Stochastic optimisation model to determine the optimal contractual capacity of a distributed energy resource offered in a balancing services contract to maximise profit
File(s)
Published version
Author(s)
Rai, Ussama Javed
Oluleye, Gbemi
Hawkes, Adam
Type
Journal Article
Abstract
In the realm of grid balancing services, determining the generation capacity of a distributed energy resource for contractual agreements with the system operator is pivotal. However, prevalent heuristic or deterministic methodologies employed by demand response aggregators often lack risk assessment and may not optimize generation capacity allocation. Consequently, the potential for maximizing utilization profits remains untapped. This paper addresses these limitations and explains the necessity of using the optimal generation capacity of a grid-connected distributed energy resource which is also fulfilling site electricity demand to maximise profit and mitigate penalties both for demand response aggregators and their clients. Demand response aggregators provide these services to the system operator on behalf of their clients whose electrical generation assets they utilize on a profit-sharing basis. The primary challenge investigated in this study lies in effectively managing the uncertainty surrounding both site electricity demand and short-term operating reserve calls by the system operator through a novel two-step approach. Firstly, a demand bin characterization technique is employed to account for site demand uncertainty. Subsequently, a stochastic model utilizing mixed integer nonlinear programming is developed using the General Algebraic Modeling System, incorporating five years of uncertainty regarding the frequency of short-term operating reserve calls which makes it instrumental and novel in determining optimal contractual generation capacity in a balancing service contract, as well as associated profits and penalties, under varying utilization prices. This distinctiveness positions it as an advancement over and distinct from deterministic approaches. Case study results demonstrate the efficacy of the proposed stochastic model in comparison to deterministic methods utilized in prior research. Specifically, the stochastic model yields a realistic profit increase of 12.7% and offers 2.62% higher contractual capacity than the heuristic approach followed by the DR aggregator highlighting its potential to enhance profitability and operational efficiency in grid balancing services.
Date Issued
2024-06
Date Acceptance
2024-05-19
Citation
Energy Reports, 2024, 11, pp.5800-5818
ISSN
2352-4847
Publisher
Elsevier
Start Page
5800
End Page
5818
Journal / Book Title
Energy Reports
Volume
11
Copyright Statement
© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S2352484724003275
Publication Status
Published
Date Publish Online
2024-05-29