Modelling Flow-based Market Coupling in the CORE region: A Data-driven and Interpretable Approach

Integrating renewables in the electricity system in a cost-efficient way requires massive transmission system investments and the efficient use of available transmission capacity. Markets are pivotal in the latter, especially in coordinating flows between countries. In the European Union, flow-based market coupling (FBMC) arose as the preferred...

A Modified Version of the IEEE 39-bus Test System for the Day-Ahead Market

Reaching net-zero emissions within the proposed time requires an enormous effort from the energy sector, and it is even more challenging for the electricity infrastructure. This article offers a modified version of the IEEE 39-bus system specifically created to allow zonal day-ahead market (ZDAM) simulations. The system representation is based...

Closed-loop simulation testing of a probabilistic DR framework for Day Ahead Market participation applied to Battery Energy Storage Systems

In this manuscript, we test the operational performance decrease of a probabilistic framework for Demand Response (DR). We use Day Ahead Market (DAM) price scenarios generated by a Combined Quantile Regression Deep Neural Network (CQR-DNN) and a Non-parametric Bayesian Network (NPBN) to maximise profit of a Battery Energy Storage System (BESS)...

Optimal Battery Energy Storage Dispatch in Energy and Frequency Regulation Markets While Peak Shaving an EV Fast Charging Station

Battery Energy Storage Systems typically procure their primary revenues from regulated energy and ancillary services markets; nonetheless, they have great potential in supporting distribution network operators and their users. This paper evaluates the potential business case of battery storage systems integrating market application and services...

Day Ahead Market price scenario generation using a Combined Quantile Regression Deep Neural Network and a Non-parametric Bayesian Network: A framework for risk-based Demand Response

In this manuscript we propose a methodology to generate electricity price scenarios from probabilistic forecasts. Using a Combined Quantile Regression Deep Neural Network, we forecast hourly marginal price distribution quantiles for the DAM on which we fit parametric distributions. A Non-parametric Bayesian Network (BN) is applied to sample from...

Security Constrained Unit Commitment and Economic Dispatch by AC Sensitivity Factors

The dispatch schedule of the electrical power plant units is the result of the solution to the unit commitment optimisation problem, and it minimises the cost of production considering pre-defined technical limits. The security-constrained unit commitment problem has been defined as including the network constraints in the previously mentioned...

Security Constrained Unit Commitment and Economic Dispatch applied to the Modified IEEE 39-bus system Case

The operation schedule of the power generation units in electrical power systems is determined by the optimisation problem known as unit commitment (UC), aiming at minimising the total cost considering the generation constraints. To obtain a feasible solution from the network perspective, the security-constrained UC (SCUC) problem has been...

Zonal Day-Ahead Energy Market: A Modified Version of the IEEE 39-bus Test System

The increasing penetration of renewable energy resources (RES) in transmission system operating conditions require a suitable test system and a dataset to cope with current issues. RES penetration remarkably affects day-ahead market outcomes regarding zonal prices and dispatched generation levels. For this purpose, zonal day-ahead energy market...

Optimization of Battery Energy Storage System Operation in Distribution Grids

Currently, the main revenue streams for grid connected BESS stem from regulated energy markets, such as Day Ahead Market (DAM) and Primary Frequency Regulation (PFR). Furthermore, BESS have great potential in providing services to network operators, however a clear business case for such functionalities is still missing. Therefore, this research...

Electricity price forecasting in European Day Ahead Markets: a greedy consideration of market integration

In this manuscript we explore European feature importance in Day Ahead Market (DAM) price forecasting models, and show that model performance can deteriorate when too many features are included due to over-fitting. We propose a greedy algorithm to search over candidate countries for European features to be used in a DAM price forecasting model,...

Probabilistic DAM price forecasting using a combined Quantile Regression Deep Neural Network with less-crossing quantiles

In this paper we propose a Quantile Regression Deep Neural Network capable of forecasting multiple quantiles in one model using a combined quantile loss function, and apply it to probabilistically forecast the prices of 8 European Day Ahead Markets. We show that the proposed loss function significantly reduces the quantile crossing problem to ...

Influence of the European Electricity Market on the System Design of Airborne Wind Energy: A method to quantify the merit order effect of wind power on the revenue generation of the systems

Airborne wind energy (AWE) is a new generation of wind energy technology which uses tethered kites to reach the stronger and steadier high altitude wind resource. Research and development of AWE has been accelerated in the last two decades. Around 40 institutions around the world are working on their own concepts and architectures of the...

Increasing the Market Value of Wind Power Using Improved Stochastic Process Modeling and Optimization: A Case Study of a Belgian Wind Power Producer

Wind Power Producers (WPP) incur a negative result through mandatory participation on the balancing market. This study analyzes the performance of multiple improved offering strategies for a WPP offering both on the day-ahead market and the balancing market. It does so on revenue, as well as its impact on the grid and electricity market as a...

Trading power instead of energy in day-ahead electricity markets

Day-ahead electricity markets are inefficient due to their coarse discretisation of time and their representation of electricity production and consumption in energy per time interval. This leads to excessive costs and infeasible schedules in the market clearing results. Some real-world systems have increased the resolution to improve accuracy,...

Optimal non-zero Price Bids for EVs in Energy and Reserves Markets using Stochastic Optimization

In power systems, demand and supply always have to be balanced. This is becoming more challenging due to the sustained penetration of renewable energy sources. Because of the increasing amount of electrical vehicles (EVs), and the high capacity and flexibility of their charging process, EVs are a good candidate for providing balancing services...

Impact of Wind Generation on Electricity Prices in Day Ahead Market

This master thesis aims at understanding the impact of wind on day-ahead-market (DAM) electricity prices using an integrated engineering-economic model. A realistic case study has been implemented, which is Electric Reliability Council of Texas (ERCOT) in Texas, U.S. in the month of August, 2014. To address the main research question of wind ?s...