Search arXivSearch

arXiv · 2411.11868

Strategic Optimization and Demand Response for Thermal Load Management in Multi-Regional Integrated Energy Systems: A Stackelberg Game Approach

Abstract

In the context of high fossil fuel consumption and inefficiency within China's energy systems, effective demand-side management is essential. This study examines the thermal characteristics of various building types across different functional areas, utilizing the concept of body coefficient to integrate their unique structural and energy use traits into a demand response framework supported by real-time pricing. We developed a Stackelberg game-based bi-level optimization model that captures the dynamic interplay of costs and benefits between integrated energy providers and users. This model is formulated into a Mixed Integer Linear Programming (MILP) problem using Karush-Kuhn-Tucker (KKT) conditions and linearized with the Big M method, subsequently solved using MATLAB and CPLEX. This approach enables distinctive management of heating loads in public and residential areas, optimizing energy efficiency while balancing the interests of both providers and users. Furthermore, the study explores how the proportion of different area types affects the potential for reducing heat loads, providing insights into the scalability and effectiveness of demand response strategies in integrated energy systems. This analysis not only highlights the economic benefits of such strategies but also their potential in reducing dependency on traditional energy sources, thus contributing to more sustainable energy system practices.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ranran Yang. 2024-11-03. Strategic Optimization and Demand Response for Thermal Load Management in Multi-Regional Integrated Energy Systems: A Stackelberg Game Approach. https://arxiv.org/abs/2411.11868

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Observability and parameter estimation of a generic model for aggregated distributed energy resources

We propose a novel framework for estimating the parameters of an aggregated distributed energy resources (DER A) model. First, we introduce a rigorous method to determine whether all model parameters are estimable. When they are not, our approach identifies the subset of parameters that can be estimated. The proposed framework offers new insights into the number and specific parameters that can be reliably estimated based on commonly available measurements. It also highlights the limitations of calibrating such models. Second, we introduce a Kalman filtering method to calibrate the DER A model. Since we account for nonlinear effects such as saturation and deadbands, we develop a specific mechanism to handle smoothing functions within the Kalman filter. Specifically, we consider the extended and the unscented Kalman filter. We demonstrate the effectiveness of the proposed framework on a modified IEEE 34-node distribution feeder with inverter- based resources. Our findings align with the North American Electric Reliability Corporation's parameterization guideline and underscore the importance of model calibration in accurately capturing the collective dynamics of distributed energy resources installed on distribution systems.

eess.SY

Salted Fisher Information for Hybrid Systems

Discrete events change how parameter-influence propagates in hybrid systems. Prevailing Fisher information for- mulations assume that sensitivities evolve smoothly according to continuous-time variational equations and therefore neglect the sensitivity updates induced by discrete events. This paper derives a Fisher information matrix formulation compatible with hybrid systems. To do so, we use the saltation matrix, which encodes the first-order transformation of sensitivities induced by discrete events. We call the resulting formulation the salted Fisher information matrix (SFIM). The proposed framework unifies continuous information accumulation during flows with discrete updates at event times. We also show that hybrid persistence of excitation is sufficient for the SFIM to be positive definite

eess.SY

Min-Max Grassmannian Optimization for Online Subspace Tracking

We propose GeRoST (Geometrically Robust Subspace Tracking), an online subspace tracking algorithm that models uncertainty in a subspace using a Grassmannian ball. We derive an exact scalar dual for the worst-case subspace problem, establish conditions for a unique worst-case subspace and a Riemannian gradient, and characterize the minimum radius needed to cover a dimensional extension of the target subspace. Each update uses either a spectral direction computed in a reduced subspace or the gradient of the window reconstruction loss. Our numerical experiments show that GeRoST achieves lower mean post-fault prediction error than GREAT in system identification. In video separation, it achieves higher precision and a better precision--recall balance, as measured by the F$_1$ score, than both GREAT and GRASTA at the reported thresholds, with lower recall and longer runtime.

eess.SY