As the penetration of intermittent, low carbon generation increases, system inertia will decrease dramatically and pose a major threat to system stability. The majority of research in this area investigates the methods of providing inertia from the supply side, but this paper develops a novel method that will evaluate the demand side contributions to system inertia based on historical data of frequency outage events in the GB power system. This is achieved by isolating the generation contribution from the total system inertia and considering the power/frequency ratio as an indicator of the additional inertia contribution from spinning reserve. The results show that the demand side could contribute an average of 1.75s inertia constant in the system load base, representing 20% of the total system inertia in the GB power system. Finally, the system inertia distribution, the rate of change of frequency (ROCOF), the minimum primary frequency response requirements and the variable frequency drive (VFD) penetrations are investigated at a variety of demand levels.

Wind power trading in pool-based electricity markets is a decision-making problem and is generally modeled using a multi-stage stochastic programming approach because of the implicit uncertainty of wind input. In any stochastic programming approach, representation of random input process is a major issue. Due to uncertainty in wind availability, generated power by wind turbines is stochastic and is represented by possible values with corresponding probability of occurrence or scenarios. Accurate representation of uncertainty generally requires the consideration of large number of scenarios, thus necessitating the need for scenario-reduction techniques. This article presents simplified algorithms for wind power scenario generation and reduction. A time series based auto regressive moving average model is used for scenario generation, and probability distance based backward reduction is used for scenario reduction. The algorithms have been implemented for next-day scenario generation of wind farm located at Barnstable, Massachusetts, USA. The results prove the ability of the proposed algorithms in wind uncertainty modeling. These algorithms can successfully be utilized to generate optimal wind power bids for trading in electricity markets.

Wind and solar have major share among the growing renewable penetration, due to their extensive availability and improved technologies. Both wind and solar generation are highly uncertain and intermittent as compared to system load. The increased number of such uncertain and intermittent variables necessitates complex multivariate operational strategies for system operation. A compilation of different uncertain and intermittent variables such as load, wind and solar generation, to a single uncertain variable called net load, reduces system operational planning complexity. Net load is the difference between total load and renewable generation. Thus, conventional generation units have to be scheduled for net load. Prior knowledge about net load can help optimum operational planning such as generation scheduling and power system flexibility estimations. There have been significant advancement in load and renewable generation forecasting over the last decades. Still, there is little attention towards net load forecasting (NLF). This study proposes a novel NLF model using Gumbel copula based joint probability distribution for load, wind and solar generation forecasting error aggregation. Gumbel copula covers all extreme forecasting errors due to max‐stable property. Proposed model uses modified Grey index models for forecasting. Results show that proposed model has strong potential in very short‐term NLF.