A.J. Conejo

The Ohio State University, Columbus, Ohio, United States

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Publications (242)503.19 Total impact

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    ABSTRACT: 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 non-linear and generally intractable, into a mixed-integer linear master problem and a set of non-linear, 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.
    IEEE Transactions on Power Systems 05/2015; DOI:10.1109/TPWRS.2015.2409198 · 3.53 Impact Factor
  • C. Ruiz, A.J. Conejo
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    ABSTRACT: The work reported in this paper addresses the problem of transmission expansion planning under uncertainty in an electric energy system. We consider different sources of uncertainty, including future demand growth and the availability of generation facilities, which are characterized for different regions within the electric energy system. An adaptive robust optimization model is used to derive the investment decisions that minimizes the system’s total costs by anticipating the worst case realization of the uncertain parameters within an uncertainty set. The proposed formulation materializes on a mixed-integer three-level optimization problem whose lower-level problem can be replaced by its KKT optimality conditions. The resulting mixed-integer bilevel model is efficiently solved by decomposition using a cutting plane algorithm. A realistic case study is used to illustrate the working of the proposed technique, and to analyze the relationship between the optimal transmission investment plans, the investment budget and the level of supply security at the different regions of the network.
    European Journal of Operational Research 04/2015; 242(2). DOI:10.1016/j.ejor.2014.10.030 · 1.84 Impact Factor
  • 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 04/2015; 30(2):848-856. DOI:10.1109/TPWRS.2014.2332540 · 3.53 Impact Factor
  • Ricardo M. Lima, Augusto Q. Novais, Antonio J. Conejo
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    ABSTRACT: This paper addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. To solve this problem, two variants of a constraint generation algorithm are proposed, and their application and characteristics discussed. Both algorithms are used to solve two case studies based on two producers, each operating equivalent generation units, differing only in the thermal units’ characteristics. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. These findings are presented and analyzed in detail, and an attempted rationale is proposed to explain the less intuitive outcomes. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority.
    European Journal of Operational Research 01/2015; 240(2):457–475. DOI:10.1016/j.ejor.2014.07.013 · 1.84 Impact Factor
  • Luis Baringo, Antonio J. Conejo
    IEEE Transactions on Power Systems 01/2015; DOI:10.1109/TPWRS.2015.2411332 · 3.53 Impact Factor
  • Ruth Dominguez, Antonio J. Conejo, Miguel Carrion
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    ABSTRACT: Renewable energy sources are here to stay for a number of important reasons, including global warming and the depletion of fossil fuels. We explore in this paper how a thermal-dominated electric energy system can be transformed into a renewable-dominated one. This study relies on a stochastic programming model that allows representing the uncertain parameters plaguing such long-term planning exercise. Being the final year of our analysis 2050, we represent the transition from today to 2050 by allowing investment in both production and transmission facilities, with the target of achieving a renewable-dominated minimum-cost system. The methodology developed is illustrated using a realistic large-scale case study. Finally, policy conclusions are drawn.
    IEEE Transactions on Power Systems 01/2015; 30(1):316-326. DOI:10.1109/TPWRS.2014.2322909 · 3.53 Impact Factor
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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.
    IEEE Transactions on Power Systems 09/2014; DOI:10.1109/TPWRS.2014.2369452 · 3.53 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 09/2014; 29(5):2150-2159. DOI:10.1109/TPWRS.2014.2299533 · 3.53 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 07/2014; 29(4):1701-1710. DOI:10.1109/TPWRS.2013.2293542 · 3.53 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. DOI:10.1016/j.epsr.2014.01.008 · 1.60 Impact Factor
  • Luis 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. DOI:10.1109/TPWRS.2013.2292859 · 3.53 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. DOI:10.1016/j.apenergy.2014.01.014 · 5.26 Impact Factor
