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Prof. Dr. Luis Baringo
Escuela Técnica Superior de Ingeniería Industrial, University of Castilla–La Mancha, 13071 Ciudad Real, Spain

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Research Keywords & Expertise

0 Electricity Markets
0 Operations Research
0 Power Systems
0 Robust Optimization
0 Stochastic Programming

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Robust Optimization
Stochastic Programming
Electricity Markets
Power Systems
Electric energy systems

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Book chapter
Published: 01 June 2021 in Virtual Power Plants and Electricity Markets
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ACS Style

Luis Baringo; Morteza Rahimiyan. Correction to: Virtual Power Plant Model. Virtual Power Plants and Electricity Markets 2021, C1 -C1.

AMA Style

Luis Baringo, Morteza Rahimiyan. Correction to: Virtual Power Plant Model. Virtual Power Plants and Electricity Markets. 2021; ():C1-C1.

Chicago/Turabian Style

Luis Baringo; Morteza Rahimiyan. 2021. "Correction to: Virtual Power Plant Model." Virtual Power Plants and Electricity Markets , no. : C1-C1.

Journal article
Published: 29 May 2021 in International Journal of Electrical Power & Energy Systems
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This paper presents a novel approach for the investment planning of a virtual power plant trading energy in an electricity market. The virtual power plant comprises conventional generating units, renewable generating units, storage units, and a set of flexible demands. In order to maximize its expected profit, the virtual power plant has the possibility of installing new conventional, renewable, and storage units. Such investment decisions are made under the long-term uncertainty associated with future production costs of the conventional generating units, future consumption levels of the flexible demands, and future energy market prices, as well as the short-term variability of market prices and renewable production levels. In addition, the effect of generation and storage operation on investment decisions is precisely characterized by a detailed nonconvex formulation. The resulting model is cast as a scenario-based two-stage stochastic programming problem wherein the conditional value-at-risk is used to represent the risk aversion of the owner of the virtual power plant. Numerical results from several case studies show that the virtual power plant can significantly increase its expected profit by expanding its generation and storage assets. Moreover, neglecting nonconvex operational constraints generally results in over-investment in conventional generating units. The moderate computational effort required to solve instances with up to 45 candidate assets backs the practical applicability of the proposed approach.

ACS Style

Ana Baringo; Luis Baringo; José M. Arroyo. Holistic planning of a virtual power plant with a nonconvex operational model: A risk-constrained stochastic approach. International Journal of Electrical Power & Energy Systems 2021, 132, 107081 .

AMA Style

Ana Baringo, Luis Baringo, José M. Arroyo. Holistic planning of a virtual power plant with a nonconvex operational model: A risk-constrained stochastic approach. International Journal of Electrical Power & Energy Systems. 2021; 132 ():107081.

Chicago/Turabian Style

Ana Baringo; Luis Baringo; José M. Arroyo. 2021. "Holistic planning of a virtual power plant with a nonconvex operational model: A risk-constrained stochastic approach." International Journal of Electrical Power & Energy Systems 132, no. : 107081.

Journal article
Published: 25 March 2021 in International Journal of Electrical Power & Energy Systems
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The main purpose of this study is to support a retail electric provider (REP) to make the best day-ahead dynamic pricing decisions in a realistic scenario. These decisions are made with the aim of maximizing the profit achieved by the REP under the assumption that mixed types of customers with different behaviors in the electricity market are considered. While some of the customers have installed smart meters with an embedded home energy management system (HEMS) in their home, others do not participate in the demand response (DR) programs. For this purpose, a bi-level hybrid demand modeling framework is proposed. It firstly uses an optimal energy management algorithm with bill minimization in order to model the behavior of customers with smart meters. Then, using a customers' behavior learning machine (CBLM), the behavior of other groups without smart meters is modeled. Therefore, the proposed hybrid model cannot only schedule usage of home appliances to the interests of customers with smart meters but can also be used to understand electricity usage behavior of customers without smart meters. The proposed model includes a stacked auto-encoder (SAE), one of the deep learning (DL) methods suitable for real-valued inputs, and adaptive neuro-fuzzy inference system (ANFIS). Based on the established hybrid demand model for all customers, a profit maximization algorithm is developed in order to achieve optimal prices for the REP under relevant market constraints. The results of the case studies confirm the applicability and effectiveness of the proposed model.

