T. Barforoushi, M. P. Moghaddam, M. H. Javidi, M. K. Sheik-El-Eslami,
Volume 2, Issue 2 (4-2006)
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.
M. Esmaili, H. A Shayanfar, N. Amjady,
Volume 6, Issue 1 (3-2010)
Abstract
Congestion management in electricity markets is traditionally done using deterministic values of power system parameters considering a fixed network configuration. In this paper, a stochastic programming framework is proposed for congestion management considering the power system uncertainties. The uncertainty sources that are modeled in the proposed stochastic framework consist of contingencies of generating units and branches as well as load forecast errors. The Forced Outage Rate of equipment and the normal distribution function to model load forecast errors are employed in the stochastic programming. Using the roulette wheel mechanism and Monte-Carlo analysis, possible scenarios of power system operating states are generated and a probability is assigned to each scenario. Scenario reduction is adopted as a tradeoff between computation time and solution accuracy. After scenario reduction, stochastic congestion management solution is extracted by aggregation of solutions obtained from feasible scenarios. Congestion management using the proposed stochastic framework provides a more realistic solution compared with the deterministic solution by a reasonable uncertainty cost. Results of testing the proposed stochastic congestion management on the 24-bus reliability test system indicate the efficiency of the proposed framework.
