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In this paper, the operation of electrical, heat and gas networks with flexible energy hubs is presented, where hubs is contributed in energy market that is based on market clearing price. This scheme maximizes the difference between energy hubs profit and operation cost of generation units subjected to operation model of sources, storages and responsible load in hub format, flexibility limits of hubs, and optimal power flow model of energy networks. This formulation is based on market clearing price model, so that it obtain value of energy price according to balancing between the generation and demand in different buses if energy networks. Penalty function method in this paper uses to calculate of primal variables and some dual variables (energy prices). In the following, the stochastic optimization uses to model the uncertainties of load and renewable power. Finally, the obtained numerical results show the capabilities of the proposed scheme in the improving flexibility and economic situations of hubs. Also, optimal operation of hubs is able to improve the economic and operation induces of energy networks in comparison to load flow study, and it improves the social welfare based on reducing of energy price. So that 100% flexibility condition obtains for Hubs. In this condition, the operation cost, energy loss, voltage drop and temperature drop are reduced about to 19.8%, 7.1%, 19.5% and 16.7%, respectively, with respect to load flow analysis, but the pressure drop increases about to 4.9%. [ABSTRACT FROM AUTHOR]

Power management in microgrids is a major challenge due to its low total inertia and capacity. The lower the microgrid generation capacity is, the higher the share of each generation unit in total power will be, and the higher the frequency deviation in less time will be when an outage occurs. So, preventive actions can be more reasonable and affordable than corrective actions for microgrid power and frequency control. In this regard, a new primary frequency response-constrained unit commitment model is presented here to prevent excessive frequency deviations by more commitment of higher inertia power plants and more contribution of renewable energy resources or energy storage systems' fast inertia response. To have a mixed-integer linear programming model, the primary frequency response constraints are linearized. The model is solved by the combination of two commercial solvers named MOSEK and YALMIP in the MATLAB 2018 environment. The proposed model is examined on a real isolated microgrid (an island). The results show that by activating the primary frequency support of distributed energy resources, the power can be managed with lower costs because there will be less need to start up fast (and expensive) gas turbine generation units. In addition, although comparing the model with others shows the more expensive management procedure, better frequency stability is obtained in contingencies. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT)-based energy management systems (EMSs) are considered a new technology in which consumers can manage their electricity payments according to their preferences, such as reducing costs or increasing satisfaction. Each consumer has its own program for communicating with a central control unit. In addition, the central control unit that is responsible for energy pricing can access consumer information and network performance status through the IoT infrastructure. Therefore, technical analysis can be performed using big data to determine the optimal price in order to make a compromise between the buyer and the goals of the distribution system operators. This paper presents a model to accurately assess the impact of pricing on the behavior of IoT-based energy systems. Then, according to the load specifications of each item and the technical limitations of the distribution network, the best time to use pricing is determined. The results show that the higher the price variance, the more discomfort the consumer and the lower the daily payment. Therefore, in this paper, the main goal of energy management is to minimize the total weight of the costs paid and their discomfort level. The paper could facilitate further penetration of IoT-based EMSs into smart grids. The study was performed on an IEEE standard 33-bus network. Optimization was implemented using YALMIP and MOSEK toolboxes. Therefore, it can be concluded that IoT technology allows consumers to enjoy the benefits of the network and makes optimal consumption management possible. [ABSTRACT FROM AUTHOR]

Network reconfiguration (NR) has undergone new changes to adapt to smart grid evolution. Internet of things (IoT) has found its way to smart grids especially in the distribution systems. Demand‐side management can be more efficient in the presence of IoT. IoT‐based energy management systems can have bidirectional communication with smart grids and decide according to their preferences, time‐of‐use (TOU) pricing, and their flexible devices. Therefore, customers would consume electricity more interactively. Consequently, the total load profile will be changed and a new NR approach must be adopted to meet load demands. So, the selection of critical switches must be modified to achieve the most effective network resource utilisation. For this purpose, a mixed‐integer linear programming model for feeder reconfiguration is presented considering the customers' behaviours in the IoT environment. At first, the customer manner is investigated at different prices and preferences. Then the total load profile is calculated considering the normal distribution of uncertain customer preference factors. Finally, the identification of critical switches is obtained using the proposed method. The optimisation is done in YALMIP and MOSEK toolboxes. Results show the effectiveness of the proposed method to efficient utilisation of IoT potentials and renewable energy benefits. [ABSTRACT FROM AUTHOR]