Synergetic urbanism: A theoretical exploration of a vertical farm as local heat source and flexible electricity user

The urban energy transition requires innovative heating and cooling systems, as well as enhanced flexibility in electricity usage. This paper explores the theoretical potential for vertical farms to contribute to the energy transition by supplying residual heat to local district heat networks and flexible electricity usage. A stepped approach...

Modelling and optimization of bifacial PV farm: Potential of E/W vertical farm configuration

Solar photovoltaic (PV) has seen the most rapid growth among the renewable energy sources in the last decade. The market share of bifacial PV modules is expected to rise up to 70% in 2033. Such technology enables different farm configurations; however, it introduces complexities during the modelling phase, particularly concerning the estimation...

The effect of flow-based market coupling on cross-border exchange volumes and price convergence in Central Western European electricity markets

Since 2015 available cross-border transmission capacity is determined using flow-based market coupling (FBMC) in the day-ahead electricity markets of Central Western Europe. This paper empirically estimates the effect of introducing FBMC on day-ahead electricity price convergence and cross-border exchange volumes. In the month following the...

Back to the Future: Projecting Dutch Daily Day-ahead Market Price Series Under 2050 Energy Scenarios

Under the increasing electrification of end uses in the energy transition towards more renewable integration, the electricity price keeps gaining importance on every scale from individual well-being to the competitiveness of an economy. Though scarce in the scientific literature, Long-Term Electricity Price Projection (LEPP) has great potentials...

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...

Generating Electricity Price Forecasting Scenarios to Analyze Whether Price Uncertainty Impacts Tariff Performance

A higher share of renewables and electric vehicles increase the risk of congestion in electricity distribution systems. New distribution tariff designs have been proposed to prevent congestion. However, most modeling of tariff performance assumes deterministic price information. This paper proposes a method to assess the impact of price...

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The...

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 ...

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,...

The potential of price-driven flexible electricity demand and its effect on the electricity price in the Netherlands

Renewable energy sources like wind and solar, are intermittent, which means demand will not always match supply causing volatile and sometimes even negative electricity prices. For power companies it will be valuable to invest in technologies that can respond to those prices, which will result in an increasing flexibility of demand. However, to...

Valuation of electricity storage contracts based on the COS method: with underlying polynomial electricity prices

In this thesis we introduce valuation techniques to price electricity storage contracts, where the electricity prices follow a structural model based on polynomial processes. In particular we focus on a Fourier-based pricing method known as the COS method, which performs impressively to price the contracts accurately. We provide details on how...

Hybrid boiler systems in the Dutch industry: A techno-economic analysis of the potential of hybrid boiler systems to cost-effectively decarbonise steam generation in the Dutch industry

The Netherlands aims to accelerate the energy transition. Accordingly, ambitious targets have been set for the industrial sector. This will require additional investments in the Dutch industry which is expected to reduce the CO2 emissions at limited costs in comparison with other sectors. However, the ambition to reduce the emissions can create...

Accelerating the Energy Transition: The Renewable Energy Business Environment in the top-7 Emitting Countries

The current energy infrastructure, energy policy, and the business environment are designed to support the conventional power system. Hence, it is crucial to create a conducive business environment to accelerate the deployment and power generation through solar PV and wind power. This research has adapted the theory of the business environment...

Safe and effective application of ammonia as a marine fuel

To achieve significant reduction in greenhouse gas emissions in shipping and enable ship owners to phase out fossil fuels entirely, renewable fuels, such as ammonia, play a crucial role. Ammonia is considered a balanced solution in terms of volumetric energy density and renewable synthetic production cost compared to other renewable fuels....

Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and...

Forecasting day-ahead electricity prices in Europe: The importance of considering market integration

Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive...

Optimal Management and Control of Solar PV Attached Electricity Storage According to Energy Price Signals

End – consumers have become more active in the effort to achieve secure operation of the electricity grid. Demand response offers a set of solutions allowing consumers to achieve normalised load profiles and increase the balance between energy generation and consumption. Storage technologies and renewable energy sources integrated in microgrid...