The impact of forecasting errors and remedial actions on operational security and efficiency in classical and probabilistic market coupling

Müller, Sven; Liebenau, V.; Ruthe, Sebastian; Spieker, Christopher; Kittl, Chris; Dalhues, Stefan; Mayorga, D.; Franz, Valeri; Rehtanz, Christian

doi:10.1109/pscc.2014.7038490

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…As outlined in [18], network constraints can be included in the optimization representing the future flow-based market coupling in Europe as an alternative to the ATC-constraints. This enables the analysis of internal congestion management, the need for preventive and corrective control actions (like power flow control by HVDC links, Phase Shifting Transformers or redispatch), the enforcement of (N-k)-security, probabilistic evaluation of risks of overloads, as well as techno-economic assessment of network extension necessity and the comparison of alternative solutions under consideration of uncertain future scenarios.…”

Section: Integration Of Transmission Network Modelsmentioning

confidence: 99%

European electricity market simulation for future scenarios with high renewable energy production

Spieker

Teuwsen

Liebenau

et al. 2015

2015 IEEE Eindhoven PowerTech

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The increasing feed-in of Renewable Energy Sources (RES) and the coupling of electricity markets fundamentally change the European energy system. To investigate the impact of these developments, models are necessary which provide regionally disaggregated load and generation time series as an estimate of future system states. With the Model of International Energy Systems (MILES), this paper presents a market model for simulating demand and supply of electrical energy under consideration of generation and transmission constraints. Previous versions of MILES have already been applied in several energy system studies. As important characteristics, the model enables to generate spatially high-resolution feed-in time series of RES as well as Combined Heat and Power (CHP) plants and to perform one-year simulations for determining costminimal hourly generation schedules of thermal and pumped storage power plants. MILES is validated by the comparison of simulation results for the ENTSO-E region for the year 2013 with published historical data.

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Section: Integration Of Transmission Network Modelsmentioning

confidence: 99%

European electricity market simulation for future scenarios with high renewable energy production

Spieker

Teuwsen

Liebenau

et al. 2015

2015 IEEE Eindhoven PowerTech

Self Cite

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“…E-mail: roald@lanl.gov proposed in literature. These include, among others, robust and worst-case methods [3]- [6], two-and multi-stage stochastic programming based on samples [7]- [10] or stochastic approximation techniques [11], and chance-constrained formulations [12]- [21]. In this paper, we work with chance constraints, which ensure that the system constraints will be satisfied with a specified probability.…”

Section: Introductionmentioning

confidence: 99%

Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms

Roald

Andersson

2018

IEEE Trans. Power Syst.

210

185

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Higher levels of renewable electricity generation increase uncertainty in power system operation. To ensure secure system operation, new tools that account for this uncertainty are required. In this paper, we adopt a chance-constrained AC optimal power flow formulation, which guarantees that generation, power flows and voltages remain within their bounds with a pre-defined probability. We then discuss different chanceconstraint reformulations and solution approaches for the problem. We first describe an analytical reformulation based on partial linearization, which enables us to obtain a tractable representation of the optimization problem. We then provide an efficient algorithm based on an iterative solution scheme which alternates between solving a deterministic AC OPF problem and assessing the impact of uncertainty. The flexibility of the iterative scheme enables not only scalable implementations, but also alternative chance-constraint reformulations. In particular, we suggest two sample based reformulations that do not require any approximation or relaxation of the AC power flow equations.In a case study based on four different IEEE systems, we assess the performance of the method, and demonstrate scalability of the iterative scheme. We further show that the analytical reformulation accurately and efficiently enforces chance constraints in both in-and out-of-sample tests, and that the analytical reformulations outperforms the two alternative, sample-based chance constraint reformulations.

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“…In [12], [13], a threestage optimal power flow (OPF) framework where corrective control actions are used to ensure feasibility during worst-case combinations of contingencies and uncertainty was proposed, and was extended to the definition of optimal corrective control actions in [14]. The OPF formulation in [15] accounts for uncertainty and includes corrective control for a limited number of pre-selected, critical uncertainty scenarios. In [16], [17], post-contingency corrective control of power flow control devices such as HVDC and PSTs is applied within a chance constrained OPF (CC-OPF) framework, which ensures that the constraints will hold with a desired probability.…”

Section: Introductionmentioning

confidence: 99%

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

Roald

Misra

Krause

et al. 2016

IEEE Trans. Power Syst.

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Higher shares of electricity generation from renewable energy sources and market liberalization is increasing uncertainty in power systems operation. At the same time, operation is becoming more flexible with improved control systems and new technology such as phase shifting transformers (PSTs) and high voltage direct current connections (HVDC). Previous studies have shown that the use of corrective control in response to outages contributes to a reduction in operating cost, while maintaining N-1 security. In this work, we propose a method to extend the use of corrective control of PSTs and HVDCs to react to uncertainty. We characterize the uncertainty as continuous random variables, and define the corrective control actions through affine control policies. This allows us to efficiently model control reactions to a large number of uncertainty sources. The control policies are then included in a chance constrained optimal power flow formulation, which guarantees that the system constraints are enforced with a desired probability. By applying an analytical reformulation of the chance constraints, we obtain a second-order cone problem for which we develop an efficient solution algorithm. In a case study for the IEEE 118 bus system, we show that corrective control for uncertainty leads to a decrease in operational cost, while maintaining system security. Further, we demonstrate the scalability of the method by solving the problem for the IEEE 300 bus and the Polish system test cases.

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The impact of forecasting errors and remedial actions on operational security and efficiency in classical and probabilistic market coupling

Cited by 4 publications

References 13 publications

European electricity market simulation for future scenarios with high renewable energy production

European electricity market simulation for future scenarios with high renewable energy production

Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

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