ACS Style

Hossein Taherian; Mohammad Reza Aghaebrahimi; Luis Baringo; Saeid Reza Goldani. Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid. International Journal of Electrical Power & Energy Systems 2021, 130, 107004 .

AMA Style

Hossein Taherian, Mohammad Reza Aghaebrahimi, Luis Baringo, Saeid Reza Goldani. Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid. International Journal of Electrical Power & Energy Systems. 2021; 130 ():107004.

Chicago/Turabian Style

Hossein Taherian; Mohammad Reza Aghaebrahimi; Luis Baringo; Saeid Reza Goldani. 2021. "Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid." International Journal of Electrical Power & Energy Systems 130, no. : 107004.

Journal article
Published: 20 March 2021 in Energy Economics
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This paper proposes a two-stage robust optimization model for the transmission network expansion planning problem. Long-term uncertainties in the peak demand and generation capacity are modeled using confidence bounds, while the short-term variability of demand and renewable production is modeled using a set of representative days. As a distinctive feature, this work takes into account the non-convex operation of conventional generating units and storage facilities, which results in a two-stage robust optimization model with a discrete recourse problem. The resulting problem is solved using a nested column-and-constraint generation algorithm that guarantees convergence to the global optimum in a finite number of iterations. An illustrative example and a case study are used to show the performance of the proposed approach. Numerical results show that neglecting the non-convex operation of conventional generating units and storage facilities leads to suboptimal expansion decisions.

ACS Style

Álvaro García-Cerezo; Luis Baringo; Raquel García-Bertrand. Robust transmission network expansion planning considering non-convex operational constraints. Energy Economics 2021, 98, 105246 .

AMA Style

Álvaro García-Cerezo, Luis Baringo, Raquel García-Bertrand. Robust transmission network expansion planning considering non-convex operational constraints. Energy Economics. 2021; 98 ():105246.

Chicago/Turabian Style

Álvaro García-Cerezo; Luis Baringo; Raquel García-Bertrand. 2021. "Robust transmission network expansion planning considering non-convex operational constraints." Energy Economics 98, no. : 105246.

Journal article
Published: 18 March 2021 in IEEE Transactions on Power Systems
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The use of historical data in transmission network expansion planning problems is key to represent the short-term uncertainties in demand and stochastic renewable production conditions. Nevertheless, the use of all available historical data leads to intractable problems. For this reason, input data should be reduced while keeping important information about the system under study. Several clustering methods have been used in the technical literature for this purpose, but these generally do not represent extreme conditions such as peak demand levels, which may be critical to avoid load shedding. This letter proposes a novel approach to obtain representative time periods based on the maximum dissimilarity algorithm, which properly represents these extreme conditions. Numerical results show that the load is completely supplied using the proposed technique in all cases and that the number of required representative time periods is significantly reduced in comparison with other techniques, which translates into a reduction of the complexity of the transmission expansion planning problem.

ACS Style

Alvaro Garcia-Cerezo; Raquel Garcia-Bertrand; Luis Baringo. Enhanced Representative Time Periods for Transmission Expansion Planning Problems. IEEE Transactions on Power Systems 2021, 36, 3802 -3805.

AMA Style

Alvaro Garcia-Cerezo, Raquel Garcia-Bertrand, Luis Baringo. Enhanced Representative Time Periods for Transmission Expansion Planning Problems. IEEE Transactions on Power Systems. 2021; 36 (4):3802-3805.

Chicago/Turabian Style

Alvaro Garcia-Cerezo; Raquel Garcia-Bertrand; Luis Baringo. 2021. "Enhanced Representative Time Periods for Transmission Expansion Planning Problems." IEEE Transactions on Power Systems 36, no. 4: 3802-3805.

