A.J. Conejo

The Ohio State University, Columbus, Ohio, United States

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Publications (220)347.74 Total impact

  • Source
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    ABSTRACT: Distribution microgrids are being challenged by reverse power flows and voltage fluctuations due to renewable generation, demand response, and electric vehicles. Advances in photovoltaic (PV) inverters offer new opportunities for reactive power management provided PV owners have the right investment incentives. In this context, reactive power compensation is considered here as an ancillary service. Accounting for the increasing time-variability of distributed generation and demand, a stochastic reactive power compensation scheme is developed. Given uncertain active power injections, an online reactive control scheme is devised. Such scheme is distribution-free and relies solely on power injection data. Reactive injections are updated using the Lagrange multipliers of the second-order cone program obtained via a convex relaxation. Numerical tests on an industrial 47-bus test feeder corroborate the reactive power management efficiency of the novel stochastic scheme over its deterministic alternative as well as its capability to track variations in solar generation.
    09/2014;
  • S.Jalal Kazempour, Antonio J. Conejo, Carlos Ruiz
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    ABSTRACT: 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.
    IEEE Transactions on Power Systems 06/2014; · 2.92 Impact Factor
  • C. Battistelli, A.J. Conejo
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    ABSTRACT: This paper presents an analysis and systemization of automatic generation control (AGC) in distribution networks (DNs) with high penetration of distributed resources, including electric vehicles (EVs). A methodology is developed that allows designing the AGC service at the distribution level, and an optimization model is proposed to assess the potential of AGC provision from EVs according to an objective of optimal economic management. A realistic case study is considered to analyze the proposed approach, and to illustrate both the potential of the methodology and the effectiveness of the optimization model. Results show that the proposed methodology represents a flexible tool that any system operator could use for the operational planning and the management of ancillary services such as AGC with EVs.
    Electric Power Systems Research 06/2014; 111:22–31. · 1.69 Impact Factor
  • L. Baringo, A.J. Conejo
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    ABSTRACT: This paper considers the problem of identifying the optimal investment of a strategic wind power investor that participates in both the day-ahead (DA) and the balancing markets. This investor owns a number of wind power units that jointly with the newly built ones allow it to have a dominant position and to exercise market power in the DA market, behaving as a deviator in the balancing market in which the investor buys/sells its production deviations. The model is formulated as a stochastic complementarity model that can be recast as a mixed-integer linear programming (MILP) model. A static approach is proposed focusing on a future target year, whose uncertainties pertaining to demands, wind power productions, and balancing market prices are precisely described. The proposed model is illustrated using a simple example and two case studies.
    IEEE Transactions on Power Systems 05/2014; 29(3):1250-1260. · 2.92 Impact Factor
  • R. Domínguez, A.J. Conejo, M. Carrión
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    ABSTRACT: This paper analyzes the operation of a fully renewable electric energy system from the viewpoint of the system operator. The generation system is dominated by concentrating solar power plants (CSP) with storage, and includes wind and biomass power plants and pumped-storage facilities. The transmission network is represented using a dc approximation, the demand is considered elastic and the uncertainty of renewable production is modeled via scenarios. To carry out the analysis, we use a two-stage stochastic programming model that represents the day-ahead market (first stage) and the actual operation of the system (second stage). This model is recast as a mixed-integer linear programming problem solvable using branch-and-cut techniques. The proposed model is applied to a realistic case study based on the IEEE 118-node system and solar/wind data from Texas, US. The impact on operation and operation cost of the system flexibility and of the operation and maintenance costs of renewable energies are analyzed. Finally, we study the operation of the system throughout the four seasons of the year.
    Applied Energy 04/2014; 119:417–430. · 5.26 Impact Factor
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    ABSTRACT: 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.
    IEEE Transactions on Power Systems 01/2014; · 2.92 Impact Factor
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    ABSTRACT: This paper proposes a decentralized methodology to optimally schedule generating units while simultaneously determining the geographical allocation of the required reserve. We consider an interconnected multi-area power system with cross-border trading in the presence of wind power uncertainty. The multi-area market-clearing model is represented as a two-stage stochastic programming model. The proposed decentralized procedure relies on an augmented Lagrangian algorithm that requires no central operator intervention but just moderate interchanges of information among neighboring regions. The methodology proposed is illustrated using an example and a realistic case study.
    IEEE Transactions on Power Systems 01/2014; 29(4):1701-1710. · 2.92 Impact Factor
  • M. Rahimiyan, L. Baringo, A.J. Conejo
