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Active load shifting of the electricity demand unlocks a variety of benefits. Examples of such advantages include the increased stability of energy systems, reduced electricity costs and financial savings in the transmission as well as generation infrastructure. Although the technology necessary for demand response has been extensively studied for individual appliances or at the micro-grid level, evaluations of its nationwide impact are scarce. Yet governments and policy-makers require quantitative assessments in order to understand the underlying value and derive appropriate policies. For this purpose, this paper utilizes real-world data from the German-Austrian electricity market in order to calculate ex post the impact of demand response on electricity spot prices and load. As a result, we find that a 25% adoption rate of the available potential for load shifting could have decreased nationwide electricity expenses by approximately €500 million, or 6%, in 2014. At the same time, we observe that the price volatility rises under this scheme and thus impairs non-flexible electricity customers. This observation entails significant implications in terms of designing effective policies. [ABSTRACT FROM AUTHOR]

• A novel CTSGAN deep learning framework is first proposed and applied to model day-ahead electricity price scenarios. • The CTSGAN point forecasting model can eventually be transformed into a probabilistic forecasting model by enhancing the diversity of random input. • The CTSGAN probabilistic forecasting model can directly yield high-quality forecasting intervals with different coverage probabilities as a multi-objective forecasting model. • PSO-based optimal conditions selection method can increase the forecasting accuracy. Electricity prices in spot markets are volatile and can be affected by various factors, such as generation and demand, system contingencies, local weather patterns, bidding strategies of market participants, and uncertain renewable energy outputs. Because of these factors, electricity price forecasting is challenging. This paper proposes a scenario modeling approach to improve forecasting accuracy, conditioning time series generative adversarial networks on external factors. After data pre-processing and condition selection, a conditional TSGAN or CTSGAN is designed to forecast electricity prices. Wasserstein Distance, weights limitation, and RMSProp optimizer are used to ensure that the CTGAN training process is stable. By changing the dimensionality of random noise input, the point forecasting model can be transformed into a probabilistic forecasting model. For electricity price point forecasting, the proposed CTSGAN model has better accuracy and has better generalization ability than the TSGAN and other deep learning methods. For probabilistic forecasting, the proposed CTSGAN model can significantly improve the continuously ranked probability score and Winkler score. The effectiveness and superiority of the proposed CTSGAN forecasting model are verified by case studies. [ABSTRACT FROM AUTHOR]