Journal article
Published: 04 September 2020 in Energies
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This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM are calculated by using the Loss Sensitivity Factor (LSF). The optimal size of the DSTATCOM is simulated by using the newly developed CSA. In the proposed method, load flow calculations are performed by using a fast and efficient backward/forward sweep algorithm. Here, the mathematically formed objective function of the proposed method is to reduce the total system power losses. Standard 33-bus and 69-bus systems have been used to show the effectiveness of the proposed CSA-based optimization method in the RDS with different load models. The simulated results confirm that the optimal allocation of DSTATCOM plays a significant role in power loss minimization and enhanced voltage profile. The placement of DSTATCOM in RDS also plan an important role for minimizing uncertainties in the distribution level. The proposed method encourages one to use renewable-based resources, which results in affordable and clean energy.

ACS Style

Devabalaji Kaliaperumal Rukmani; Yuvaraj Thangaraj; Umashankar Subramaniam; Sitharthan Ramachandran; Rajvikram Madurai Elavarasan; Narottam Das; Luis Baringo; Mohamed Imran Abdul Rasheed. A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks using Bio-Inspired Cuckoo Search Algorithm. Energies 2020, 13, 4615 .

AMA Style

Devabalaji Kaliaperumal Rukmani, Yuvaraj Thangaraj, Umashankar Subramaniam, Sitharthan Ramachandran, Rajvikram Madurai Elavarasan, Narottam Das, Luis Baringo, Mohamed Imran Abdul Rasheed. A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks using Bio-Inspired Cuckoo Search Algorithm. Energies. 2020; 13 (18):4615.

Chicago/Turabian Style

Devabalaji Kaliaperumal Rukmani; Yuvaraj Thangaraj; Umashankar Subramaniam; Sitharthan Ramachandran; Rajvikram Madurai Elavarasan; Narottam Das; Luis Baringo; Mohamed Imran Abdul Rasheed. 2020. "A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks using Bio-Inspired Cuckoo Search Algorithm." Energies 13, no. 18: 4615.

Journal article
Published: 01 September 2020 in IEEE Transactions on Power Systems
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Recently, several authors have reported specific criteria to support the widely accepted use of a convex operational model for bulk energy storage in the formulation of power system operation and planning problems involving binary variables. Using two counterexamples, we show that this modeling simplification may give rise to impractical solutions featuring simultaneous charging and discharging, even though the recently published criteria are satisfied. Thus, both counterexamples invalidate those criteria and demonstrate that the customarily used convex model may be unsuitable for the precise incorporation of bulk storage in such instances of mixed-integer programming.

ACS Style

Jose M. Arroyo; Luis Baringo; Ana Baringo; Ricardo Bolanos; Natalia Alguacil-Conde; Noemi Gonzalez Cobos. On the Use of a Convex Model for Bulk Storage in MIP-Based Power System Operation and Planning. IEEE Transactions on Power Systems 2020, 35, 4964 -4967.

AMA Style

Jose M. Arroyo, Luis Baringo, Ana Baringo, Ricardo Bolanos, Natalia Alguacil-Conde, Noemi Gonzalez Cobos. On the Use of a Convex Model for Bulk Storage in MIP-Based Power System Operation and Planning. IEEE Transactions on Power Systems. 2020; 35 (6):4964-4967.

Chicago/Turabian Style

Jose M. Arroyo; Luis Baringo; Ana Baringo; Ricardo Bolanos; Natalia Alguacil-Conde; Noemi Gonzalez Cobos. 2020. "On the Use of a Convex Model for Bulk Storage in MIP-Based Power System Operation and Planning." IEEE Transactions on Power Systems 35, no. 6: 4964-4967.

Journal article
Published: 31 March 2020 in Applied Energy
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The decarbonization of energy systems passes through the transition towards low- and zero-emission vehicles and the investments in efficient technologies. To this end, an adaptive robust optimization approach is proposed for the expansion planning problem of a distribution system where expansion decisions involve the construction of renewable generating units, storage units, and charging stations for electric vehicles. The problem is formulated under the perspective of a central planner that aims at determining the expansion plan that minimizes both investment and operation costs. Both short-term variability and long-term uncertainty are considered in the proposed approach and are modeled in different ways. Short-term variability of the demand, the production of stochastic units, and the price of electricity withdrawn from or injected into the transmission system is modeled using a number of representative days corresponding to different operating conditions. Long-term uncertainty in the future peak demands, the future value of electricity exchanged with the transmission grid, and the number of electric vehicles is instead modeled through confidence bounds. A case study based on a 69-node distribution network shows the effectiveness of the proposed technique and the relationship between the optimal expansions decisions, the revenues from selling electricity to the electric vehicles, the degree of independence from the transmission system, and the role played by the investment budget availability. Moreover, an ex-post decarbonization analysis is conducted to evaluate the environmental impact of the adoption of electric vehicles. Finally, the proposed approach outperforms the results of a stochastic model in terms of computational performance.

