Erik Delarue

A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing

As the share of wind power in the electricity system rises, the limited predictability of wind power generation becomes increasingly critical for operating a power system reliably. In most operational and economic models, the wind power forecast error (WPFE) is often assumed to follow a Gaussian or the so-called β-distribution. However, these distributions might not be suited to fully describe the skewed and heavy-tailed character of WPFE data. In this paper, the Lévy α-stable distribution is proposed as an improved description of the WPFE. The method presented allows us to quantify the probability of a certain error, given a certain wind power forecast. Based on recent historical wind power data, the feasibility of the Lévy α-stable distribution as a WPFE description is demonstrated. The added value of this improved statistical model of the WPFE is illustrated in a state-of-the-art probabilistic reserve sizing method. Results show that this new statistical description of the WPFE can hold important information for short-term economic and operational (reliability) studies for power systems with a significant wind power penetration.

Short-term demand response of flexible electric heating systems: The need for integrated simulations

Active Demand Response (ADR) can contribute to a more (cost-)efficient operation of and investment in the electrical power system as it provides the needed flexibility to cope with the intermittent character of renewables. One of the promising demand side technologies in terms of ADR are electric heating systems as they allow to modify their electrical load pattern without affecting the thermal energy service they deliver, due to the thermal inertia in the system. However, these systems are hard to describe with traditional demand side models, since the performance depends on boundary conditions (occupants behaviour, weather conditions). Therefore, in this paper, an integrated system approach is applied, taking into account the dynamics and constraints of both electricity supply and heating systems. Only such an integrated system approach is able to simultaneously consider all technical and comfort constraints present in the system. The effects not captured by traditional approaches - such as price elasticities and virtual generator models - are identified and quantified, enabling the reader to select a modelling approach, weighing the computational effort against the required accuracy. In extensive power system studies, this approach can be used to assess the technical potential and all effects of flexible demand side technologies.

SHORT-TERM CO2 ABATEMENT IN THE EUROPEAN POWER SECTOR: 2005–2006

This paper provides an estimate of short-term abatement of CO2 emissions through fuel switching in the European power sector in response to the CO2 price imposed by the EU Emissions Trading Scheme (EU ETS) in 2005 and 2006. The estimate is based on the use of a highly detailed simulation model of the European power sector in which abatement is the difference between simulations of actual conditions with and without the observed CO2 price. We estimate that the cumulative abatement over this period was about 53 million metric tons. The paper also explains the complex relationship between abatement and daily, weekly, and seasonal variations in load, relative fuel prices, and the price of CO2 allowances.

Selecting Representative Days for Capturing the Implications of Integrating Intermittent Renewables in Generation Expansion Planning Problems

Due to computational restrictions, energy-system optimization models (ESOMs) and generation expansion planning models (GEPMs) frequently represent intraannual variations in demand and supply by using the data of a limited number of representative historical days. The vast majority of the current approaches to select a representative set of days relies on either simple heuristics or clustering algorithms and comparison of different approaches is restricted to different clustering algorithms. This paper contributes by: i) proposing criteria and metrics for evaluating representativeness, ii) providing a novel optimization-based approach to select a representative set of days, and iii) evaluating and comparing the developed approach to multiple approaches available from the literature. The developed optimization-based approach is shown to achieve more accurate results than the approaches available from the literature. As a consequence, by applying this approach to select a representative set of days, the accuracy of ESOMs/GEPMs can be improved without increasing the computational cost. The main disadvantage is that the approach is computationally costly and requires an implementation effort.

