In this paper, the problem of optimal placement of 5 virtual inertia is considered as a techno-economic problem from a 6 frequency stability point of view. First, a data driven-based equiv-7 alent model of battery energy storage systems, as seen from the 8 electrical system, is proposed. This experimentally validated model 9 takes advantage of the energy storage system special attributes to 10 contribute to inertial response enhancement, via the virtual inertia 11 concept. Then, a new framework is proposed, which considers the 12 battery storage system features, including annual costs, lifetime and 13 state of charge, into the optimal placement formulation to enhance 14 frequency response with a minimum storage capacity. Two well-15 known dynamical frequency criteria, the frequency nadir and the 16 rate of change of frequency, are utilized in the optimization formu-17 lation to determine minimum energy storage systems. Moreover, a 18 power angle-based stability index is also used to assess the effect 19 of virtual inertia on transient stability. Sensitivity and uncertainty 20 analyses are further conducted to assess the applicability of the 21 method. The efficiency of the proposed framework is demonstrated 22 on a linearized model of a three-area power system as well as two 23 nonlinear systems. Simulation results suggest that the proposed 24 method gives improved results in terms of stability measures and 25 less ESS capacity, when compared with other methods proposed in 26 the literature. Q1 27 Index Terms-Optimal placement, frequency nadir, virtual 28 inertia, energy storage systems, inertial response, rate of change 29 of frequency, transient stability, uncertainty analysis, sensitivity 30

Renewable-energy-based microgrids (MGs) are being advocated around the world in response to increasing energy demand, high levels of greenhouse gas (GHG) emissions, energy losses, and the depletion of conventional energy resources. However, the high investment cost of the MGs besides the low selling price of the energy to the main grid are two main challenges to realize the MGs in developing countries such as Iran. For this reason, the government should define some incentive policies to attract investor attention to MGs. This paper aims to develop a framework for the optimal planning of a renewable energy-based MG considering the incentive policies. To investigate the effect of the incentive policies on the planning formulation, three different policies are introduced in a pilot system in Iran. The minimum penetration rates of the RESs in the MG to receive the government incentive are defined as 20% and 40% in two different scenarios. The results show that the proposed incentive policies reduce the MG’s total net present cost (NPC) and the amount of carbon dioxide (CO2) emissions. The maximum NPC and CO2 reduction in comparison with the base case (with incentive policies) are 22.87% and 56.13%, respectively. The simulations are conducted using the hybrid optimization model for electric renewables (HOMER) software.

Reduced rotational inertia in power grids due to increasing penetration levels of distributed generations (DGs) may lead to degraded performance of the traditional frequency control scheme. In response to this challenge, inertia adequacy has recently emerged as a new research area in modern power systems. Accordingly, an attempt is made in this paper to tackle inertia adequacy in the generation expansion planning (GEP) problem. This in turn helps to realize sustainable economic development. For this purpose, the inertia adequacy constraint is tied to the frequency stability metrics. In other words, minimum permissible inertia that satisfies frequency dynamic standards is defined as an inertia adequacy constraint in the GEP formulation. Further, an experimentally validated model of a cluster of DGs is employed to realize a penetrated power grid to deal with sustainable planning development. Finally, an iterative-based algorithm is proposed to expand the system with minimum cost and with the highest permissible penetration level of DGs. The effectiveness of the proposed algorithm is examined in the Garver test system.

This paper presents a novel mathematical model to simultaneously tackle the economic dispatch (ED) problem considering valve point effect, load uncertainty, distributed generation (DG) uncertainty, incentive-based demand response, and plug-in electric vehicle into the transmission expansion planning (TEP) problem to minimize the total cost of the system. Monte-Carlo is employed to consider the uncertain characteristic of DGs and loads. Considering ED problem in solving TEP problem with uncertain aspects of DGs and loads, made the problem so complicated. So, to overcome this complicity, a new meta-heuristic coronavirus herd immunity optimizer (CHIO) algorithm is utilized. The presented methodology is verified on an IEEE 24-bus test system. Finally, to evaluate the CHIO algorithm efficiency, a comparison is made between the results obtained by CHIO and Branch and Bound (B&B) algorithm. Numerical results show the efficiency of the newly presented methodology in solving TEP and ED problems simultaneously.

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