Electric Power Systems Research

Journal: Electric Power Systems Research

Abbreviation

Electr. power syst. res.

Publisher

Elsevier

ISSN

0378-7796
1873-2046

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Publications 1 - 10 of 43

Battery control with lookahead constraints in distribution grids using reinforcement learning
da Silva André, Joel; Stai, Eleni; Stanojev, Ognjen; et al. (2022)
Electric Power Systems Research
In this paper, a computationally efficient real-time control of a battery with lookahead state-of-energy constraints in active distribution grids with distributed energy sources is presented. The goal is to follow a previously computed dispatch plan or to optimize a monetary cost from buying and selling power at the point of common coupling. However, the lookahead constraints render the battery decisions non-trivial. The current practice in literature to solve this problem is Model Predictive Control (MPC), which does not scale for large grids. Instead, here, we propose a reinforcement learning approach based on the Deep Deterministic Policy Gradient (DDPG) algorithm. To satisfy the lookahead battery constraints we adapt the experience replay technique used in DDPG. To guarantee the satisfaction of the hard grid constraints, we introduce a safety layer that performs constrained optimization. Our approach does not need forecasts contrary to MPC. We perform evaluations on a realistic grid and comparisons with Lyapunov optimization and MPC. We show that we can achieve costs close to MPC and Lyapunov, while reducing the computational time by multiple orders of magnitude.
Reduced-order modeling of inverter-based generation using hybrid singular perturbation
Caduff, Ivo; Markovic, Uros; Roberts, Ciaran; et al. (2021)
Electric Power Systems Research
© 2020 Elsevier B.V. This paper contributes to the field of inverter modelling for large-scale simulations by introducing a novel Model-Order Reduction (MOR) method based on singular perturbation. Motivated by the timescale separation between the fast and slow dynamics in an inverter-based power system, the proposed nonlinear MOR concept extends on the existing zero- and first-order reduction methods by combining the low computational burden of the former approach with the higher accuracy of the latter one. As a result, such hybrid MOR technique preserves the slow system dynamics of the full-order model, while simultaneously capturing the impact of the removed fast states on slow variables. Moreover, we introduce several improvements to the existing first-order MOR in order to make it tractable and more efficient when applied to a realistic full-order inverter model. The novel hybrid approach is applied to both grid-forming and grid-following inverter control schemes, and compared against existing reduction methods from the literature. The results showcase a better time-domain performance of the hybrid method during transients, while having a negligible increase in computational requirements compared to the traditional zero-order approach.
Power grid parameter estimation without phase measurements: Theory and empirical validation
Brouillon, Jean-Sébastien; Moffat, Keith; Dörfler, Florian; et al. (2024)
Electric Power Systems Research
Reliable integration and operation of renewable distributed energy resources requires accurate distribution grid models. However, obtaining precise models by field inspection is often prohibitively expensive, given their large scale and the ongoing nature of grid operations. To address this challenge, considerable efforts have been devoted to harnessing abundant consumption data for automatic model inference. The primary result of the paper is that, while the impedance of a line or a network can be estimated without synchronized phase angle measurements in a consistent way, the admittance cannot. Furthermore, a detailed statistical analysis is presented, quantifying the expected estimation errors of four prevalent admittance estimation methods. Such errors constitute fundamental model inference limitations that cannot be resolved with more data. These findings are empirically validated using synthetic data and real measurements from the town of Walenstadt, Switzerland, confirming the theory. The results contribute to our understanding of grid estimation limitations and uncertainties, offering guidance for both practitioners and researchers in the pursuit of more reliable and cost-effective solutions.
Aggregated models of active distribution networks for stability studies of large transmission systems
Fuchs, Alexander; Demiray, Turhan Hilmi; Larsson, Mats (2022)
Electric Power Systems Research
The paper presents a new framework to quantify the amount of dynamic grid support that should be provided from converter-based energy resources replacing generation from synchronous machines. A tuning approach for local stability issues in active distribution networks (ADN) is presented, followed by an aggregation procedure to derive adequate low-order ADN models. The resulting models from benchmark systems with varying shares of grid-forming converter-based generation up to 100% of the distribution grid load are published along with the paper. They allow an adequate representation of ADNs for the assessment of voltage and frequency performance in large-scale transmission grid simulations. A quantitative study of the full continental ENTSO-E system investigates a large disturbance in the form of a system split for multiple development paths of converters replacing synchronous machines. It is found that about 10%–20% of the new converters should have grid-support capabilities to maintain the dynamic power system performance at today's level.
Stochastic modeling for the aggregated flexibility of distributed energy resources
Wen, Yilin; Guo, Yi; Hu, Zechun; et al. (2024)
Electric Power Systems Research
This paper proposes an uncertainty modeling method for the aggregated power flexibility of DERs. Basically, both outer and inner approximated power-energy boundary models are utilized to describe the aggregated flexibility of controllable DERs. These power and energy boundary parameters are uncertain because the availability of controllable devices, such as electric vehicles and thermostatically controlled loads, cannot be precisely predicted. The optimal operation problem of the aggregator is thus formulated as chance-constrained programming (CCP). Then, a flexibility envelope searching algorithm based on the ALSO-X+ method is proposed to solve the CCP, the result of which is a conservative approximation of the original CCP but not as conservative as the Conditional Value-at-Risk approximation. After optimizing the aggregated power of the group of DERs, the decision at the aggregator level is disaggregated into the flexibility regions of individual DERs. Finally, the numerical test demonstrates the effectiveness and robustness of the proposed method.