ACS Style

Luis Baringo; Luigi Boffino; Giorgia Oggioni. Robust expansion planning of a distribution system with electric vehicles, storage and renewable units. Applied Energy 2020, 265, 114679 .

AMA Style

Luis Baringo, Luigi Boffino, Giorgia Oggioni. Robust expansion planning of a distribution system with electric vehicles, storage and renewable units. Applied Energy. 2020; 265 ():114679.

Chicago/Turabian Style

Luis Baringo; Luigi Boffino; Giorgia Oggioni. 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units." Applied Energy 265, no. : 114679.

Journal article
Published: 06 March 2020 in Energies
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A model suitable to obtain where and when renewable energy sources (RES) should be allocated as part of generation planning in distribution systems is formulated. The proposed model starts from an existing two-stage stochastic mixed-integer linear programming (MILP) problem including investment and scenario-dependent operation decisions. The aim is to minimize photovoltaic and wind investment costs, operation costs, as well as total substation costs including the cost of the energy bought from substations and energy losses. A new Benders’ decomposition framework is used to decouple the problem between investment and operation decisions, where the latter can be further decomposed into a set of smaller problems per scenario and planning period. The model is applied to a 34-bus system and a comparison with a MILP model is presented to show the advantages of the model proposed.

ACS Style

Sergio Montoya-Bueno; Jose Ignacio Muñoz-Hernandez; Javier Contreras; Luis Baringo. A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems. Energies 2020, 13, 1225 .

AMA Style

Sergio Montoya-Bueno, Jose Ignacio Muñoz-Hernandez, Javier Contreras, Luis Baringo. A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems. Energies. 2020; 13 (5):1225.

Chicago/Turabian Style

Sergio Montoya-Bueno; Jose Ignacio Muñoz-Hernandez; Javier Contreras; Luis Baringo. 2020. "A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems." Energies 13, no. 5: 1225.

Journal article
Published: 10 January 2020 in Energies
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Short-term uncertainty needs to be properly modeled when analyzing a planning problem in a power system. Since the use of all available historical data may lead to problems of computational intractability, clustering algorithms may be applied in order to reduce the computational effort without compromising accurate representation of historical data. In this paper, we propose a modified version of the traditional K-means method, seeking to represent the maximum and minimum values of input data, namely, electricity demand and renewable production in several locations of a power system. Extreme values of these parameters must be represented as they are high-impact decisions that are taken with respect to expansion and operation. The method proposed is based on the K-means algorithm, which represents the correlation between demand and wind-power production. The chronology of historical data, which influences the performance of some technologies, is characterized through representative days, each made up of 24 operating conditions. A realistic case study, applying representative days, analyzes the generation and transmission expansion planning of the IEEE 24-bus Reliability Test System. Results show that the proposed method is preferable to the traditional K-means technique.

ACS Style

Álvaro García-Cerezo; Luis Baringo; Raquel García-Bertrand. Representative Days for Expansion Decisions in Power Systems. Energies 2020, 13, 335 .

AMA Style

Álvaro García-Cerezo, Luis Baringo, Raquel García-Bertrand. Representative Days for Expansion Decisions in Power Systems. Energies. 2020; 13 (2):335.

Chicago/Turabian Style

Álvaro García-Cerezo; Luis Baringo; Raquel García-Bertrand. 2020. "Representative Days for Expansion Decisions in Power Systems." Energies 13, no. 2: 335.

