4.7 Article

An Adjustable Scenario Optimisation Approach in Operating PV-Rich Distribution Systems

Journal

IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 38, Issue 5, Pages 4095-4106

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2022.3212406

Keywords

Distribution systems; DER integration; scenario optimisation approach; adjustable robust counterpart

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In this paper, an adjustable scenario optimisation-based solution approach is proposed to maximize the usage of distributed energy resources (DER) generation while ensuring network security. Extensive comparisons and simulations show that the proposed approach outperforms conventional scenario optimisation and adjustable robust approaches in terms of performance.
The embrace of intermittent distributed energy resources (DER) on the demand side increases the concerns around the safe operation of distribution networks. Existing solutions either neglect the available information around the distribution of uncertain parameters or underestimate DER capability in providing real-time recourse actions. In this paper, we propose an adjustable scenario optimisation-based solution approach that maximises the usage of DER generation within the operation horizon, while providing the required level of network security. Our modelling i) guarantees hard constraints satisfaction, ii) allows fast-acting devices, e.g., inverters, to adjust in response to live realisations, and iii) enables the operators to maintain the soft constraints with a required probability using a joint chance-constrained (JCC) program. We reformulate the JCCs using the scenario optimisation approach that directly takes empirical data and provides a pre-specified bound on the risk of constraint violation. To increase the scalability of our proposal, we introduce an approximation of JCCs to be used within the scenario optimisation approach. Through extensive comparisons and simulations, we show that our approach outperforms the conventional scenario optimisation and adjustable robust approaches respectively by 83% and 80% in our experiments.

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