Modeling load forecast uncertainty using generative adversarial networks
Wang, Yi; Hug, Gabriela; Liu, Zijie; et al. (2020)
Electric Power Systems Research
The integration of distributed energy resources (DER) increase the uncertainty of the load. Probabilistic load forecasting (PLF) is able to model these uncertainties in the form of quantile, interval, or density. However, the uncertainties are usually given individually for every single period which fails to capture the temporal variations across periods. Therefore, this paper proposes a generative adversarial network (GAN)-based scenario generation approach to model both the uncertainties and the variations of the load. Specifically, point forecasting is first conducted and the corresponding residuals are calculated. On this basis, a conditional GAN model is designed and trained. Then, the well-trained GAN model generates residual scenarios that are conditional on the day type, temperatures, and historical loads. Finally, the effectiveness of the uncertainty modeling by the generated scenarios is evaluated from different perspectives. Case studies on open datasets verify the effectiveness and superiority of the proposed method. © 2020 Elsevier B.V.
Real-time coordination of integrated transmission and distribution systems: Flexibility modeling and distributed NMPC scheduling
Dai, Xinliang; Guo, Yi; Jiang, Yuning; et al. (2024)
Electric Power Systems Research
This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all controllable devices by power-energy envelopes and provides them to the transmission system operator (TSO). At the transmission system level, a distributed nonlinear model predictive control (NMPC) approach is proposed to coordinate the economic dispatch of multiple TSOs, considering the aggregated flexibility of all distribution systems. The subproblems of the proposed approach are associated with different TSOs and individual time periods. In addition, the aggregated flexibility of controllable devices in distribution networks is encapsulated, re-calculated, and communicated through the power-energy envelopes, facilitating a reduction in computational complexity and eliminating redundant information exchanges between TSOs and DSOs, thereby enhancing privacy and security. The framework's effectiveness and applicability in real-world scenarios are validated through simulated operational scenarios on a summer day in Germany, highlighting its robustness in the face of significant prediction mismatches due to severe weather conditions.
Saturation-informed current-limiting control for grid-forming converters
Desai, Maitraya Avadhut; He, Xiuqiang; Huang, Linbin; et al. (2024)
Electric Power Systems Research
In this paper, we investigate the transient stability of a state-of-the-art grid-forming complex-droop control (i.e., dispatchable virtual oscillator control, dVOC) under current saturation. We quantify the saturation level of a converter by introducing the concept of degree of saturation (DoS), and we propose a provably stable current-limiting control with saturation-informed feedback, which feeds the degree of saturation back to the inner voltage-control loop and the outer grid-forming loop. As a result, although the output current is saturated, the voltage phase angle can still be generated from an internal virtual voltage-source node that is governed by an equivalent complex-droop control. We prove that the proposed control achieves transient stability during current saturation under grid faults. We also provide parametric stability conditions for multi-converter systems under grid-connected and islanded scenarios. The stability performance of the current-limiting control is validated with various case studies.
Privacy-preserving distributed market mechanism for active distribution networks
Franke, Matthias; Stanojev, Ognjen; Mitridati, Lesia; et al. (2024)
Electric Power Systems Research
Amidst the worldwide efforts to decarbonize power networks, Local Electricity Markets (LEMs) in distribution networks are gaining importance due to the increased adoption of renewable energy sources and prosumers. Considering that LEMs involve data exchange among independent entities, privacy and cybersecurity are some of the main practical challenges in LEM design. This paper proposes a secure market protocol using innovations from distributed optimization and Secure MultiParty Computation (SMPC). The considered LEM is formulated as an uncertainty-aware joint market for energy and reserves with affine balancing policies. To achieve scalability and enable the use of SMPC, market clearing is solved using the Consensus ADMM algorithm. Subsequently, the data exchange among participants via ADMM iterations is protected using the Shamir secret-sharing scheme to ensure privacy. The market protocol is further reinforced by a secure and verifiable settlement process that uses SMPC and ElGamal commitments to verify market quantities and by a secure recovery scheme for missing network measurements. Finally, the feasibility and performance of the proposed LEM are evaluated on a 15-bus test network.
Introducing price feedback of local flexibility markets into distribution network planning
Lustenberger, Michael; Bellizio, Federica; Cai, Hanmin; et al. (2024)
Electric Power Systems Research
Increasing penetration of distributed energy resources in distribution networks is expected to cause congestion in the near future. While grid reinforcement is the standard approach to resolve such issues, utilizing demand-side flexibility provides a viable and cost-efficient alternative. In this work, we propose a decision making tool for distribution system operators that allows them to integrate flexibility procurement from local flexibility markets in their planning process. The tool estimates the future cost of procuring flexibility services and compares them to alternative measures such as grid reinforcement. Long time horizons are considered, as the lead time of grid reinforcement projects can be several years. Price feedback from local flexibility markets is used to calibrate and improve the estimations. Results indicate that the proposed tool can be used to estimate flexibility service cost over several years, aiding distribution system operators in defining a cost-efficient long-term distribution network development strategy.

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