Preprint
Published: 16 July 2019
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Short-term uncertainty should be properly modeled when the expansion planning problem in a power system is analyzed. Since the use of all available historical data may lead to intractability, clustering algorithms should be applied in order to reduce computer workload without renouncing accuracy representation of historical data. In this paper, we propose a modified version of the traditional K-means method that seeks to attain the representation of maximum and minimum values of input data, namely, the electric load and the renewable production in several locations of an electric energy system. The crucial role of depicting extreme values of these parameters lies in the fact that they can have a great impact on the expansion and operation decisions taken. The proposed method is based on the traditional K-means algorithm that represents the correlation between electric load and wind-power production. Chronology of historical data, which influences the performance of some technologies, is characterized though representative days, each one composed of 24 operating conditions. A realistic case study based on the generation and transmission expansion planning of the IEEE 24-bus Reliability Test System is analyzed applying representative days and comparing the results obtained using the traditional K-means technique and the proposed method.

ACS Style

Álvaro García-Cerezo; Luis Baringo. Representative Days for Expansion Decisions in Power Systems. 2019, 1 .

AMA Style

Álvaro García-Cerezo, Luis Baringo. Representative Days for Expansion Decisions in Power Systems. . 2019; ():1.

Chicago/Turabian Style

Álvaro García-Cerezo; Luis Baringo. 2019. "Representative Days for Expansion Decisions in Power Systems." , no. : 1.

Article
Published: 20 December 2018 in IET Generation, Transmission & Distribution
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The real-time (RT) energy management of virtual power plants (VPPs) is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This article presents a comparative analysis on alternative energy management models for a VPP that includes a wind power unit, an energy storage unit, and some flexible demands, which are interconnected within a small size electric energy system. The smart grid technology enables RT operation of the VPP by taking advantage of bidirectional communication infrastructure. To accomplish this task, the RT energy management is implemented through alternative decision-making tools, namely, (i) a robust model, (ii) a stochastic programming model, and (iii) a stochastic robust model. To handle uncertainties, these optimisation models use prediction intervals, scenarios, and a combination of both, respectively. A realistic case study provides a comparative out-of-sample analysis considering the impact of the different parameters used to manage risk in these three models.

ACS Style

Morteza Rahimiyan; Luis Baringo. Real‐time energy management of a smart virtual power plant. IET Generation, Transmission & Distribution 2018, 13, 2015 -2023.

AMA Style

Morteza Rahimiyan, Luis Baringo. Real‐time energy management of a smart virtual power plant. IET Generation, Transmission & Distribution. 2018; 13 (11):2015-2023.

Chicago/Turabian Style

Morteza Rahimiyan; Luis Baringo. 2018. "Real‐time energy management of a smart virtual power plant." IET Generation, Transmission & Distribution 13, no. 11: 2015-2023.

Journal article
Published: 28 November 2018 in IEEE Transactions on Power Systems
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ACS Style

Ana Baringo; Luis Baringo; Jose M. Arroyo. Day-Ahead Self-Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets Under Uncertainty. IEEE Transactions on Power Systems 2018, 34, 1881 -1894.

AMA Style

Ana Baringo, Luis Baringo, Jose M. Arroyo. Day-Ahead Self-Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets Under Uncertainty. IEEE Transactions on Power Systems. 2018; 34 (3):1881-1894.

Chicago/Turabian Style

Ana Baringo; Luis Baringo; Jose M. Arroyo. 2018. "Day-Ahead Self-Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets Under Uncertainty." IEEE Transactions on Power Systems 34, no. 3: 1881-1894.

Conference paper
Published: 01 June 2018 in 2018 Power Systems Computation Conference (PSCC)
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ACS Style

Ana Baringo; Luis Baringo; Jose M. Arroyo. Self Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets: A Stochastic Adaptive Robust Optimization Approach. 2018 Power Systems Computation Conference (PSCC) 2018, 1 .

AMA Style

Ana Baringo, Luis Baringo, Jose M. Arroyo. Self Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets: A Stochastic Adaptive Robust Optimization Approach. 2018 Power Systems Computation Conference (PSCC). 2018; ():1.

Chicago/Turabian Style

Ana Baringo; Luis Baringo; Jose M. Arroyo. 2018. "Self Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets: A Stochastic Adaptive Robust Optimization Approach." 2018 Power Systems Computation Conference (PSCC) , no. : 1.

