Jussi Ekström

Statistical analysis of large scale wind power generation using Monte Carlo Simulations

As more wind power generation is installed, the effect of wind power on the electric power system is becoming increasingly important. This paper presents two time series models that can be used in Monte Carlo simulations to assess the risk of very high or low wind speeds occurring contemporaneously in multiple locations. The suitability of the models is assessed for existing measured locations and new non-measured locations. The simulation results are verified against measurements from 19 locations from Finland. Also, an example scenario is given to show the effect of geographical spread on the aggregate power generation of multiple wind power generation units.

A Statistical Model for Hourly Large-Scale Wind and Photovoltaic Generation in New Locations

The analysis of large-scale wind and photovoltaic (PV) energy generation is of vital importance in power systems, where their penetration is high. This paper presents a modular methodology to assess the power generation and volatility of a system consisting of both PV plants (PVPs) and wind power plants (WPPs) in new locations. The methodology is based on statistical modeling of PV and WPP locations with a vector autoregressive model, which takes into account both the temporal correlations in individual plants and the spatial correlations between the plants. The spatial correlations are linked through distances between the locations, which allow the methodology to be used to assess scenarios with PVPs and WPPs in multiple locations without actual measurement data. The methodology can be applied by the transmission and distribution system operators when analyzing the effects and feasibility of new PVPs and WPPs in system planning. The model is verified against hourly measured wind speed and solar irradiance data from Finland. A case study assessing the impact of the geographical distribution of the PVPs and WPPs on aggregate power generation and its variability is presented.

Minimizing Variance in Variable Renewable Energy Generation in Northern Europe

The growing installations of variable renewable energy (VRE) sources, which are driven by weather patterns, can cause challenges to the operation and planning of power systems. This paper minimizes the variance of aggregate VRE generation based on the amount of different VRE technology types installed in different countries over a large geographical area. A mixture of offshore and onshore wind, and solar photovoltaic generation is considered. In the presented case study in Northern Europe, the optimized scenario provides a doubling of the expected annual VRE energy with a much lower increase in the aggregate VRE generation variability compared to other scenarios. The optimized scenario shows clearly the benefit of having a mixture of different VRE technologies with geographically highly spread installations.

Probabilistic prosumer node modeling for estimating planning parameters in distribution networks with renewable energy sources

With the increase in distributed generation, the demand-only nature of many secondary substation nodes in medium voltage networks is becoming a mix of temporally varying consumption and generation with significant stochastic components. Traditional planning, however, has often assumed that the maximum demands of all connected substations are fully coincident, and in cases where there is local generation, the conditions of maximum consumption and minimum generation, and maximum generation and minimum consumption are checked, again assuming unity coincidence. Statistical modelling is used in this paper to produce network solutions that optimize investment, running and interruption costs, assessed from a societal perspective. The decoupled utilization of expected consumption profiles and stochastic generation models enables a more detailed estimation of the driving parameters using the Monte Carlo simulation method. A planning algorithm that optimally places backup connections and three layers of switching has, for real-scale distribution networks, to make millions of iterations within iterations to form a solution, and therefore cannot computationally afford millions of parallel load flows in each iteration. The interface that decouples the full statistical modelling of the combinatorial challenge of prosumer nodes with such a planning algorithm is the main offering of this paper.

Assessing the upward demand response potential for mitigating the wind generation curtailment: A case study

The increased penetration of intermittent renewable generation has already resulted in spilling and it is projected that renewable energy curtailment level will continue to soar. This paper presents a framework to assess the flexibility of domestic thermal loads and Electric vehicles (EVs) charging load for power sink as a means to reduce wind energy curtailment during different times of a year. The objective of the framework is to jointly optimize the flexible loads to mitigate the curtailment thereby increasing the utilization of intermittent renewable generation. The proposed model is applied to the Finnish power system. The simulation results suggested that the proper activation of demand response (DR) is a feasible curtailment mitigation option but with an important caveat that potential subdued as the renewable penetration increases in the system.