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An energy deviation settlement (EDS) mechanism has been proposed to settle the deviation between consumption and energy contracts in China's forward electricity market. Due to high EDS costs of electricity retailers, a deviation mutual insurance (DMI) mechanism is put forward, representing an option for electricity retailers to reduce the deviation through contract transfer. Given this background, an optimal alliance strategy is proposed to maximize the utility of different types of individual electricity retailers. The alliance income model considers the EDS cost reduction and the cooperation cost analyzed based on transaction cost theory (TCT). An improved income allocation method, which considers distribution indexes selected by game theory and resource dependence theory (RDT), is proposed to ensure the comprehensive and reasonable allocation of alliance income. Considering the expected income and financial risk, an evaluation model using conditional value at risk (CVaR) as the financial risk assessment indicator is presented to measure the stability of alliances. Simulations based on data from a provincial electricity market in China illustrate that the EDS costs of retailers can be reduced by selecting appropriate partners for alliance. In addition, the proposed alliance income allocation method is more reasonable and more targeted than the cooperative game-based methods. [ABSTRACT FROM AUTHOR]

Day-ahead energy market clearing relies on a deterministic equivalent model with a limited time horizon, which may lead to inefficient scheduling of generating units from the point of view of generators. For this reason, generators may wish to assume the risk of self-committing their units with the hope of securing greater profits. This phenomenon may reduce the room for economic signals in the day-ahead market. In this paper we investigate the influence of risk aversion and price volatility on the decision of generators to self-commit units. We present a stochastic programming model for self-committing combined cycle units under price uncertainty with a conditional value at risk criterion. We use Benders decomposition to solve the problem and present results on a case study to draw conclusions. [ABSTRACT FROM PUBLISHER]

This paper presents a stochastic framework for short-term operation of a distribution company (disco). The proposed framework optimizes disco's operational decisions in two hierarchical stages. The first stage, called day-ahead operation stage (DAOS), deals with the operational decisions on purchases from the day-ahead market and commitment of distributed generation (DG) units. The objective of this stage is to minimize the expected operating cost while the financial risk exposed by uncertain real-time prices and loads is restricted to a given level. The model associated with this stage is based on the mixed-integer programming (MIP) format. The second stage, named real-time operation stage (RTOS), deals with disco's activities in real-time. In RTOS, decisions are made on real-time market transactions, dispatch of online DGs, and invocation of load curtailments (LCs) such that the expected operating cost is minimized. This stage is formulated as a nonlinear programming (NLP) problem. To investigate the effectiveness of the developed framework, it is applied to a typical Finnish 20-kV urban distribution network. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach for the joint energy and reserves scheduling and unit commitment with n-K reliability constraints for the day-ahead market. The proposed method includes a novel n-K criterion where demand must be met with a specified probability under any simultaneous loss of K generating units. A chance-constrained method is proposed with an \alpha-quantile measure to determine the confidence level to meet the demand under K simultaneous contingencies. The chance-constrained optimization problem is recast as a mixed integer linear programming optimization problem. Wind and demand uncertainty are included into the model. The methodology proposed is illustrated with several case studies where the effect of increasing wind power penetration is analyzed showing the performance of our model. [ABSTRACT FROM PUBLISHER]

This paper proposes a decision-making framework, based on stochastic programming, for a retailer: 1) to determine the sale price of electricity to the customers based on time-of-use (TOU) rates, and 2) to manage a portfolio of different contracts in order to procure its demand and to hedge against risks, within a medium-term period. Supply sources include the pool, self-production facilities and several instruments such as forward contracts, call options, and interruptible contracts. The objective is to maximize the profit and simultaneously to minimize the risks in terms of a multi-period risk measure. Moreover, the risks are measured using conditional value at risk (CVaR) methodology. The reaction of the customers to the retailers' selling prices as well as the competition between the retailers is modeled through a market share function. The problem is formulated as a mixed-integer stochastic programming. It is solved by a decomposition technique, and the decomposed parts are solved by a branch-and-bound algorithm. [ABSTRACT FROM PUBLISHER]

Merchant transmission projects (MTPs) are primarily market solutions for importing inexpensive power when this energy is available. As a market driven solution, full recovery of the investment is not guaranteed. In general, the recovery is based on transmission service charges and the value of incremental financial transmission rights (FTRs) created by the project. Since FTRS are valuable only when the system is constrained, the FTR's value diminishes as the constraint is eliminated by the MTP. Hence, the MTP will be commercially viable only if its transmission capacity is fully subscribed. Otherwise, the MTP will likely not be built. This paper presents a two-step market-based transmission rate design for recovering an MTP investment. The first step is to develop a firm transmission service rate for using the MTP via an open subscription bid process. If the MTP capacity is not fully subscribed, the authors propose that the remaining investment be recovered via non-firm transmission services if and when cheap energy is available. Considering the random availability of economic energy, this second step identifies the range of transmission rates for developing possible rate discounts to encourage non-firm transmission usages to optimize the MTP investment recovery. [ABSTRACT FROM AUTHOR]

The deregulation of the electricity industry has caused for the generation company (Genco) the need of tools for measuring and managing the risk, beyond the classical problem of generating unit scheduling. In this paper, a probabilistic framework for the problem of managing risk faced by Gencos trading in day-ahead energy market is proposed. In particular, a stochastic forecast of electricity price and the technical features of hydrothermal units are considered. The approach is based on an optimization procedure for maximizing expected profits in the presence of risk constraints. Conditional value at risk for the distribution of daily profit is used as risk measure. The effectiveness of the proposed model is tested for the case of one producer of the Italian electricity system with a fleet of hydrothermal generating units. [ABSTRACT FROM AUTHOR]