Search

In an electricity pool with significant share of wind power, all generators including conventional and wind power units are generally scheduled in a day-ahead market based on wind power forecasts. Then, a real-time market is cleared given the updated wind power forecast and fixed day-ahead decisions to adjust power imbalances. This sequential market-clearing process may cope with serious operational challenges such as severe power shortage in real-time due to erroneous wind power forecasts in day-ahead market. To overcome such situations, several solutions can be considered such as adding flexible resources to the system. In this paper, we address another potential solution based on information sharing in which market players share their own wind power forecasts with others in day-ahead market. This solution may improve the functioning of sequential market-clearing process through making more informed day-ahead schedules, which reduces the need for balancing resources in real-time operation. This paper numerically evaluates the potential value of sharing forecasts for the whole system in terms of system cost reduction. Besides, its impact on each market player’s profit is analyzed. The framework of this study is based on a stochastic two-stage market setup and complementarity modeling, which allows us to gain further insights into information sharing impacts. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient solution approach based on Benders’ decomposition to solve a network-constrained ac unit commitment problem under uncertainty. The wind power production is the only source of uncertainty considered in this paper, which is modeled through a suitable set of scenarios. The proposed model is formulated as a two-stage stochastic programming problem, whose first-stage refers to the day-ahead market, and whose second-stage represents real-time operation. The proposed Benders’ approach allows decomposing the original problem, which is mixed-integer nonlinear and generally intractable, into a mixed-integer linear master problem and a set of nonlinear, but continuous subproblems, one per scenario. In addition, to temporally decompose the proposed ac unit commitment problem, a heuristic technique is used to relax the inter-temporal ramping constraints of the generating units. Numerical results from a case study based on the IEEE one-area reliability test system (RTS) demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

The smart grid technology enables an increasing level of responsiveness on the demand side, facilitating demand serving entities—large consumers and retailers—to procure their electricity needs under the best conditions. Such entities generally exhibit a proactive role in the pool, seeking to procure their energy needs at minimum cost. Within this framework, we propose a mathematical model to help large consumers to derive bidding strategies to alter pool prices to their own benefit. Representing the uncertainty involved, we develop a stochastic complementarity model to derive bidding curves, and show the advantages of such bidding scheme with respect to non-strategic ones. [ABSTRACT FROM PUBLISHER]

This paper proposes an approach for analyzing the impacts of large-scale wind power integration on electricity market equilibria. A pool-based oligopolistic electricity market is considered including a day-ahead market and a number of real-time markets. Wind power is considered within the generation portfolio of the strategic producers, and the uncertainty of wind power production is modeled through a set of plausible scenarios. The strategic behavior of each producer is modeled through a stochastic bilevel model. The resulting nonlinear equilibrium problem with equilibrium constraints (EPEC) is linearized and then solved. Numerical results for a test case with increasing levels of the wind power penetration is provided. [ABSTRACT FROM AUTHOR]

This paper analyzes numerically the approach reported in the companion paper for identifying generation investment equilibria in an electricity market where the producers behave strategically. To this end, a two-node illustrative example and a large-scale case study based on the IEEE reliability test system (RTS) are examined and the results obtained are reported and discussed. [ABSTRACT FROM PUBLISHER]

The first of this two-paper series proposes a methodology to characterize generation investment equilibria in a pool-based network-constrained electricity market, where the producers behave strategically. To this end, the investment problem of each strategic producer is represented using a bilevel model, whose upper-level problem determines the optimal investment and the supply offering curves to maximize its profit, and whose several lower-level problems represent different market clearing scenarios. This model is transformed into a mathematical program with equilibrium constraint (MPEC) through replacing the lower-level problems by their optimality conditions. The joint consideration of all producer MPECs, one per producer, constitutes an equilibrium problem with equilibrium constraints (EPEC). To identify the solutions of this EPEC, each MPEC problem is replaced by its Karush-Kuhn-Tucker (KKT) conditions, which are in turn linearized. The resulting mixed-integer linear system of equalities and inequalities allows determining the EPEC equilibria through an auxiliary MILP problem. [ABSTRACT FROM PUBLISHER]

Abstract: We describe a model to analyze the equilibrium encompassing an electricity futures market and a number of electricity spot markets sequentially arranged along the time horizon spanned by the futures market. Profit-maximizing strategic electricity producers react to both prices and rival production changes, in both the spot and the futures markets. At each time period, the total demand is considered to depend linearly on the spot price of the considered time period, and the futures market price is assumed to equal the average spot price over the time horizon. Equilibrium conditions at each spot market are described as a function of the futures market decision variables, which in turn allows describing the equilibrium in the futures market implicitly enforcing equilibrium in each spot market. The proposed model allows deriving analytical expressions that characterize such multi-market equilibrium and that can be recast as a mixed linear complementarity problem. This model is useful to gain insight on the outcomes and characteristics of the considered multi-market equilibrium. Such insight may allow the regulator to better design the futures and spot trading floors, their rules and sequential timing. It may also allow producers to increase the effectiveness of their respective offering strategies. [Copyright &y& Elsevier]

