Matti Koivisto

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.

Simulation of regional day-ahead PV power forecast scenarios

Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weather-driven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records.

Impacts of offshore grid developments in the North Sea region on market values by 2050: How will offshore wind farms and transmission lines pay?

Increasing the integration of renewable energy in Northern and Central Europe markets is greatly influenced by the development of electricity transmission grid infrastructure. On the background of the fast development of offshore wind energy and its connection to the onshore electricity systems, a coordinated grid development in the North Sea may not only save costs for individual wind farms, but also deliver additional benefits through the provision of increased interconnection of electricity markets. The previous studies do not include offshore wind development with high ambition in the long term perspective and do not focus on the assessment of the specific effects on the economic value of offshore wind farms connected to Belgium, Norway the UK, the Netherlands, and Germany (North Sea Link, Cobra Cable, Viking Link, Nord Link, BritNed and Nemo Link). This paper tries to shed some lights on the substantial differences in the expected economic exposure of wind power plants and transmission lines to the development of the electricity grid in the North Sea. Since details of the prospective energy system around the North Sea region shape these revenue expectations, we further develop and apply the energy model Balmorel. The tool is used to quantify effects of the implementation of a meshed offshore grid compared to a radial grid that connects wind farms in a non-coordinated fashion to the countries by 2050. The model runs conducted for the present paper show substantial variation of expectable market values of wind farms on hub level due to impacts of different options for grid structures. The results aim to inform the discussion on possibilities for the allocation of grid expansion costs to the different connected countries including Belgium, Denmark, Germany, the Netherlands, Norway and Britain.

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.

Large-scale wind generation simulations: From the analysis of current installations to modelling the future

Modelling and understanding variability in wind generation will be increasingly important in the future with growing shares of wind power in energy systems. Crucially, the modelling needs to be extended to future scenarios, also considering the expected technological development of installations. Reanalysis data is often used in large-scale simulations to model the variability in wind. Wind power plant (WPP) data is also required, but may be only partially available. In this paper, a methodology for estimating missing hub height data is presented, using multiple regression models and large WPP and turbine datasets. The resulting estimated hub heights are presented on a pan-European level, and a scenario with capacity factor development until 2050 for two example countries is shown in detail.

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.