Articles written in Sadhana

    • Probabilistic planning for participation of virtual power plants in the presence of the thermal power plants in energy and reserve markets


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      Renewable energy-based on virtual power plants (VPPs) has recently attracted considerable attention for participating in energy and reserve markets due to the disadvantages of thermal power plants (TPPs). The present paper aims to maximize the VPP profitability in distribution networks including thermalpower plants, at minimum load cost, using a mathematical model for implementing the VPP and evaluating its role in the energy and reserve markets. The proposed model includes a series of probabilistic scenarios used to consider the uncertainty of wind/solar generation. Therefore in the first step, the lower bound of the problem, i.e., minimizing demand cost for all the units, should be calculated. It determines the status of VPP units based on the best-case scenarios. Afterward, the problem is cut to calculate the upper bound of the problem which is maximizing the profit of the VPP. The problem is evaluated in two cases: one is the presence of VPP only in the energy market and the other is the simultaneous presence of the VPP in the reserve and energy markets. Thecomputation ends with the convergence of lower and upper bounds of the problem. Since the proposed method uses a piece-wise model of thermal units and the problem has nonlinear equations, Mixed Integer Programming(MIP) used to calculate the contribution of units by utilizing GAMS software. Finally, the VPP profitability calculated for the day-ahead energy and reserve market after determining the method for the participation of power plants in supply at the minimum cost. The proposed method was then applied to a sample system consisting of three thermal plants, three wind farms, two solar farms, and two energy storage systems, considering several situations to examine the impact of the resources and also the resulting profitability in the energy and reserve market. The final step was the analysis of the results.

    • Risk-based performance of regional-market management considering information gap decision theory and demand response program


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      In the current study, the optimal performance of regional market management (RMM) is presented in one specific area of the market in order to determine the optimal demand aiming to minimize the costs of smoothing the load curve, and to implement the demand response program (DRP). In addition, the attempt hasbeen made in this study to minimize the involuntary lost energy in terms of the initial price uncertainty and the technical constraints including the price fluctuation limits, demand ceilings and the relative risk limits. Furthermore, a market-based tensile model is presented in the form of a combination of the formulations of over lapping generations (OLG) and price elasticity (PEM) for determining demand levels in DRP. The informationgap decision theory (IGDT) is presented to model the initial price uncertainty in the above issue, according todifferent rates of time preference as well as the algorithm of the co-evolutionary particle swarm optimization (CPSO). IGDT risk-aversion and risk-taking strategies help RMM to select the best strategy with the desired risk level for controlling the price uncertainty, improving the load curve, enhancing the reliability and reducing the costs.

    • Placement and optimal size of DG in the distribution network based on nodal pricing reduction with nonlinear load model using the IABC algorithm


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      The growing use of distributed generation (DG) at the distribution level has led to a change in the status of distribution networks from a passive network to an active network such as transmission systems. Therefore, transmission network pricing method such as nodal pricing could be used in the distribution network.DG connection to the distribution network affects bus nodal pricing. If the DG presence reduces losses and congestion in the distribution network, nodal pricing will also decrease. This paper presents a method for calculating the optimal size and place of DG in the distribution network based on nodal pricing. This planning is done to maximize the profits of distribution companies that have used DG in their network to meet several advantages. The simulation was performed using the improved artificial bee colony algorithm (IABC). In theIABC algorithm, by exchanging the received information between bees according to Newton and gravity laws, it uses all this algorithm capacity to find the ideal answer by considering the constraints applied to the system. In most DG placement articles, network loads are assumed to be constant. Because loads are often sensitive to voltage and frequency, constant load analysis leads to inaccurate results. Therefore, in this paper, the proposed method is implemented on a 38-bus radial distribution system with a model of real loads sensitive to the voltage and frequency of the system, including residential, commercial, and industrial loads.

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