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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 03/2014; 29(2):645-655. DOI:10.1109/TPWRS.2013.2288316 · 3.53 Impact Factor
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    ABSTRACT: New generation algorithms focus on hybrid miscellaneous of exact and heuristic methods. Combining meta-heuristics and exact methods based on mathematical models appears to be a very promising alternative in solving many combinatorial optimization problems. ...
    Computers & Operations Research 01/2014; 41:221-222. DOI:10.1016/j.cor.2013.08.009 · 1.72 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. DOI:10.1109/TPWRS.2013.2265854 · 3.53 Impact Factor
  • Luis 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 11/2013; 28(4):4645-4654. DOI:10.1109/TPWRS.2013.2273276 · 3.53 Impact Factor
  • Eduardo Caro, R M'inguez, Antonio J. Conejo
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    ABSTRACT: The state estimator is a key tool in the operation of any real-world electric energy system. In this paper, a state estimator based on a weighted least squares model is proposed which is robust against outliers. This algorithm presents two relevant features: robustness that is achieved by readjusting measurement weights, and accuracy that is attained by considering measurement dependencies. The proposed method is tested in the IEEE 57-bus and 118-bus systems and the obtained results are analyzed using Design of Experiments and ANOVA techniques. (c) 2013 Elsevier B.V. All rights reserved.
    Electric Power Systems Research 11/2013; 104:9-17. DOI:10.1016/j.epsr.2013.05.021 · 1.60 Impact Factor
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    ABSTRACT: The observability of the state of a power system given a set of redundant measurements is a relevant problem as it is a necessary step prior to state estimation, as well as an accurate estimate of the state is crucial to achieve a secure operation. In this paper, this problem is addressed through a novel approach: the solution of a well-behaved integer linear programming problem. Several realistic case studies including up to 2383 buses (Polish system) illustrate the computational efficiency of the method proposed. The obtained results are analyzed from a statistical perspective and compared with a traditional approach using the design of experiments and analysis of variance (ANOVA) methodologies.
    Electric Power Systems Research 11/2013; 104:207-215. DOI:10.1016/j.epsr.2013.06.019 · 1.60 Impact Factor
  • Salvador Pineda, Antonio J. Conejo
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    ABSTRACT: As a consequence of competition in electricity markets, a wide variety of financial derivatives have emerged to allow market agents to hedge against risks. Electricity options and forward contracts constitute adequate instruments to manage the financial risks pertaining to price volatility or unexpected unit failures faced by power producers. A multi-stage stochastic model is described in this tutorial paper to determine the optimal forward and option contracting decisions for a risk-averse power producer. The key features of electricity options to reduce both price and availability risks are illustrated by using two examples.
    09/2013; 1(2):101-109. DOI:10.1007/s40565-013-0018-y
  • 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 08/2013; 28(3):2623-2631. DOI:10.1109/TPWRS.2012.2235468 · 3.53 Impact Factor

Publication Stats

8k Citations
503.19 Total Impact Points

Institutions

  • 2014–2015
    • The Ohio State University
      • Department of Computer Science and Engineering
      Columbus, Ohio, United States
  • 1998–2014
    • University of Castilla-La Mancha
      • • Department of Chemical Engineering
      • • Electric Engineering, Electronics, Automatics and Communications
      Ciudad Real, Castille-La Mancha, Spain
  • 2011
    • Universidad de Cantabria
      Santander, Cantabria, Spain
  • 2007
    • Johns Hopkins University
      Baltimore, Maryland, United States
  • 1999–2006
    • University Carlos III de Madrid
      • Department of Statistics
      Getafe, Madrid, Spain
    • Iowa State University
      • Department of Electrical and Computer Engineering
      Ames, IA, United States
  • 2002–2005
    • McGill University
      • Department of Electrical & Computer Engineering
      Montréal, Quebec, Canada
  • 1994–2000
    • University of Malaga
      Málaga, Andalusia, Spain
    • Universidad Pontificia Comillas
      Madrid, Madrid, Spain
  • 1997–1999
    • University of Waterloo
      • Department of Electrical & Computer Engineering
      Waterloo, Quebec, Canada