Stochastic Gradient-based Fast Distributed Multi-Energy Management for an Industrial Park with Temporally-Coupled Constraints

TL;DR

This paper introduces a stochastic gradient-based distributed algorithm for real-time multi-energy management in industrial parks, effectively handling uncertainties and constraints to minimize costs and reduce peak loads.

Contribution

It proposes a novel online, distributed energy management framework with a two-timescale stochastic gradient algorithm for industrial parks with coupled energy constraints.

Findings

Achieves near-optimal cost when electricity bid-ask spread is small.

Ensures feasibility and optimality through analytical parameter setting.

Reduces peak loads via user incentive mechanisms.

Abstract

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid-ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically.