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.

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.

Microgrids with different technologies in distributed generations (DGs), different control facilities and power electronic interfaces require proper management and operation strategies. In these strategies, in order to reach the optimum scheduling, the stochastic nature of some decision variables should be considered. Subsequently, it will lead to a decrease in the forced load curtailment and an increase in the economic efficiency from the perspective of both the maingrid and the microgrid owner. In this study, the availability of dispatchable DGs, energy storage, renewable energy sources (RESs) and the maingrid as well as the power generation of RESs and load are studied through their uncertainty natures. For dealing with these uncertainties, stochastic variables computation module is designed which generates several scenarios by Monte Carlo simulation at each hour. The microgrid operation is optimised in uncertainty environment through a linear two‐stage stochastic model. The stochastic scheduling model which is solved by mixed‐integer linear programming is compared with a deterministic model through three different cases in presence of demand response on a sample microgrid. The results explicitly show benefits of the proposed stochastic model since it provides accuracy in scheduling and decreases the operation cost.

This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators' behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting and load recovery. A stochastic bilevel problem is formulated in which in the upper-level the ISO minimizes the total operation cost and in the lower-level the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints (MPEC) by replacing the lower-level program with its Karush-Kuhn-Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to running the program without considering the lower-level.