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In this paper, correlation between uncertainties related to loads is addressed in transmission network expansion planning (TNEP). The TNEP problem is formulated in a multi-objective form with objective functions of minimum investment cost, minimum congestion cost, and minimum risk cost. Load correlation is modeled using unscented transformation method and compared to a simulation method. Initially, the impact of load correlation on congestion cost and risk cost is shown. Then, using the Pareto solutions of this multi-objective TNEP problem, impact of load correlation on the TNEP solutions is illustrated. The Pareto solutions are obtained using the Non-dominated Sorting Genetic Algorithm II (NSGA II) that is capable in handling non-convex and non-linear problems with mixed integer structures. Test results are provided for the IEEE 24-bus Reliability Test System (RTS) and a representation of the actual Iranian 400 kV transmission network. [ABSTRACT FROM AUTHOR]

The term smart grid refers to a modernization of the electrical network consisting in the integration of various technologies such as dispersed generation, dispatchable loads, communication systems and storage devices which operates in grid-connected and islanded modes. As a result, traditional optimization techniques in new power systems have been seriously influenced during the last decade. One of the most important technical and economical tools in this regard is the Optimal Power Flow (OPF). As a fundamental optimization tool in the operation and planning fields, OPF has an undeniable role in the power system. This paper reviews and compares the OPF approaches mainly related to smart distribution grids. In this work, the main OPF approaches are compared in terms of their objective functions, constraints, and methodologies. Furthermore, computational performances, case study networks and the publication date of these methods are reported. Finally, some basic challenges arising from the new OPF methodologies in smart grids are addressed. [ABSTRACT FROM AUTHOR]

• Presenting a complete model of an EH contains RES to investigate the OPGF problem. • Modeling the operation limits of CHP, and ramp rate of generators. • Considering demand response programs for both electrical and thermal loads. • Analyzing several EHs, allocated in various buses to minimize the operation costs. • Applying the GAMS software to modified integrated power and gas IEEE14-bus system. Energy hubs (EHs) can help connect different types of energy and create a multi-carrier energy network (MCEN), thereby promoting energy network operation flexibility. Due to the connection of different types of energy in these networks, reduction of operating costs has become a controversial challenge. Herein, the optimal power and gas flow (OPGF) problem in an MCEN was investigated in the presence of wind and solar power sources, electrical and thermal energy storage (EES and TES), and electrical and thermal demand response programs (EDRP and TDRP). In the mentioned EHs, the operating costs of electric and gas networks were minimized, and the operation of EHs was simultaneously investigated. All constraints of the electric and gas networks and different equipment, including combined heat and power (CHP) units, were also considered. The simulation results in GAMS showed that the simultaneous optimization of the operation cost of the case study network, including 14 electric buses and 9 gas buses, during a 24-hour period was reduced up to 4%. Also, due to the lower price of gas, its consumption has increased by about 5%, and this ultimately decreases the total cost of optimal energy distribution of the network compared to the case in which the mentioned strategies and programs were not implemented. [ABSTRACT FROM AUTHOR]

Renewable generation and distributed resources are becoming increasingly relevant due to their various advantages and intense regulatory support. Therefore, there is a need to consider the effects of these new sources in the transmission network. In addition, the accurate assessment of losses is necessary in order to evaluate the impact of distributed generation. In this paper, we present a single-objective optimization method that is applied to different case studies that include an AC and a DC power flow in order to consider losses accurate and efficiently. The proposed algorithm considers uncertainty in wind generation and demand, as well as the costs for investment, repair, maintenance, and losses. This problem is solved using an Imperialist Competitive Algorithm (ICA), a meta-heuristic method that has been proposed recently and has shown promising results compared to the other well-established evolutionary methods such as Genetic Algorithm (GA). The proposed method is investigated on the IEEE 24-bus and IEEE 118-bus test systems. The results are compared to the results reported in the literature. Our results confirm that an accurate evaluation of losses using an ACPF does modify the optimal plan and hence it is important to include an ACPF when performing Transmission Expansion Planning (TEP). In addition, the implemented ICA displays a more efficient performance than a plan GA in the same case study. [ABSTRACT FROM AUTHOR]