Indirect coordination of electricity demand for balancing wind power

Abstract

High penetration of wind energy in modern power systems led to an increase in the balancing requirements, especially in load‐following and unit commitment time frames, which only further compromised the overall reliability and efficiency of electricity supply. One of the possible solutions to address this critical balancing capacity issue is the application of demand response schemes on regular basis for matching electricity consumption and production. This study examines the existing applications of demand response in the given area and proposes an alternative approach based on a proactive dispatch of large industrial consumers by using indirect coordination. The candidate industries suitable for the implementation of the new demand response scheme are sought for and presented. The usefulness and the performance of the proposed arrangement operating as a regular balancing mechanism have been evaluated and demonstrated in several test‐case scenarios by applying simplified Monte Carlo simulations.

Demand Side Management of public clean water supply

“…Traditional demand side resources such as domestic water heating and air conditioning systems or refrigeration need to be aggregated into large, controllable 'units' to have a significant effect on power system operation [10]. Keane et al [6] suggest consideration of aggregated demand side resources in the same category as generation.…”

Optimal air-conditioning load control in distribution network with intermittent renewables

“…In recent years, many DSM techniques have been proposed to reshape the energy consumption profiles of residential appliance loads. These techniques can be categorized into two classes: the price-based DSM schemes (also known as indirect load control) [2][3][4] and incentive-based DSM schemes (or direct load control) [5,6].…”

Combination of moment‐matching, Cholesky and clustering methods to approximate discrete probability distribution of multiple wind farms

“…Approximating a discrete probability distribution (DPD), which consists of scenarios and probabilities, is a significant task for relieving the burden of solving stochastic programs of wind power generation. Owing to the simplicity of the calculation involved, DPDs have been widely used in many stochastic programs of wind power systems, such as unit commitment [1–5], economic dispatch [6, 7], probabilistic power flow [8, 9], network planning [10, 11], storage capacity planning [12, 13], and wind power forecast [14–18]. However, despite the widespread applications of DPDs, accurate approximation of a DPD with a few scenarios remains a challenge.…”