1,150 research outputs found

    Distributed Stochastic Market Clearing with High-Penetration Wind Power

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    Integrating renewable energy into the modern power grid requires risk-cognizant dispatch of resources to account for the stochastic availability of renewables. Toward this goal, day-ahead stochastic market clearing with high-penetration wind energy is pursued in this paper based on the DC optimal power flow (OPF). The objective is to minimize the social cost which consists of conventional generation costs, end-user disutility, as well as a risk measure of the system re-dispatching cost. Capitalizing on the conditional value-at-risk (CVaR), the novel model is able to mitigate the potentially high risk of the recourse actions to compensate wind forecast errors. The resulting convex optimization task is tackled via a distribution-free sample average based approximation to bypass the prohibitively complex high-dimensional integration. Furthermore, to cope with possibly large-scale dispatchable loads, a fast distributed solver is developed with guaranteed convergence using the alternating direction method of multipliers (ADMM). Numerical results tested on a modified benchmark system are reported to corroborate the merits of the novel framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9 figure

    Factoring flexible demand non-convexities in electricity markets

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    An exact solution method for binary equilibrium problems with compensation and the power market uplift problem

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    We propose a novel method to find Nash equilibria in games with binary decision variables by including compensation payments and incentive-compatibility constraints from non-cooperative game theory directly into an optimization framework in lieu of using first order conditions of a linearization, or relaxation of integrality conditions. The reformulation offers a new approach to obtain and interpret dual variables to binary constraints using the benefit or loss from deviation rather than marginal relaxations. The method endogenizes the trade-off between overall (societal) efficiency and compensation payments necessary to align incentives of individual players. We provide existence results and conditions under which this problem can be solved as a mixed-binary linear program. We apply the solution approach to a stylized nodal power-market equilibrium problem with binary on-off decisions. This illustrative example shows that our approach yields an exact solution to the binary Nash game with compensation. We compare different implementations of actual market rules within our model, in particular constraints ensuring non-negative profits (no-loss rule) and restrictions on the compensation payments to non-dispatched generators. We discuss the resulting equilibria in terms of overall welfare, efficiency, and allocational equity

    Investment incentives and auction design in electricity markets

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    Motivated by the regulatory debate in electricity markets, we seek to understand how market design affects market performance through its impact on investment incentives. For this purpose, we study a two-stage game in which firms choose their capacities under demand uncertainty prior to bidding into the spot market. We analyse a number of different market design elements, including (i) two commonly used auction formats, the uniform-price and discriminatory auctions, (ii) price-caps and (iii) bid duration. We find that, although the discriminatory auction tends to lower prices, this does not imply that investment incentives at the margin are poorer; indeed, under reasonable assumptions on the shape of the demand distribution, the discriminatory auction induces (weakly) stronger investment incentives than the uniform-price format
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