Optimal price signal generation for demand-side energy management

TL;DR

This paper develops an optimization framework using a Flexibility Function to generate optimal price signals for demand-side energy management, enhancing grid balancing and ancillary services in smart energy systems.

Contribution

It introduces a novel Flexibility Function-based approach for optimal price signal generation and compares sequential and simultaneous computation methods within demand-side management.

Findings

Price signals effectively influence demand flexibility.

Model predictive control improves grid balancing.

Sequential and simultaneous methods have different efficiencies.

Abstract

Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price signal and weather forecasts. In this work, we use a Flexibility Function to represent the relationship between the price signal and the demand and investigate optimization problems for the price signal computation. Consequently, this study considers the higher and lower levels in the hierarchy from the markets to appliances, households, and districts. This paper investigates optimal price generation via the Flexibility Function and studies its employment in controller design for demand-side management, its capability to provide ancillary services for balancing throughout the Smart Energy Operating System, and its effect on the physical level performance. Sequential and simultaneous approaches for computing the price signal, along with various cost functions are analyzed and compared. Simulation results demonstrate the generated price/penalty signal and its employment in a model predictive controller.