Coupling Pumped Hydro Energy Storage With Unit Commitment

Renewable electricity generation not only provides affordable and emission-free electricity but also introduces additional complexity in the day-ahead planning procedure. To address the stochastic nature of renewable generation, system operators must schedule enough controllable generation to have the flexibility required to compensate unavoidable real-time mismatches between the production and consumption of electricity. This flexibility must be scheduled ahead of real-time and comes at a cost, which should be minimized without compromising the operational reliability of the system. Energy storage facilities, such as pumped hydro energy storage (PHES), can respond quickly to mismatches between demand and generation. Hydraulic constraints on the operation of PHES must be taken into account in the day-ahead scheduling problem, which is typically not done in deterministic models. Stochastic optimization enhances the procurement of flexibility, but requires more computational resources than conventional deterministic optimization. This paper proposes a deterministic and an interval unit commitment formulation for the co-optimization of controllable generation and PHES, including a representation of the hydraulic constraints of the PHES. The proposed unit commitment (UC) models are tested against a stochastic UC formulation on a model of the Belgian power system to compare the resulting operational cost, reliability, and computational requirements. The cost-effective regulating capabilities offered by the PHES yield significant operational cost reductions in both models, while the increase in calculation times is limited.

Optimization and Allocation of Spinning Reserves in a Low-Carbon Framework

Low-carbon electric power systems are often characterized by high shares of renewables, such as wind power. The variable nature and limited predictability of some renewables will require novel system operation methods to properly size and cost-efficiently allocate the required reserves. The current state-of-the-art stochastic unit commitment models internalize this sizing and allocation process by considering a set of scenarios representing the stochastic input during the unit commitment optimization. This results in a cost-efficient scheduling of reserves, while maintaining the reliability of the system. However, calculation times are typically high. Therefore, in this paper, we merge a state-of-the-art probabilistic reserve sizing technique and stochastic unit commitment model with a limited number of scenarios in order to reduce the computational cost. Results obtained for a power system with a 30% wind energy penetration show that this hybrid approach allows to approximate the expected operational costs and reliability of the resulting unit commitment of the stochastic model at roughly one thirtieth of the computational cost. The presented hybrid unit commitment model can be used by researchers to assess the impact of uncertainty on power systems or by independent system operators to optimize their unit commitment decisions taking into account the uncertainty in their system.

Smart Cities Initiative: how to foster a quick transition towards local sustainable energy systems

The European Commission has recently launched the Smart Cities Initiative to demonstrate and disseminate how to foster a quick transition towards local sustainable energy systems. Within this initiative, the three main challenges faced by pioneering cities, are to reduce or modify the demand for energy services, to improve the uptake of energy efficient technologies and to improve the uptake of renewables in the urban environment. We find that enough resources will need to be provided to a significant number of pioneering cities, and propose that the initiative would allocate these resources through project competition, rewarding innovation, ambition and performance, which have been ingredients of success at Member State level.

A Mixed Integer Linear Programming Model for a Pulverized Coal Plant With Post-Combustion Carbon Capture

Carbon capture and storage (CCS) seems to be an indispensable technology to safeguard the future of fossil-fired generation in the context of global warming. The deployment of CCS has an impact on the functioning and balancing of the overall electricity generation system. In this paper a mixed integer linear programming (MILP) model is developed for an ultra super-critical pulverized coal plant with post-combustion capture. Emphasis is on an appropriate representation of the dynamic behavior of this power plant. Four operating modes are considered for the capture plant, i.e., normal, start-up, off, and stand-by. The model is illustrated by means of a methodological example.

Possible role of power-to-gas in future energy systems

Energy storage can become increasingly important in energy systems dominated by intermittent renewable energy sources. In this paper, the possible opportunities for power-togas, as long-term storage option, are compared to the opportunities for short-term storage technologies. A simulation is performed to quantitatively address the difference between both storage options. In this analysis, short-term storage is characterized by a relatively high efficiency but low energy storage capacity. Power-to-gas is characterized by a lower efficiency but a higher energy storage capacity. Results indicate that power-to-gas could play a role in future energy systems with a high imposed share of renewable energy, especially when the renewable energy production profile shows a seasonal trend.

Compressed air energy storage multi-stream value assessment on the french energy market

The compressed air energy storage (CAES) technology is considered as an attractive bulk energy storage solution next to the pumped hydro storage, whose development potential is very limited, especially in Europe. Nowadays, the promotion of CAES in the power system will essentially depend on the economic viability of the investment project in certain economic and regulatory environment. This paper performs a global evaluation of 2nd generation gas CAES plants on the French electricity markets, incorporating both regulated and deregulated sources of revenue.