We address the generation investment problem faced by a strategic power producer and consider a detailed description of the uncertain parameters involved, namely, rival producer investment and market offering, and demand growth. To identify optimal investment decisions, we consider a target year and propose a bilevel model whose upper-level problem determines investment and offering decisions to maximize expected profit, and whose many lower-level problems represent market clearing conditions per demand block and scenario. Since the producer total expected profit is sufficiently convex with respect to investment decisions, a Benders' decomposition approach is proposed that results in a tractable formulation even if hundred of scenarios are used to describe uncertain parameters. Extensive numerical simulations based on realistic case studies show the good performance of the proposed decomposition approach. [ABSTRACT FROM PUBLISHER]

This paper provides a methodology to assist a strategic producer in making informed decisions on generation investment. A single target year is considered with demand variations modeled through blocks. The strategic behavior of the producer is represented through a bilevel model: the upper-level considers both investment decisions and strategic production actions and the lower-level corresponds to market clearing. Prices are obtained as dual variables of power balance equations. Rival uncertainties (on offering and investment) are characterized through scenarios. The resulting model is a large-scale mixed-integer LP problem solvable using currently available branch-and-cut techniques. Results pertaining to an illustrative example and a case study are reported and discussed. [ABSTRACT FROM PUBLISHER]

Abstract: This work addresses a relevant methodology for self-scheduling of a price-taker fuel and emission constrained power producer in day-ahead correlated energy, spinning reserve and fuel markets to achieve a trade-off between the expected profit and the risk versus different risk levels based on Markowitz’s seminal work in the area of portfolio selection. Here, a set of uncertainties including price forecasting errors and available fuel uncertainty are considered. The latter uncertainty arises because of uncertainties in being called for reserve deployment in the spinning reserve market and availability of power plant. To tackle the price forecasting errors, variances of energy, spinning reserve and fuel prices along with their covariances which are due to markets correlation are taken into account using relevant historical data. In order to tackle available fuel uncertainty, a framework for self-scheduling referred to as rolling window is proposed. This risk-constrained self-scheduling framework is therefore formulated and solved as a mixed-integer non-linear programming problem. Furthermore, numerical results for a case study are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Unlike markets for storable commodities, electricity markets depend on the real-time balance of supply and demand. Although much of the present-day grid operate effectively without storage technologies, cost-effective ways of storing electrical energy can make the grid more efficient and reliable. This work addresses an economic comparison between emerging and traditional Electric Energy Storage (EES) technologies in a competitive electricity market. In order to achieve this goal, an appropriate Self-Scheduling (SS) approach must first be developed for each of them to determine their maximum potential of expected profit among multi-markets such as energy and ancillary service markets. Then, these technologies are economically analyzed using Internal Rate of Return (IRR) index. Finally, the amounts of needed financial supports are determined for choosing the emerging technologies when an investor would like to invest on EES technologies. Among available EES technologies, we consider NaS battery (Natrium Sulfur battery) and pumped-storage plants as emerging and traditional technologies, respectively. [Copyright &y& Elsevier]

This work addresses a new framework for self-scheduling of an individual price-taker pumped-storage plant in a day-ahead (DA) market. The goal is achieving the best trade-off between the expected profit and the risks when the plant participates in DA energy, spinning reserve and regulation markets. In this paper, a set of uncertainties including price forecasting errors and also the uncertainty of power delivery requests in the ancillary service markets are contemplated. Considering these uncertainties, a new approach is proposed which is called dynamic self-scheduling (DSS). This risk-constrained dynamic self-scheduling problem is therefore formulated and solved as a mixed integer programming (MIP) problem. Numerical results for a case study are discussed. [Copyright &y& Elsevier]

This paper proposes an optimal power flow (OPF) model with flexible AC transmission system (FACTS) devices to minimize wind power spillage. The uncertain wind power production is modeled through a set of scenarios. Once the balancing market is cleared, and the final values of active power productions and consumptions are assigned, the proposed model is used by the system operator to determine optimal reactive power outputs of generating units, voltage magnitude and angles of buses, deployed reserves, and optimal setting of FACTS devices. This system operator tool is formulated as a two-stage stochastic programming model, whose first-stage describes decisions prior to uncertainty realization, and whose second-stage represents the operating conditions involving wind scenarios. Numerical results from a case study based on the IEEE RTS demonstrate the usefulness of the proposed tool. [ABSTRACT FROM PUBLISHER]