A stand-alone simulation game for the participation of wind producers in day-ahead electricity markets

This paper presents an instance of a web-based stand-alone simulation game aimed at teaching electricity market subjects. The main objectives of this version of the electricity markets game (EmGA) platform are to introduce players/students to the short-term electricity market structure and to highlight the importance of forecasting tools in decision-making. A hypothetical situation has been carefully designed using historical data, where each player takes the role of a wind farm owner willing to participate in the day-ahead electricity market to maximize its economic profit. Players are asked to submit hourly energy bids which are then compared to the wind farm's actual energy production and mapped to revenue by basic market rules for price-taker participants. Preliminary student feedback reinforces the potential of the EmGA platform for improving the teaching of electricity market subjects, showing a general acceptance and a perception of improved knowledge reception of more than 77%

Hybrid local electricity market designs with distributed and hierarchical structures

With the increasing penetration of distributed energy resources (DERs), prosumers are becoming more active and consumer-centric electricity market designs are needed at the distribution grid level. In order to co-optimize the local energy source management of the prosumers and the energy trading between them, an integrated hybrid local electricity market (LEM), which combines peer-to-peer (P2P) markets and community-based markets, is designed. Moreover, the integrated LEMs are decomposed into two market structures, namely the distributed structure and the hierarchical structure, leveraging the alternating direction method of multipliers (ADMM). In the distributed structure, the integrated hybrid LEM is decomposed into multiple blocks by agents who coordinate a community-based market and negotiate with other agents for the P2P trading. In the hierarchical structure, the integrated hybrid LEM is decomposed into two blocks by markets with a centralized P2P market and several community-based markets. Simulation results suggest that the hierarchical structure converges to the optimal result for both the locally adaptive ADMM (LA-ADMM) and constant penalty factor ADMM with various values. The distributed structure achieves a distributed P2P market, but it only converges with LA-ADMM and exhibits a substantially higher computational burden than the hierarchical structure due to its multi-block nature

A Class-Driven Approach Based on Long Short-Term Memory Networks for Electricity Price Scenario Generation and Reduction

Uncertainty characterization is an essential component of decision-making problems in electricity markets. In this work, a class-driven approach is proposed to describe stochasticity. The methodology consists of a three-step process that includes a class allocation component, a generative element based on a long short-term memory neural network and an automated reduction method with a variance-based continuation criterion. The system is employed and evaluated on Dutch imbalance market prices. Test results are presented, expressing the proficiency of the approach, both in generating realistic scenario sets that reflect the erratic dynamics in the data and adequately reducing generated sets without the need to explicitly and manually predetermine the cardinality of the reduced set

Introducing technical indicators to electricity price forecasting: a feature engineering study for linear, ensemble, and deep machine learning models

Day-ahead electricity market (DAM) volatility and price forecast errors have grown in recent years. Changing market conditions, epitomised by increasing renewable energy production and rising intraday market trading, have spurred this growth. If forecast accuracies of DAM prices are to improve, new features capable of capturing the effects of technical or fundamental price drivers must be identified. In this paper, we focus on identifying/engineering technical features capable of capturing the behavioural biases of DAM traders. Technical indicators (TIs), such as Bollinger Bands, Momentum indicators, or exponential moving averages, are widely used across financial markets to identify behavioural biases. To date, TIs have never been applied to the forecasting of DAM prices. We demonstrate how the simple inclusion of TI features in DAM forecasting can significantly boost the regression accuracies of machine learning models; reducing the root mean squared errors of linear, ensemble, and deep model forecasts by up to 4.50%, 5.42%, and 4.09%, respectively. Moreover, tailored TIs are identified for each of these models, highlighting the added explanatory power offered by technical features

Performance Comparison of two Market Algorithms for Providing Support Services to Distribution Systems

Several market algorithms, such as Fast Locational Marginal Pricing (FLMP) and Receding Horizon Control (RHC), have shown the ability to cope with grid congestion. The RHC algorithm can cope with fluctuations in power consumption and generation but is computationally intensive due to the larger search space within the time horizon. The FLMP algorithm is not complex but does not utilize a horizon with information on future consumption and generation. This paper demonstrates a performance comparison between the FLMP and Horizon Marginal Pricing (HMP) algorithm. HMP is an algorithm that combines the simplicity of the FLMP algorithm with the time horizon possibilities of the RHC algorithm. The HMP algorithm uses the predicted generation profile to adjust the bid curves, such that consumption shifts to moments with more renewable generation available. Simulations are carried out to compare the performance between the HMP and FLMP algorithms. The results show comparable performance between the FLMP and HMP, whilst the FLMP computes the simulation faster and requires less bandwidth

A stand-alone simulation game for the participation of wind producers in day-ahead electricity markets

This paper presents an instance of a web-based stand-alone simulation game aimed at teaching electricity market subjects. The main objectives of this version of the electricity markets game (EmGA) platform are to introduce players/students to the short-term electricity market structure and to highlight the importance of forecasting tools in decision-making. A hypothetical situation has been carefully designed using historical data, where each player takes the role of a wind farm owner willing to participate in the day-ahead electricity market to maximize its economic profit. Players are asked to submit hourly energy bids which are then compared to the wind farm's actual energy production and mapped to revenue by basic market rules for price-taker participants. Preliminary student feedback reinforces the potential of the EmGA platform for improving the teaching of electricity market subjects, showing a general acceptance and a perception of improved knowledge reception of more than 77%