage variations is introduced for the proposed structure. It is shown that simultaneous usage of battery and supercapacitor improves the performance of the MG in handling the abrupt load changes in the both grid-connected and islanded mode operations. To evaluate the performance of the proposed structure and control algorithm, different conditions are simulated in MATLAB/Simulink software and the results are presented. The results confirm a high degree of performance for proposed structure and control method.

Abstract
Capacity withholding of generation companies is an important issue in market monitoring procedures. The capacity withholding can be intensified in the transmission and generation constrained system. The strategic maintenance of market participants can impose multiple constraints on the system and changes the wholesale electricity market prices. The strategic maintenance of transmission and generation facilities is known as dynamic capacity withholding (DCW) and all of the market-monitoring units need algorithms to detect and reduce DCW. In this paper, a new dynamic capacity withholding index is presented. The method is analyzed on the IEEE 30, 57-bus test system. The numerical results show the effectiveness of the proposed index. © 2020 IEEE.

Abstract
Energy and social welfare management of smart buildings have been influenced by cooling systems. Although the combination of cooling systems in the smart grid has stimulated serious discussions over the last decade, its execution and control with more penetration of renewable energy have not been directly tackled. Hence, the present paper is designed to explore the suitability of implementing a novel controller for a cooling system in smart grid settings and high shares of renewable energies. The controller operates from a local control entity by responding to a set of inside nominated points and outside signals, such as access to renewable energy sources and customer welfare. Not only it reduces the purchasing power from the distribution grid with the help of optimization processes, but also minimizes the overall cost and size of the microgrid. Managing the cooling system simultaneously increases the reliability of the microgrid. As a result, the smart cooling system and renewable energy operate in unity, thus providing separate and mutual benefits for the whole system. The results presented in this study support that the proposed cooling system controller is capable of planning a microgrid system.

Abstract
One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users' anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles' owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs' batteries during the off-peak hours and drawing it from the EVs' batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs' charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.