Optimal price signal generation for demand-side energy management

TL;DR

This work addresses how to generate optimal price signals for demand-side management to enable 100% renewable energy integration by leveraging a nonlinear, stochastic Flexibility Function (FF) embedded in a hierarchical Smart Energy OS. It formulates price generation as an optimization over $Y_t = FF(U_t, B_t)$ with dynamics $dX_t = f(X_t, U_t, B_t)dt + g(X_t)dw_t$, and compares sequential and simultaneous horizon strategies to produce price signals that drive Model Predictive Control for load shifting. The study demonstrates grid-balancing and ancillary-services benefits, provides a Norwegian demand-prediction case, and analyzes HVAC-based DSM in a district-neighborhood context with potential district heating integration. Results indicate improved demand-tracking and effective load shifting, suggesting FF-based price signals can reduce costs and enhance grid reliability while linking market operations with physical-level flexibility.

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.

Figures (15)

• Figure 1: The Smart Energy OS.
• Figure 2: Demand prediction using flexibility function
• Figure 3: Block diagram of higher and lower levels of smart energy OS in the presence of FF.
• Figure 4: Sketch of sequential and simultaneous price signal generation in the Smart Energy OS based on FFs. For given price signals, $U_t$ or $[U_1, ..., U_N]$, the flexibility function is used to predict the demand, $D_t$ or $[D_1, ..., D_N]$, which should be as close as possible to the reference demand, $D_{ref_t}$ or $[D_{ref_1}, ..., D_{ref_N}]$.
• Figure 5: The existing vs. novel structures for the price generation considering ancillary services. The supply and demand curve is shown as a tool for electricity market price determination, where $P$ is the price per MWh and $Q$ is the supply power.