With the expansion of smart meters, like the Advanced Metering Infrastructure (AMI), and the Internet of Things (IoT), each smart city is equipped with various kinds of electronic devices. Therefore, equipment and technologies enable us to be smarter and make various aspects of smart cities more accessible and applicable. The goal of the current paper is to provide an inclusive review on the concept of the smart city besides their different applications, benefits, and advantages. In addition, most of the possible IoT technologies are introduced, and their capabilities to merge into and apply to the different parts of smart cities are discussed. The potential application of smart cities with respect to technology development in the future provides another valuable discussion in this paper. Meanwhile, some practical experiences all across the world and the key barriers to its implementation are thoroughly expressed.

Home energy management (HEM) systems enable residential consumers to participate in demand response programs (DRPs) more actively. However, HEM systems confront some practical difficulties due to the uncertainty related to renewable energies as well as the uncertainty of consumers' behavior. Moreover, the consumers aim for the highest level of comfort and satisfaction in operating their electrical appliances. In addition, technical limits of the appliances must be considered. Furthermore, DR providers aim at keeping the participation of consumers in DRPs and minimize the “response fatigue” phenomenon in the long-term period. In this paper, a stochastic model of an HEM system is proposed by considering uncertainties of electric vehicles availability and small-scale renewable energy generation. The model optimizes the customer's cost in different DRPs, while guarantees the inhabitants' satisfaction by introducing a response fatigue index. Different case studies indicate that the implementation of the proposed stochastic HEM system can considerably decrease both the customers' cost and response fatigue.

The optimized operation of building energy management system (BEMS) is of great significance to its operation security, economy and efficiency. This paper proposed a day-ahead multi-objective optimization model for a BEMS under time-of-use (TOU) price based demand response (DR), which integrates building integrated photovoltaic (BIPV) with other generations to optimize the economy and occupants’ comfort by the synergetic dispatch of source-load-storage. The occupants' comfort contains three aspects of the indoor environment: visual comfort, thermal comfort, and indoor air quality comfort. With the consideration of controllable load that could participate in DR programs, the balances among different energy styles, electric, thermal and cooling loads are guaranteed during the optimized operation. YALMIP toolbox in MATLAB was applied to solve the optimization problem. Finally, a case study was conducted to validate the effectiveness and adaptability of the proposed model.

With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Over the past few years, convolutional neural networks (CNNs) have been used to solve several complex deep learning challenges, making substantial progress in some fields and contributing to state of the art performances. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This article proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO). The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from datasets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.