Journal article
Published: 08 June 2017 in IEEE Transactions on Power Systems
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This paper proposes a stochastic adaptive robust optimization approach for the generation and transmission expansion planning problem. The problem is formulated under the perspective of a central planner, e.g., the transmission system operator, that aims at determining the generation and transmission expansion plans that minimize both the expansion and operation costs. This central planner builds the transmission facilities and promotes the building of the most suitable generating units among private profit-oriented investors. Uncertainties in the future peak demand and the future generation (fuel) cost are modeled using confidence bounds, while uncertainties in the demand variability and the production of stochastic units are modeled using a number of operating conditions. Results of an illustrative example and a case study based on the IEEE 118-bus Test System show the effectiveness of the proposed approach.

ACS Style

Luis Baringo; Ana Baringo. A Stochastic Adaptive Robust Optimization Approach for the Generation and Transmission Expansion Planning. IEEE Transactions on Power Systems 2017, 33, 792 -802.

AMA Style

Luis Baringo, Ana Baringo. A Stochastic Adaptive Robust Optimization Approach for the Generation and Transmission Expansion Planning. IEEE Transactions on Power Systems. 2017; 33 (1):792-802.

Chicago/Turabian Style

Luis Baringo; Ana Baringo. 2017. "A Stochastic Adaptive Robust Optimization Approach for the Generation and Transmission Expansion Planning." IEEE Transactions on Power Systems 33, no. 1: 792-802.

Journal article
Published: 01 December 2016 in IEEE Transactions on Power Systems
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This paper proposes a novel approach for the offering strategy of a virtual power plant that participates in the day-ahead and the real-time energy markets. The virtual power plant comprises a conventional power plant, a wind-power unit, a storage facility, and flexible demands, which participate in the day-ahead and the real-time markets as a single entity in order to optimize their energy resources. We model the uncertainty in the wind-power production and in the market prices using confidence bounds and scenarios, respectively, which allows us to formulate the strategic offering problem as a stochastic adaptive robust optimization model. Results of a case study are provided to show the applicability of the proposed approach.

ACS Style

Ana Baringo; Luis Baringo. A Stochastic Adaptive Robust Optimization Approach for the Offering Strategy of a Virtual Power Plant. IEEE Transactions on Power Systems 2016, 32, 3492 -3504.

AMA Style

Ana Baringo, Luis Baringo. A Stochastic Adaptive Robust Optimization Approach for the Offering Strategy of a Virtual Power Plant. IEEE Transactions on Power Systems. 2016; 32 (5):3492-3504.

Chicago/Turabian Style

Ana Baringo; Luis Baringo. 2016. "A Stochastic Adaptive Robust Optimization Approach for the Offering Strategy of a Virtual Power Plant." IEEE Transactions on Power Systems 32, no. 5: 3492-3504.

Journal article
Published: 26 October 2015 in IEEE Transactions on Power Systems
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We consider an energy management system that controls a cluster of price-responsive demands. Besides these demands, it also manages a wind-power plant and an energy storage facility. Demands, wind-power plant, and energy storage facility are interconnected within a small size electric energy system equipped with smart grid technology and constitute a virtual power plant that can strategically buy and sell energy in both the day-ahead and the real-time markets. To this end, we propose a two-stage procedure based on robust optimization. In the first stage, the bidding strategy in the day-ahead market is decided. In the second stage, and once the actual scheduling in the day-ahead market is known, we decide the bidding strategy in the real-time market for each hour of the day. We consider that the virtual power plant behaves as a price taker in these markets. Robust optimization is used to deal with uncertainties in wind-power production and market prices, which are represented through confidence bounds. Results of a realistic case study are provided to show the applicability of the proposed approach.

ACS Style

Morteza Rahimiyan; Luis Baringo. Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach. IEEE Transactions on Power Systems 2015, 31, 2676 -2687.

AMA Style

Morteza Rahimiyan, Luis Baringo. Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach. IEEE Transactions on Power Systems. 2015; 31 (4):2676-2687.

Chicago/Turabian Style

Morteza Rahimiyan; Luis Baringo. 2015. "Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach." IEEE Transactions on Power Systems 31, no. 4: 2676-2687.

