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A MULTISTAGE EXPANSION PLANNING METHOD FOR OPTIMAL SUBSTATION PLACEMENT
S. Najafi Ravadanegh,
Volume 10, Issue 1 (3-2014)
Abstract
Optimal distribution substation placement is one of the major components of optimal distribution system planning projects. In this paper optimal substation placement problem is solved using Imperialist Competitive Algorithm (ICA) as a new developed heuristic optimization algorithm. This procedure gives the optimal size, site and installation time of medium voltage substation, using their related costs subject to operating and optimization constraints. A multistage and pseudo-dynamic expansion planning is applied to consider dynamic of the system parameters for example, load forecasting uncertainty, asset management and geographical constraints. In order to evaluate the effectiveness of the proposed method a sensitivity analysis of ICA parameters on obtained results is done. A graphical representation of obtained results is used to show the efficiency and capability of the proposed method both from the planning view and graphical aspects. The results show the efficiency and capability of the proposed method which has been tested on a real size distribution network.
Regulatory Impacts on Distributed Generation and Upstream Transmission Substation Expansion Planning: A Novel Stochastic Bi-level Model
F. Misaghi, T. Barforoushi, M. Jafari-Nokandi,
Volume 13, Issue 2 (6-2017)
Abstract

In this paper, a novel framework is proposed to study impacts of regulatory incentive on distributed generation (DG) investment in sub-transmission substations, as well as upgrading of upstream transmission substations. Both conventional and wind power technologies are considered here. Investment incentives are fuel cost, firm contracts, capacity payment and investment subsidy relating to wind power. The problem is modelled as a bi-level stochastic optimization problem, where the upper level consists of investor's decisions maximizing its own profit. Both market clearing and decision on upgrading of transmission substation aiming at minimizing the total cost are considered in the lower level. Due to non-convexity of the lower level and impossibility of converting to single level problem (i.e. mathematical programming with equilibrium constraints (MPEC)), an algorithm combing enumeration and mathematical optimization is used to tackle with the non-convexity. For each upgrading strategy of substations, a stochastic MPEC, converted to a mixed integer linear programming (MILP) is solved. The proposed model is examined on a six-bus and an actual network. Numerical studies confirm that the proposed model can be used for analysing investment behaviour of DGs and substation expansion.


Optimal Distributed Generation Planning in Radial Distribution Networks Considering Protection Coordination Limits
M. Esmaeilzadeh, I. Ahmadi, N. Ramezani,
Volume 14, Issue 2 (6-2018)
Abstract
Distributed generation (DG) has been widely used in distribution network to reduce the energy losses, improve voltage profile and system reliability, etc.  The location and capacity of DG units can influence on probability of protection mal-operation in distribution networks. In this paper, a novel model for DG planning is proposed to find the optimum DG location and sizing in radial distribution networks. The main purpose of the suggested model is to minimize the total cost including DG investment and operation costs. The operation costs include the cost of energy loss, the cost of protection coordination and also the mal-operation cost. The proposed DG planning model is implemented in MATLAB programming environment integrated with DIgSILENT software. The simulation results conducted on the standard 38-bus radial distribution network confirm the necessity of incorporating the protection coordination limits in the DG planning problem. Additionally, a sensitivity analysis has been carried out to illustrate the significance of considering these limits.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.