Quantifying the predictability of renewable energy data for improving power systems decision-making

Published: March 24, 2023Decision-making in the power systems domain often relies on predictions of renewable generation. While sophisticated forecasting methods have been developed to improve the accuracy of such predictions, their accuracy is limited by the inherent predictability of the data used. However, the predictability of time series data cannot be measured by existing prediction techniques. This important measure has been overlooked by researchers and practitioners in the power systems domain. In this paper, we systematically assess the suitability of various predictability measures for renewable generation time series data, revealing the best method and providing instructions for tuning it. Using real-world examples, we then illustrate how predictability could save end users and investors millions of dollars in the electricity sector.Sahand Karimi-Arpanahi, S. Ali Pourmousavi, and Nariman Mahdav

Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes

Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal usage of network assets. Yet, existing DR models often overlook a crucial element, the irrational behaviour of electricity end users. In this work, we propose a price-responsive model that incorporates key aspects of end-user irrationality, specifically loss aversion, time inconsistency, and bounded rationality. To this end, we first develop a framework that uses Multiple Seasonal-Trend decomposition using Loess (MSTL) and non-stationary Gaussian processes to model the randomness in the electricity consumption by residential consumers. The impact of this model is then evaluated through a community battery storage (CBS) business model. Additionally, we propose a chance-constrained optimisation model for CBS operation that deals with the unpredictability of the end-user irrationality. Our simulations using real-world data show that the proposed DR model provides a more realistic estimate of price-responsive behaviour considering irrationality. Compared to a deterministic model that cannot fully take into account the irrational behaviour of end users, the chance-constrained CBS operation model yields an additional 19% revenue. In addition, the business model reduces the electricity costs of end users with a rooftop solar system by 11%.Comment: This manuscript has been submitted to IEEE Transactions on Smart Grid for possible publicatio

Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy

The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.Sahand Karimi-Arpanahi, S. Ali Pourmousav

Leveraging the flexibility of electric vehicle parking lots in distribution networks with high renewable penetration

Publisher Copyright: © 2022 Elsevier LtdThe ongoing rapid increase in the integration of variable and uncertain renewable energy sources calls for enhancing the ways of providing flexibility to power grids. To this end, we propose an optimal approach for utilizing electric vehicle parking lots to provide flexibility at the distribution level. Accordingly, we present a day-ahead scheduling model for distribution system operators, where they can offer discounts on the network tariff to electric vehicle parking lot operators. This way, they will be encouraged to exploit the potential flexibility of electric vehicle batteries to assist in alleviating the steep ramps of system net-load. To determine the optimal discounts, the distribution system operator minimizes the network operating costs considering the network operational constraints, while the electric vehicle parking lot operators try to maximize their profits. Due to the contradictory objectives and decision hierarchy, the problem is an instance of Stackelberg games and can be formulated as a bi-level program, which is linearized and converted to a single-level mixed-integer linear program using strong-duality theorem and Karush–Kuhn–Tucker conditions. To validate the proposed model, comprehensive simulation studies are performed on a test distribution network. The simulation results show that implementing the model can reduce the peak-off-peak difference and peak-to-average ratio of the network net-load by up to 15% and 24%, respectively.Peer reviewe

Cost-Effective Community Battery Sizing and Operation Within a Local Market Framework

Extreme peak power demand is amajor factor behind high electricity prices, despite occurring only for a few hours annually. This peak demand drives the need for costly upgrades for the network asset, which is ultimately passed on to the end-users through higher electricity network tariffs. To alleviate this issue, we propose a solution for cost-effective peak demand reduction in a local neighbourhood using prosumer-centric flexibility and community battery storage (CBS). Accordingly, we present a CBS sizing framework for peak demand reduction considering receding horizon operation and a bilevel program in which a profit-making entity (leader) operates the CBS and dynamically sets mark-up prices. Through the dynamic mark-up and real-time wholesale market prices, the CBS operator can harness the demand-side flexibility provided by the load-shifting behaviour of the local prosumers (followers). To this end, we develop a realistic priceresponsive model that adjusts prosumers’ behaviour with respect to fluctuations of dynamic prices while considering prosumers’ discomfort caused by load shifting. The simulation results based on real-world data show that adopting the proposed framework and the price-responsive model not only increases the CBS owner’s profit but also reduces peak demand and prosumers’ electricity bills by 38% and 24%, respectively.Nam Trong Dinh, Sahand Karimi-Arpanahi, S. Ali Pourmousavi, Mingyu Guo, and Jon Anders Reichert Liisber