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Friedrich Kunz - One of the best experts on this subject based on the ideXlab platform.

  • Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market
    Networks and Spatial Economics, 2015
    Co-Authors: Jan Abrell, Friedrich Kunz
    Abstract:

    In northern Europe, wind energy has become a dominate renewable energy source, due to natural conditions and national support schemes. However, the uncertainty about wind generation affects existing network infrastructure and power production Planning of generators, which cannot be fully diminished by wind forecasts. In this paper we develop a stochastic electricity market model to analyze the impact of uncertain wind generation on the different electricity markets as well as network congestion management. Stochastic programming techniques are used to incorporate uncertain wind generation. The technical characteristics of transporting electrical energy as well as power plants are explicitly taken into account. The consecutive clearing of the electricity markets is incorporated by a rolling Planning Procedure reflecting the market regime of European markets. The model is applied to the German electricity system covering one week. Two different approaches of considering uncertain wind generation are analyzed and compared to a deterministic approach. The results reveal that the flexibility of generation dispatch is increased either by using more flexible generation technologies or by operating rather inflexible technologies under part-load conditions.

  • integrating intermittent renewable wind generation a stochastic multi market electricity model for the european electricity market
    2013
    Co-Authors: Jan Abrell, Friedrich Kunz
    Abstract:

    In northern Europe wind energy has become a dominating renewable energy source due to natural conditions and national support schemes. However, the uncertainty about wind generation affects existing network infrastructure and power production Planning of generators and cannot not be fully diminished by wind forecasts. In this paper we develop a stochastic electricity market model to analyze the impact of uncertain wind generation on the different electricity markets as well as network congestion management. Stochastic programming techniques are used to incorporate uncertain wind generation. The technical characteristics of transporting electrical energy as well as power plants are explicitly taken into account. The consecutive clearing of the electricity markets is incorporated by a rolling Planning Procedure reflecting the market regime of European markets. The model is applied to the German electricity system covering an exemplary week. Three different cases of considering uncertain wind generation are analyzed. The results reveal that the flexibility of the generation dispatch is increased either by using more flexible generation technologies or by flexibilizing the generation pattern of rather inflexible technologies.

Daniel S Kirschen - One of the best experts on this subject based on the ideXlab platform.

  • coupling pumped hydro energy storage with unit commitment
    IEEE Transactions on Sustainable Energy, 2016
    Co-Authors: Kenneth Bruninx, Yury Dvorkin, Erik Delarue, Hrvoje Pandzic, William Dhaeseleer, Daniel S Kirschen
    Abstract:

    Renewable electricity generation not only provides affordable and emission-free electricity but also introduces additional complexity in the day-ahead Planning Procedure. To address the stochastic nature of renewable generation, system operators must schedule enough controllable generation to have the flexibility required to compensate unavoidable real-time mismatches between the production and consumption of electricity. This flexibility must be scheduled ahead of real-time and comes at a cost, which should be minimized without compromising the operational reliability of the system. Energy storage facilities, such as pumped hydro energy storage (PHES), can respond quickly to mismatches between demand and generation. Hydraulic constraints on the operation of PHES must be taken into account in the day-ahead scheduling problem, which is typically not done in deterministic models. Stochastic optimization enhances the procurement of flexibility, but requires more computational resources than conventional deterministic optimization. This paper proposes a deterministic and an interval unit commitment formulation for the co-optimization of controllable generation and PHES, including a representation of the hydraulic constraints of the PHES. The proposed unit commitment (UC) models are tested against a stochastic UC formulation on a model of the Belgian power system to compare the resulting operational cost, reliability, and computational requirements. The cost-effective regulating capabilities offered by the PHES yield significant operational cost reductions in both models, while the increase in calculation times is limited.

  • near optimal method for siting and sizing of distributed storage in a transmission network
    IEEE Transactions on Power Systems, 2015
    Co-Authors: Hrvoje Pandzic, Yury Dvorkin, Yishen Wang, Daniel S Kirschen
    Abstract:

    Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage Planning Procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96.

  • near optimal method for siting and sizing of distributed storage in a transmission network
    IEEE Transactions on Power Systems, 2015
    Co-Authors: Hrvoje Pandzic, Yury Dvorkin, Yishen Wang, Ting Qiu, Daniel S Kirschen
    Abstract:

    Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage Planning Procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96.

Jan Abrell - One of the best experts on this subject based on the ideXlab platform.

  • Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market
    Networks and Spatial Economics, 2015
    Co-Authors: Jan Abrell, Friedrich Kunz
    Abstract:

    In northern Europe, wind energy has become a dominate renewable energy source, due to natural conditions and national support schemes. However, the uncertainty about wind generation affects existing network infrastructure and power production Planning of generators, which cannot be fully diminished by wind forecasts. In this paper we develop a stochastic electricity market model to analyze the impact of uncertain wind generation on the different electricity markets as well as network congestion management. Stochastic programming techniques are used to incorporate uncertain wind generation. The technical characteristics of transporting electrical energy as well as power plants are explicitly taken into account. The consecutive clearing of the electricity markets is incorporated by a rolling Planning Procedure reflecting the market regime of European markets. The model is applied to the German electricity system covering one week. Two different approaches of considering uncertain wind generation are analyzed and compared to a deterministic approach. The results reveal that the flexibility of generation dispatch is increased either by using more flexible generation technologies or by operating rather inflexible technologies under part-load conditions.

  • integrating intermittent renewable wind generation a stochastic multi market electricity model for the european electricity market
    2013
    Co-Authors: Jan Abrell, Friedrich Kunz
    Abstract:

    In northern Europe wind energy has become a dominating renewable energy source due to natural conditions and national support schemes. However, the uncertainty about wind generation affects existing network infrastructure and power production Planning of generators and cannot not be fully diminished by wind forecasts. In this paper we develop a stochastic electricity market model to analyze the impact of uncertain wind generation on the different electricity markets as well as network congestion management. Stochastic programming techniques are used to incorporate uncertain wind generation. The technical characteristics of transporting electrical energy as well as power plants are explicitly taken into account. The consecutive clearing of the electricity markets is incorporated by a rolling Planning Procedure reflecting the market regime of European markets. The model is applied to the German electricity system covering an exemplary week. Three different cases of considering uncertain wind generation are analyzed. The results reveal that the flexibility of the generation dispatch is increased either by using more flexible generation technologies or by flexibilizing the generation pattern of rather inflexible technologies.

Adela Pages - One of the best experts on this subject based on the ideXlab platform.

  • a three stage short term electric power Planning Procedure for a generation company in a liberalized market
    International Journal of Electrical Power & Energy Systems, 2007
    Co-Authors: Narcis Nabona, Adela Pages
    Abstract:

    In liberalized electricity markets, generation companies bid their hourly generation in order to maximize their profit. The optimization of the generation bids over a short-term weekly period must take into account the action of the competing generation companies and the market-price formation rules and must be coordinated with long-term Planning results. This paper presents a three stage optimization process with a data analysis and parameter calculation, a linearized unit commitment, and a nonlinear generation scheduling refinement. Although the Procedure has been developed from the experience with the Spanish power market, with minor adaptations it is also applicable to any generation company participating in a competitive market system.

Hrvoje Pandzic - One of the best experts on this subject based on the ideXlab platform.

  • coupling pumped hydro energy storage with unit commitment
    IEEE Transactions on Sustainable Energy, 2016
    Co-Authors: Kenneth Bruninx, Yury Dvorkin, Erik Delarue, Hrvoje Pandzic, William Dhaeseleer, Daniel S Kirschen
    Abstract:

    Renewable electricity generation not only provides affordable and emission-free electricity but also introduces additional complexity in the day-ahead Planning Procedure. To address the stochastic nature of renewable generation, system operators must schedule enough controllable generation to have the flexibility required to compensate unavoidable real-time mismatches between the production and consumption of electricity. This flexibility must be scheduled ahead of real-time and comes at a cost, which should be minimized without compromising the operational reliability of the system. Energy storage facilities, such as pumped hydro energy storage (PHES), can respond quickly to mismatches between demand and generation. Hydraulic constraints on the operation of PHES must be taken into account in the day-ahead scheduling problem, which is typically not done in deterministic models. Stochastic optimization enhances the procurement of flexibility, but requires more computational resources than conventional deterministic optimization. This paper proposes a deterministic and an interval unit commitment formulation for the co-optimization of controllable generation and PHES, including a representation of the hydraulic constraints of the PHES. The proposed unit commitment (UC) models are tested against a stochastic UC formulation on a model of the Belgian power system to compare the resulting operational cost, reliability, and computational requirements. The cost-effective regulating capabilities offered by the PHES yield significant operational cost reductions in both models, while the increase in calculation times is limited.

  • near optimal method for siting and sizing of distributed storage in a transmission network
    IEEE Transactions on Power Systems, 2015
    Co-Authors: Hrvoje Pandzic, Yury Dvorkin, Yishen Wang, Daniel S Kirschen
    Abstract:

    Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage Planning Procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96.

  • near optimal method for siting and sizing of distributed storage in a transmission network
    IEEE Transactions on Power Systems, 2015
    Co-Authors: Hrvoje Pandzic, Yury Dvorkin, Yishen Wang, Ting Qiu, Daniel S Kirschen
    Abstract:

    Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage Planning Procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96.