By employing the Open Energy Modelling Framework (Oemof), the Risk Manager identifies the optimal mix of resources in a multi-objective optimisation model. This methodology encompasses modelling complex factors such as renewable generation sources, demand profiles, grid interconnection constraints, markets pricing (e.g. Day-ahead Market), and investments, including storage systems. Monte Carlo simulations are conducted to address variability and generate probability density functions, providing valuable insights into profit ranges and the quantification of risk via 3D probability density functions, visual dashboard animations and dynamic box and whisker diagrams.
With the primary entities in a trader's portfolio being the Independent Power Producers (IPPs) and prospective consumption Power Purchase Agreements (PPAs), the Risk Manager outputs critical information for each new customer onboarded, offering price ranges per contract to facilitate negotiations. The tool incorporates the existing portfolio, including price escalations and uncertainties related to IPP production variability and expected fluctuations from customers. Non-linear optimisers within multi-year simulation models generate different probability distributions as outputs, allowing for the quantification of Value at Risk (VaR), Profit at Risk (PaR), and Cash Flow at Risk (CFaR) across various trading strategies and risk levels.
By considering uncertainties and running simulations, the Risk Manager provides traders with valuable insights into contract pricing and risk management. It enables informed decision-making during negotiations and adjustments to the portfolio.
Portfolio optimisation, a widely used technique in finance and economics, is leveraged to achieve the maximum return on distributed resources, which in the power systems context translates to amounts of electricity distributed among different markets. Typically, portfolio optimisation maximises profit while also considering the associated level of risk.
In the context of electricity trading, profits earned by various players are influenced by numerous uncertain factors, such as other players' strategies, transmission congestion, and fluctuating levels of demand and supply. These uncertainties introduce risks into electricity pricing. This is primarily due to the unique characteristic of non-mass storage of electricity, where the availability and delivery of electricity are subject to various uncontrollable factors.
The Risk Manager addresses this challenge by approaching the problem as a multi-objective optimisation. It seeks to maximise profits for traders (or achieve savings for customers) while simultaneously minimising risk. This perspective allows for a comprehensive consideration of both profit maximisation and risk reduction, enabling traders to negotiate in multiple alternative and complementary ways.
In conclusion, the Risk Manager provides traders with a robust and comprehensive solution for managing portfolio risks, optimising trading strategies, and making informed decisions.
By incorporating uncertainties, conducting simulations, and considering multi-objective optimisation, the Risk Manager empowers traders to navigate the dynamic electricity trading landscape with greater efficiency, profitability, and risk management capabilities.
