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The main goal of generation expansion planning (GEP) and transmission expansion planning (TEP) is to expand the power system to satisfy the increasing demand of electricity while maintaining efficient operation of the system. The major objective of this study is to propose a dynamic, robust GEP–TEP expansion planning in the presence of wind farms considering both long‐ and short‐term uncertainties. The suggested model allows implementing information‐gap decision theory on multi‐year long‐term uncertainties, such as demand growth and future increase in production capacity to decrease the risk in long‐term decisions. Additionally, a scenario‐based approach is employed for short‐term uncertainties in demand and wind power production in a 1‐year time horizon. The main advantage of the proposed model is to enhance the power system robustness against the uncertainties corresponding to forecast errors. To verify the robustness of the suggested expansion planning model, it is applied to the Garver 6‐bus and IEEE 24‐bus test systems. [ABSTRACT FROM AUTHOR]

This study investigates optimal wind power generator bidding strategies in the real‐time electricity market. The goal is to maximise its operating profit by determining the optimal amount of wind power to bid in the real‐time market. A bi‐level stochastic optimisation model is proposed in which the upper‐level problem is minimising the negative profit of wind power producers, while the low‐level problem clears the real‐time market. The uncertainties in the wind power production, thermal power, hydro power, demand, and energy storage are considered in the stochastic model. This study utilises a mathematical programming problem with equilibrium constraints (MPEC) and Karush–Kuhn–Tucker (KKT) conditions to transform the bi‐level problem into an equivalent single‐level mixed‐integer linear problem (MILP). Case studies obtained with the IEEE RTS‐24 Bus system demonstrate the effectiveness of the proposed model and the effect of scenarios on the wind power producer's profit. [ABSTRACT FROM AUTHOR]

In highly polluted regions, it might be necessary to operate power plants enforcing appropriate emission limits to help ensuring air quality. If the air quality is low and pollutants are difficult to diffuse, few additional emissions should be allowed to avoid further deteriorating the air quality. Wind power helps reducing pollution levels as it substitutes polluting thermal production. However, wind and air pollution are generally anti‐correlated. Storage can be used to shift wind power production from hours when higher pollution levels are allowed to hours when lower pollution levels are allowed to help meeting emission constraints at critical hours. Storage availability also achieves a comparatively lower production cost for the system as a whole. To represent the stochastic nature of wind power production and weather‐dependent emission limits, the authors propose a two‐stage stochastic programming model to analyse the trade‐off emission versus cost in an electric energy system with storage facilities. The first stage represents the day‐ahead scheduling, while the second one represents the real‐time operation under different scenarios. The authors analyse the impact of emission limits and/or storage on generation scheduling by comparing different models using an illustrative example and a case study based on the IEEE 118‐node system. [ABSTRACT FROM AUTHOR]

This study proposes a methodology to assess the effect of wind power production on the flexibility requirements of a power system. First, the study describes the probabilistic characteristics of the intra‐hour net load variability and demonstrates that they are best captured by non‐parametric statistics. Then, this non‐parametric approach is used to determine simultaneously the hourly flexibility requirements at a given probability level for large and small, continuous and discrete disturbances. This approach allocates the required flexibility among primary, secondary and tertiary regulation intervals. The usefulness of this method is then illustrated using actual 1 min resolution net load data, which has been clustered to take advantage of seasonal and daily differences in flexibility requirements. [ABSTRACT FROM AUTHOR]