Iranian Journal of Electrical and Electronic Engineering

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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.

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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.

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This paper proposes a method for determining the price bidding strategies of market participants consisting of Generation Companies (GENCOs) and Distribution Companies (DISCOs) in a day-ahead electricity market, while taking into consideration the load forecast uncertainty and demand response programs. The proposed algorithm tries to find a Pareto optimal point for a risk neutral participant in the market. Because of the complexity of the problem a stochastic method is used. In the proposed method, two approaches are used simultaneously. First approach is Fuzzy Genetic Algorithm for finding the best bidding strategies of market players, and another one is Mont-Carlo Method that models the uncertainty of load in price determining algorithm. It is demonstrated that with considering transmission flow constraints in the problem, load uncertainty can considerably influences the profits of companies and so using the second part of the proposed algorithm will be useful in such situation. It is also illustrated when there are no transmission flow constraints, the effect of load uncertainty can be modeled without using a stochastic model. The algorithm is finally tested on an 8 bus system.

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Strategic bidding in joint energy and spinning reserve markets is a challenging task from the viewpoint of generation companies (GenCos). In this paper, the interaction between energy and spinning reserve markets is modeled considering a joint probability density function for the prices of these markets. Considering pay-as-bid pricing mechanism, the bidding problem is formulated and solved as a classic optimization problem. The results show that the contribution of a GenCo in each market strongly depends on its production cost and its level of risk-aversion. Furthermore, if reserve bid acceptance is considered subjected to winning in the energy market, it can affect the strategic bidding behavior.

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The main goal of this paper is to present a new day-ahead energy acquisition model for a distribution company (Disco) in a competitive electricity market environment with Interruptible Load (IL). The work formulates the Disco energy acquisition model as a bi-level optimization problem with some of real issues, and then studies and designs a Genetic Algorithm (GA) of this optimization problem too. To achieve this goal, a novel two-step procedure is proposed. At the first step, a realistic model for an industrial interruptible load is introduced, and it is shown that Interruptible load model may affect the problem modeling and solving. At the second step, Disco energy acquisition program is formulated and solved with this realistic model. As a result, this paper shows energy acquisition programming model with ILs, by considering real assumptions. The introduced method shows a good performance of problem modeling and solving algorithm both in terms of solution quality and computational results. In addition, a case study is carried out considering a test system with some assumptions. Subsequently results show the general applicability of the proposed model with potential cost saving for the Disco

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This paper presents a novel approach for evaluating impacts of price-sensitive loads on electricity price and market power. To accomplish this aim an analytical method along with agent-based computational economics are used. At first, Nash equilibrium is achieved by computational approach of Q-learning then based on the optimal bidding strategies of GenCos, which are figured out by Q-learning, ISO's social welfare maximization is restated considering demand side bidding. In this research, it was demonstrated that Locational Marginal Price (LMP) at each node of system can be decomposed into five components. The first constitutive part is a constant value for the respective bus, while the next two components are related to GenCos and the last two parts are associated to Load Serving Entities (LSEs). Market regulators can acquire valuable information from the proposed LMP decomposition. First, sensitivity of electricity price at each bus and Lerner index of GenCos to the bidding strategies and maximum pricesensitive demand of LSEs are revealed through weighting coefficients of the last two terms in the decomposed LMP. Moreover, the decomposition of LMP expresses contribution of LSEs to the electricity price. The simulation results on two test systems confirm the capability of the proposed approach.

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Congestion and overloading for lines are the main problems in the exploitation of power grids. The consequences of these problems in deregulated systems can be mentioned as sudden jumps in prices in some parts of the power system, lead to an increase in market power and reduction of competition in it. FACTS devices are efficient, powerful and economical tools in controlling power flows through transmission lines that play a fundamental role in congestion management. However, after removing congestion, power systems due to targeting security restrictions may be managed with a lower voltage or transient stability rather than before removing. Thus, power system stability should be considered within the construction of congestion management. In this paper, a multi-objective structure is presented for congestion management that simultaneously optimizes goals such as total operating cost, voltage and transient security. In order to achieve the desired goals, locating and sizing of series FACTS devices are done with using components of nodal prices and the newly developed grey wolf optimizer (GWO) algorithm, respectively. In order to evaluate reliability of mentioned approaches, a simulation is done on the 39-bus New England network.

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