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Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional model-based approaches, batch RL techniques do not require a system identification step, making them more suitable for a large-scale implementation. This paper extends fitted Q-iteration, a standard batch RL technique, to the situation when a forecast of the exogenous data is provided. In general, batch RL techniques do not rely on expert knowledge about the system dynamics or the solution. However, if some expert knowledge is provided, it can be incorporated by using the proposed policy adjustment method. Finally, we tackle the challenge of finding an open-loop schedule required to participate in the day-ahead market. We propose a model-free Monte Carlo method that uses a metric based on the state-action value function or Q-function and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat. Our experiments show that batch RL techniques provide a valuable alternative to model-based controllers and that they can be used to construct both closed-loop and open-loop policies., Accepted Author Manuscript, Team DeSchutter, Learning & Autonomous Control

Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional model-based approaches, batch RL techniques do not require a system identification step, making them more suitable for a large-scale implementation. This paper extends fitted Q-iteration, a standard batch RL technique, to the situation when a forecast of the exogenous data is provided. In general, batch RL techniques do not rely on expert knowledge about the system dynamics or the solution. However, if some expert knowledge is provided, it can be incorporated by using the proposed policy adjustment method. Finally, we tackle the challenge of finding an open-loop schedule required to participate in the day-ahead market. We propose a model-free Monte Carlo method that uses a metric based on the state-action value function or Q-function and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat. Our experiments show that batch RL techniques provide a valuable alternative to model-based controllers and that they can be used to construct both closed-loop and open-loop policies., Accepted Author Manuscript, Team Bart De Schutter, Learning & Autonomous Control

This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of a new periodically organized auction to allocate shared storage resources through physical storage rights between different market players and accompanying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage resources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players’ willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.

This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of a new periodically organized auction to allocate shared storage resources through physical storage rights between different market players and accompanying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage resources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players’ willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.

This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of anew periodically organized auction to allocate shared storage resources through physical storage rights between different market players and ac-companying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage re-sources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players' willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.

This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of a new periodically organized auction to allocate shared storage resources through physical storage rights between different market players and accompanying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage resources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players’ willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.

This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of anew periodically organized auction to allocate shared storage resources through physical storage rights between different market players and ac-companying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage re-sources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players' willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.

This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of anew periodically organized auction to allocate shared storage resources through physical storage rights between different market players and ac-companying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage re-sources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players' willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.

In this paper, a methodology is presented to optimize the dc voltage droop settings in a multiterminal voltage-source converter high-voltage direct-current system with respect to the ac system stability. Implementing dc voltage droop control enables having multiple converters assisting the system in case of a converter outage. However, the abrupt power setpoint changes create additional stress in the ac system, especially when multiple converters are connected to the same interconnected ac system. This paper presents a methodology to determine optimizd converter droop settings in order to not compromise the ac system stability, thereby taking into account the adverse effect the droop control actions have on the interconnected ac system. Developing a disturbance model of the interconnected ac/dc system, the principal directions indicate the gain and directionality of the disturbances; from this, optimal droop settings are derived to minimize the disturbance gain., QC 20140312

Analytical expressions are derived to gain insight in the operating principles of phase shifting transformers (PSTs) in a highly meshed grid. To this extent, the dc load flow algorithm is adapted to account for such devices. This leads to a linear expression for the relation between PST settings and the active power flow in the lines. Based on these equations, the total transfer capacity (TTC) can be described mathematically, which allows for optimization. Furthermore, the linear least squares method is used to distribute a cross-border transport evenly over the interconnectors. Both criteria are demonstrated by two examples, Electrical Sustainable Energy, Electrical Engineering, Mathematics and Computer Science

Analytical expressions are derived to gain insight in the operating principles of phase shifting transformers (PSTs) in a highly meshed grid. To this extent, the dc load flow algorithm is adapted to account for such devices. This leads to a linear expression for the relation between PST settings and the active power flow in the lines. Based on these equations, the total transfer capacity (TTC) can be described mathematically, which allows for optimization. Furthermore, the linear least squares method is used to distribute a cross-border transport evenly over the interconnectors. Both criteria are demonstrated by two examples, Electrical Sustainable Energy, Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science