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    ABSTRACT: We consider a cluster of interconnected price-responsive demands (e.g., an industrial compound or a university campus) that can be supplied through the main grid and a stochastic distributed energy resource (DER), e.g., a wind plant. Additionally, the cluster of demands owns an energy storage facility. An energy management system (EMS) coordinates the price-responsive demands within the cluster and provides the interface for energy trading between the demands and the suppliers, main grid and DER. The DER and the cluster of demands have a contractual agreement based on a take-or-pay contract. Within this context, we propose an energy management algorithm that allows the cluster of demands to buy, store, and sell energy at suitable times. This algorithm results in maximum utility for the demands. The uncertainty related to both the production level of the DER and the price of the energy obtained from/sold to the main grid is modeled using robust optimization (RO) techniques. Smart grid (SG) technology is used to realize 2-way communication between the EMS and the main grid, and between the EMS and the DER. Communication takes place on an hourly basis. A realistic case study is used to demonstrate the advantages of both the coordination provided by the EMS through the proposed algorithm and the use of SG technology.
    IEEE Transactions on Power Systems 01/2014; 29(2):645-655. · 2.92 Impact Factor
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    ABSTRACT: The cross-border trading among electricity markets in an interconnected multi-area system (e.g., in central Europe) and the integration of renewable resources (e.g., wind energy) have remarkably increased in recent years. To efficiently operate such system, a proper coordination among different areas is required. Within this context, a decentralized algorithm for market clearing is proposed in this paper to dispatch simultaneously energy and reserve under wind generation uncertainty and equipment failures. The proposed technique does not require a central operator but just a moderate interchange of information among neighboring areas. Additionally, the benefit of cross-border trading is studied.
    IEEE Transactions on Power Systems 11/2013; 28(4):4373-4383. · 2.92 Impact Factor
  • E Caro, R M'inguez, A ~J Conejo
    Electric Power Systems Research 11/2013; 104:9-17. · 1.69 Impact Factor
  • Hrvoje Pandzic, A.J. Conejo, Igor Kuzle
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    ABSTRACT: This paper considers the yearly maintenance scheduling of generating units within a market environment. Each producer schedules its units' maintenance periods to maximize its revenue using a bilevel approach. The upper-level problem of this bilevel model seeks maximum revenue and contains unit scheduling constraints, while the lower-level problems represent the market clearing process under different operating conditions. This single producer maintenance problem can be recast as a mathematical program with equilibrium constraints (MPEC). Since the MPECs of all producers have to be considered simultaneously and the market clearing process is common to all of them, the proposed formulation for maintenance scheduling is an equilibrium problem with equilibrium constraints (EPEC) corresponding to a multiple-leader-common-follower game. The solution of this EPEC is a set of equilibria, in which none of the producers is able to increase its revenue unilaterally by changing the maintenance periods of its generating units.
    IEEE Transactions on Power Systems 06/2013; 28(2):922-930. · 2.92 Impact Factor
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    ABSTRACT: This paper presents an approach to solving discretely constrained, mixed linear complementarity problems (DC-MLCPs). Such formulations include a variety of interesting and realistic models of which two are highlighted: a market-clearing auction typical in electric power markets but suitable in other more general contexts, and a network equilibrium suitable to energy markets as well as other grid-based industries. A mixed-integer, linear program is used to solve the DC-MLCP in which both complementarity as well as integrality are allowed to be relaxed. Theoretical and numerical results are provided to validate the approach.
    Computers & Operations Research 05/2013; 40(5):1339–1350. · 1.91 Impact Factor
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    ABSTRACT: A virtual power plant aggregates various local production/consumption units that act in the market as a single entity. This paper considers a virtual power plant consisting of an intermittent source, a storage facility, and a dispatchable power plant. The virtual power plant sells and purchases electricity in both the day-ahead and the balancing markets seeking to maximize its expected profit. Such model is mathematically rigorous, yet computationally efficient. The offering problem is cast as a two-stage stochastic mixed-integer linear programming model which maximizes the virtual power plant expected profit. The uncertain parameters, including the power output of the intermittent source and the market prices, are modeled via scenarios based upon historical data. The proposed model is applied to a realistic case study and conclusions are drawn.
    Applied Energy 05/2013; 105(5):282-292. · 5.26 Impact Factor
  • S.Jalal Kazempour, Antonio J. Conejo, Carlos Ruiz
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    ABSTRACT: 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.
    IEEE Transactions on Power Systems 01/2013; 28(3):2623-2631. · 2.92 Impact Factor
  • S.Jalal Kazempour, Antonio J. Conejo, Carlos Ruiz
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    ABSTRACT: 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.
    IEEE Transactions on Power Systems 01/2013; 28(3):2613- 2622. · 2.92 Impact Factor
  • L. Baringo, A.J. Conejo