Conference paper
Published: 01 May 2015 in 2015 12th International Conference on the European Energy Market (EEM)
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Congestion management is crucial to maintain an efficient and reliable power system operation, and thus it is an important task for any transmission system operator. Different congestion management approaches are currently implemented by different TSOs. In this paper, we consider two market schemes. The first one considers the transmission constraints both on a day-ahead (DA) basis and in real-time (RT). This scheme is relevant to centralized markets. The second market scheme considers the transmission constraints only in RT, and is relevant to decentralized markets. We quantitatively compare the congestion management in these market schemes, in a power system with thermal and wind generation, as we vary (i) the share of thermal units, (ii) the wind penetration level, and (iii) the congestion level. For this purpose, we formulate a DA and a RT market model. The DA market is formulated as a two-stage stochastic optimization problem, and the RT market is formulated as a stochastic optimization problem with receding horizon that uses short-term wind power scenarios. We demonstrate the performance of the proposed scheme in the IEEE 24-bus Reliability Test System. The results show that as the share of inflexible units increases, and when congestion occurs, it is more efficient to implement the congestion management in the DA market. However, in systems with a large share of flexible units, the two congestion management schemes achieve similar results.

ACS Style

Alexandra Zigkiri; Luis Baringo; Göran Andersson; Marek Zima; Zigkiri Alexandra. Congestion management in electricity markets with uncertain infeeds and commitment decisions. 2015 12th International Conference on the European Energy Market (EEM) 2015, 1 -5.

AMA Style

Alexandra Zigkiri, Luis Baringo, Göran Andersson, Marek Zima, Zigkiri Alexandra. Congestion management in electricity markets with uncertain infeeds and commitment decisions. 2015 12th International Conference on the European Energy Market (EEM). 2015; ():1-5.

Chicago/Turabian Style

Alexandra Zigkiri; Luis Baringo; Göran Andersson; Marek Zima; Zigkiri Alexandra. 2015. "Congestion management in electricity markets with uncertain infeeds and commitment decisions." 2015 12th International Conference on the European Energy Market (EEM) , no. : 1-5.

Journal article
Published: 20 April 2015 in IEEE Transactions on Power Systems
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Given the significant amount of installed generation-capacity based on wind power, and also due to current economic downturn, the subsidies and incentives that have been widely used by wind-power producers to recover their investment costs have decreased and are even expected to disappear in the near future. In these conditions, wind-power producers need to develop offering strategies to make their investments profitable counting solely on the market. This paper proposes a multi-stage risk-constrained stochastic complementarity model to derive the optimal offering strategy of a wind-power producer that participates in both the day-ahead and the balancing markets. Uncertainties concerning wind-power productions, market prices, demands' bids, and rivals' offers are efficiently modeled using a set of scenarios. The conditional-value-at-risk metric is used to model the profit risk associated with the offering decisions. The proposed model is recast as a tractable mixed-integer linear programming program solvable using available branch-and-cut algorithms. Results of a case study are reported and discussed to show the effectiveness and applicability of the proposed approach.

ACS Style

Luis Baringo; Antonio Conejo. Offering Strategy of Wind-Power Producer: A Multi-Stage Risk-Constrained Approach. IEEE Transactions on Power Systems 2015, 31, 1420 -1429.

AMA Style

Luis Baringo, Antonio Conejo. Offering Strategy of Wind-Power Producer: A Multi-Stage Risk-Constrained Approach. IEEE Transactions on Power Systems. 2015; 31 (2):1420-1429.

Chicago/Turabian Style

Luis Baringo; Antonio Conejo. 2015. "Offering Strategy of Wind-Power Producer: A Multi-Stage Risk-Constrained Approach." IEEE Transactions on Power Systems 31, no. 2: 1420-1429.

Journal article
Published: 19 December 2013 in IEEE Transactions on Power Systems
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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.

ACS Style

Luis Baringo; Antonio Conejo. Strategic Wind Power Investment. IEEE Transactions on Power Systems 2013, 29, 1250 -1260.

AMA Style

Luis Baringo, Antonio Conejo. Strategic Wind Power Investment. IEEE Transactions on Power Systems. 2013; 29 (3):1250-1260.

Chicago/Turabian Style

Luis Baringo; Antonio Conejo. 2013. "Strategic Wind Power Investment." IEEE Transactions on Power Systems 29, no. 3: 1250-1260.