Linear Programming for Multi-Agent Demand Response

Alireza Fallahi,Jay M Rosenberger,Shouyi Wang,Wei-Jen Lee,Victoria C P Chen

doi:10.1109/access.2019.2959727

Alireza Fallahi, Jay M Rosenberger + Show 3 more

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https://doi.org/10.1109/access.2019.2959727

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Abstract

This research describes a real-time optimization model for multi-agent demand response (DR) from a Load Serving Entity (LSE) perspective. Three major categories of customers and five types of energy resources are considered simultaneously to achieve efficient DR decision making in highly stochastic future energy markets. Two infinite horizon stochastic optimization models are formulated; specifically, an LSE model and a dynamic pricing customer model. The objective of these models is to minimize long-term cost and discomfort penalty of the LSE and dynamic pricing customers. Because preferences of these two agents are different, they are inseparable and difficult to solve. A deterministic finite horizon linear program is solved as an approximation of the suggested stochastic model, and computational experiments are provided.

Highlights

The current electric distribution and management system has been relatively constant and stable for many decades, recent advancements may fundamentally change the design and operation of the electric system and create new challenges to the existing power supply management
They describe three goals in the objective function: overall cost, climate comfort level, and timeliness. They assign weights to each of these three terms according to customer preferences. They solve this problem with an mixed integer linear programming (MILP) algorithm, and the results show that this model can solve such problems efficiently
Computational experiments of a one-day deterministic problem show the behavior of the system. It suggests that buying from the grid for the purpose of storage or satisfying demand when market price is low or when there is a shortage of supply

Summary

Introduction

The current electric distribution and management system has been relatively constant and stable for many decades, recent advancements may fundamentally change the design and operation of the electric system and create new challenges to the existing power supply management These transformations include more renewable energy resources in the bulk power system, proliferation of distributed energy resources (DERs) of various capacities in both transmission and distribution systems, increased installations of local renewable resources at end-use points, and rapid growth of transportation electrification (e.g., Electric Vehicles-EVs) at end users [1]–[13]. A potentially high penetration of wind and solar resources is expected, as are customer-installed generation and storage operated autonomously, causing serious problems of intermittent shortage or overproduction that far exceed the capability of the current electric distribution systems [9], [11], [10], [12], [13]. This emerging issue of intermittent shortage or overproduction is critical mainly because the key differentiator of the electricity system compared to other commodities is that electricity distributors must balance supply and demand across the entire grid in real time [13]

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