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    ABSTRACT: As a result of subsidies and technological maturity, renewable electricity producers have grown in some jurisdictions to clearly dominant positions in the market. Under this context, we propose an offering strategy for a wind power producer with market power that participates in the day-ahead market as a price-maker, and in the balancing market as a deviator. Uncertainty pertaining to wind power production and balancing market price is represented through a set of correlated scenarios. The proposed model is a stochastic mathematical program with equilibrium constraints (MPEC) that can be recast as a tractable mixed-integer linear programming (MILP) problem, which is solvable using available optimization software. Results from an illustrative example and two case studies show the effectiveness of the proposed model.
    IEEE Transactions on Power Systems 01/2013; 28(4):4645-4654. · 2.92 Impact Factor
  • Fernando S. Oliveira, Carlos Ruiz, Antonio J. Conejo
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    ABSTRACT: In this article we propose a model of the supply chain in electricity markets with multiple generators and retailers and considering several market structures. We analyze how market design interacts with the different types of contract and market structure to affect the coordination between the different firms and the performance of the supply chain as a whole. We compare the implications on supply chain coordination and on the players’ profitability of two different market structures: a pool based market vs. bilateral contracts, taking into consideration the relationship between futures and spot markets. Furthermore, we analyze the use of contracts for differences and two-part-tariffs as tools for supply chain coordination. We have concluded that there are multiple equilibria in the supply chain contracts and structure and that the two-part tariff is the best contract to reduce double marginalization and increase efficiency in the management of the supply chain.
    European Journal of Operational Research 01/2013; · 2.04 Impact Factor
  • L. Baringo, A.J. Conejo
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    ABSTRACT: Stochastic programming constitutes a useful tool to address investment problems. This technique represents uncertain input data using a set of scenarios, which should accurately describe the involved uncertainty. In this paper, we propose two alternative methodologies to efficiently generate electric load and wind-power production scenarios, which are used as input data for investment problems. The two proposed methodologies are based on the load- and wind-duration curves and on the K-means clustering technique, and allow representing the uncertainty of and the correlation between electric load and wind-power production. A case study pertaining to wind-power investment is used to show the interest of the proposed methodologies and to illustrate how the selection of scenarios has a significant impact on investment decisions.
    Applied Energy 01/2013; 101:475–482. · 5.26 Impact Factor
  • C. Ruiz, A.J. Conejo, D.J. Bertsimas
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    ABSTRACT: We consider a strategic producer that trades its energy in a multi-period network-constrained electricity pool and, for strategic reasons, is interested in identifying its rival producers' offer prices. Considering industry practice, we assume that the strategic producer has knowledge of the daily market outcomes, i.e., energy quantities sold/bought and resulting locational marginal prices (LMPs) for each time period and all nodes of the network. Using this information we formulate an inverse optimization problem that allows estimating the rival producers' offer prices that have been marginal at any of the time periods under study. Such problem is well behaved, effectively identifies rival offer prices and can be efficiently solved. The effectiveness of the proposed technique is illustrated through a simple example and a realistic case study.
    IEEE Transactions on Power Systems 01/2013; 28(3):3056-3064. · 2.92 Impact Factor
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    ABSTRACT: This paper shows how Benders decomposition can be used for estimating the parameters of a fatigue model. The objective function of such model depends on five parameters of different nature. This makes the parameter estimation problem of the fatigue model suitable for the Benders decomposition, which allows us to use well-behaved and robust parameter estimation methods for the different subproblems. To build the Benders cuts, explicit formulas for the sensitivities (partial derivatives) are obtained. This permits building the classical iterative method, in which upper and lower bounds of the optimal value of the objective function are obtained until convergence. Two alternative objective functions to be optimized are the likelihood and the sum of squares error functions, which relate to the maximum likelihood and the minimum error principles, respectively. The method is illustrated by its application to a real-world problem.
    Annals of Operations Research 01/2013; · 1.03 Impact Factor

Publication Stats

5k Citations
347.74 Total Impact Points

Institutions

  • 2014
    • The Ohio State University
      Columbus, Ohio, United States
  • 1998–2013
    • University of Castilla-La Mancha
      • Department of Chemical Engineering
      Ciudad Real, Castille-La Mancha, Spain
  • 1997–2009
    • University of Waterloo
      • Department of Electrical & Computer Engineering
      Waterloo, Quebec, Canada
  • 2008
    • Hospital General Universitario de Ciudad Real
      Ciudad Real, Castille-La Mancha, Spain
  • 2001–2008
    • McGill University
      • Department of Electrical & Computer Engineering
      Montréal, Quebec, Canada
  • 1994–2008
    • University of Malaga
      • Department of Electric Engineering
      Málaga, Andalusia, Spain
    • Universidad Pontificia Comillas
      Madrid, Madrid, Spain
  • 2007
    • University of Zaragoza
      Caesaraugusta, Aragon, Spain