T. Barforoushi, M. P. Moghaddam, M. H. Javidi, M. K. Sheik-El-Eslami,
Volume 2, Issue 2 (April 2006)
Abstract
Medium-term modeling of electricity market has essential role in generation
expansion planning. On the other hand, uncertainties strongly affect modeling and
consequently, strategic analysis of generation firms in the medium term. Therefore, models
considering these uncertainties are highly required. Among uncertain variables considered
in the medium term generation planning, demand and hydro inflows are of the greatest
importance. This paper proposes a new approach for simulating the operation of power
market in medium-term, taking into account demand and hydro inflows uncertainties. The
demand uncertainty is considered using Monte-Carlo simulations. Standard Deviation over
Expected Profit (SDEP) of generation firms based on simulation results is introduced as a
new index for analyzing the influence of the demand uncertainty on the behavior of market
players. The correlation between capacity share of market players and their SDEP is also
demonstrated. The uncertainty of inflow as a stochastic variable is dealt using scenario tree
representation. Rational uncertainties as strategic behavior of generation firms, intending to
maximize their expected profit, is considered and Nash-Equilibrium is determined using the
Cournot model game. Market power mitigation effects through financial bilateral contracts
as well as demand elasticity are also investigated. Case studies confirm that this
representation of electricity market provides robust decisions and precise information about
electricity market for market players which can be used in the generation expansion
planning framework.
F. Misaghi, T. Barforoushi, M. Jafari-Nokandi,
Volume 13, Issue 2 (June 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.
T. Barforoushi, R. Heydari,
Volume 18, Issue 2 (June 2022)
Abstract
Curtailment of the production of wind resources due to uncertainty can affect the expansion of the transmission networks. The issue that needs to be addressed is how to expand the transmission network, which is accompanied by increasing wind energy utilization. In this paper, a new framework is proposed to solve the transmission expansion planning (TEP) problem in the presence of wind farms, considering wind curtailment cost. The proposed model is a risk-constrained stochastic bi-level problem that, the difference between the expected social welfare and investment cost is maximized at the upper level where optimal decisions on expansion plans are adopted by the independent system operator (ISO). To make the best use of wind generation resources, a new term called wind power curtailment cost is added to the upper level. Also, the risk index is included in expansion decisions. The market-clearing is considered at the lower level, aiming at maximizing social welfare. Uncertainties relating to wind power and the forecasted demand are modeled by sets of scenarios. Using duality theory, the proposed framework is modeled as mixed-integer linear programming (MILP) problem. The model is examined using the classical Garver’s six-bus test system and the IEEE 24-bus reliability test system (RTS). The results show that by considering the wind curtailment cost, the transmission network is expanded in a way that increases the wind energy utilization factor from 92.05% to 95.17%.
