Kenneth Bruninx

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.

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.

Endogenous Probabilistic Reserve Sizing and Allocation in Unit Commitment Models: Cost-Effective, Reliable, and Fast

In power systems with high shares of variable and limitedly predictable renewables, power system operators need to schedule flexible load, generation and storage to maintain the power system balance when forecast errors occur. To ensure a reliable and cost-effective power system operation, novel reserve sizing and allocation methods are needed. Although stochastic formulations of the unit commitment problem allow calculating an optimal trade-off between the cost of scheduling and activating reserves, load shedding and curtailment, these models may become computationally intractable for real-life power systems. Therefore, in this paper, we develop a novel set of probabilistic reserve constraints, which allows internalizing the reserve sizing and allocation problem in a deterministic unit commitment model, considering the full cost of reserve allocation and activation. Extensive numerical simulations show that this novel formulation yields UC schedules that are nearly as cost-effective as the theoretical optimal solution of the stochastic model in calculation times similar to that of a deterministic equivalent.

Impact of the German nuclear phase-out on Europe's electricity generation

The combination of the ambitious German greenhouse gas (GHG) reduction goals in the power sector and the nuclear phase-out raises many questions concerning the operational security of the German electricity generation system. This paper focusses on the technical feasibility of the German nuclear phase-out on the short term (2012-2022) and on a European scale. A detailed electricity generation simulation model is employed to address the issues at hand, including the German transmission grid and its international connections. Power plants are modelled with a high level of technical detail. A range of different renewable energy sources (RES) scenarios is considered. Results are presented for the change in generation mix, on the flows in the electric network and on operational reliability issues. The simulations show that the nuclear generation will be replaced mainly by coal and lignite based generation. Furthermore, the results indicate that export in 2012 on high demand - low and medium RES infeed days will be problematic. Keeping the seven oldest nuclear power plants (NPPs) online, would mitigate this for days with medium RES infeed. If the capacity that is currently licensed is built before 2017, the situation improves. However, the situation on the northern part of the transmission grid stays critical. In 2022, the assumed extension of the generation capacity will not suffice. Keeping the NPPs due to shut down after 2017 on line would mitigate these contingencies.

Valuing Demand Response Controllability via Chance Constrained Programming

Controllable loads can modify their electricity consumption in response to signals from a system operator, providing some of the flexibility needed to compensate for the stochasticity of electricity generated from renewable energy sources (RES) and other loads. However, unlike traditional flexibility providers, e.g., conventional generators and energy storage systems, demand response (DR) resources are not fully controlled by the system operator and their availability is limited by user-defined comfort constraints. This paper describes a deterministic unit commitment model with probabilistic reserve constraints that optimizes day-ahead power plant scheduling in the presence of stochastic RES-based electricity generation and DR resources that are only partially controllable, in this case residential electric heating systems. This model is used to evaluate the operating cost savings that can be attained with these DR resources on a model inspired by the Belgian power system.

Scenario reduction techniques and solution stability for stochastic unit commitment problems

In power systems rife with uncertainty, stochastic unit commitment (SUC) models may be used to properly size and allocate operational reserves, in order to ensure a reliable and cost-efficient operation of the power system. The performance of SUC-based unit commitment schedules is however fully dependent on the scenario sets used to describe the uncertainty at hand. Dedicated scenario generation & reduction techniques (SGT & SRT) have been developed to generate and select scenario sets that capture the uncertain parameter, e.g. wind power, and yield a cost-optimal unit commitment (UC) schedule in reasonable computing times. Probability-distance based SRTs are by far the most used. In an extensive numerical study, we analyze the performance of so-called cost functions used in these SRTs. In addition, we propose a new cost function, which allows selecting a well-balanced subset of scenarios, resulting in a tractable SUC model and a cost-optimal UC schedule.

5.4 Energy Reliability and Management

In this chapter the relationship between reliability of a power system and energy management is investigated. The current trend of more intermittent electricity generation in power systems introduces new challenges with respect to their adequacy and security. Supply and demand side energy management strategies can help in improving the reliability of the system. Among the other methods, demand response programs show a high potential for this application due to their peak shaving and load shifting capability. A thorough state of the art review about the topic, together with a qualitative exemplification by means of case studies, is provided.

Improved energy storage system & unit commitment scheduling

System operators must schedule sufficient controllable generation ahead of time to compensate unavoidable realtime mismatches between the production and consumption of electricity. If energy storage (ES) facilities are required to provide such flexibility, the technical constraints on the operation of ES must be taken into account in this scheduling problem, which is typically not done in deterministic models. Stochastic optimization enhances the procurement of flexibility, but may require more computational resources. This paper proposes an improved deterministic model for the co-optimization of controllable generation and ES, accounting for the technical constraints of the ES system and arbitrage opportunities with conventional reserve capacity. In a case study, the proposed unit commitment (UC) model is shown to yield significant operational cost reductions without affecting the systems reliability, while the increase in calculation times is limited.