Deterministic Unit Commitment Model Research Articles

Optimization Methodologies for Reserve with Large-scale Renewables Integration

Renewable energy has unpredictable intermittency and variability, increasing the complexity of grid dispatch. Fuzziness theory is introduced under the uncertainty of large-scale intermittent power supply. Renewable power is characterized by fuzziness parameters. Meanwhile, the conventional deterministic unit commitment model is transformed into a chance-constraint unit commitment with fuzziness parameters. The explicit equivalence classes of chance constraints with triangular fuzziness parameters are presented to clarify chance constraints. Also, the effectiveness of the proposed model is validated in a standard IEEE test system.

Analysis on the interaction between short-term operating reserves and adequacy

An electricity generation system adequacy assessment aims to generate statistically significant adequacy indicators given projected developments in, i.a., renewable and conventional generation, demand, demand response and energy storage availability. Deterministic unit commitment (DUC) models with exogenous reserve requirements, as often used in today's adequacy studies to represent day-to-day power system operations, do not account for the contribution of operating reserves to the adequacy of the system. Hence, the adequacy metrics obtained from such an analysis represent a worst-case estimate and should be interpreted with care. In this paper, we propose to use a DUC model with a set of state-of-the-art probabilistic reserve constraints (DUC-PR). The performance of the DUC-PR model in the context of adequacy assessments is studied in a numerical case study. The Expected Energy Not Served (EENS) volume obtained with the DUC model is shown to be a poor estimate of the true EENS volume. In contrast, the DUC-PR methodology yields an accurate estimate of the EENS volume without significantly increasing the computational burden. Policy makers should encourage adopting novel operational power system models, such as the DUC-PR model, to accurately estimate the contribution of operating reserves to system adequacy.

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.

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 (UC) 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 UC 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 residential demand response on power system operation: A Belgian case study

The future power system is characterized by more renewable and uncontrollable capacity at the supply side and an electrification of energy at the demand side. Both evolutions increase the need for flexibility in the power system. Although this flexibility can be triggered at the supply and demand side, the latter is often overlooked. In this perspective, this paper assesses the impact of the use of flexibility at the demand side, also referred to as demand response, on power system operation. A two-stage modeling approach is used which combines a day-ahead deterministic unit commitment model and an hourly simulation in real-time. This approach is tested for two alternative Belgian generation technology mix scenarios including a detailed representation of residential demand response. Hereby, realistic cycling patterns of white goods and mobility patterns of battery electric vehicles serve as an input. This approach allows to quantify operational benefits of demand response and to assess a potential introduction of demand response in power system operation. Results show that in general demand response contributes to a lower cost, higher reliability, and lower emission level of power system operation. Moreover, a higher amount of uncontrollable capacity increases these benefits and therefore the societal value of demand response.