Stochastic risk‐based flexibility scheduling for large customers with onsite solar generation. Issue 14 (1st October 2019)

• Record Type: Journal Article
• Authors: Angizeh, Farhad; Parvania, Masood
• Is Part Of: IET renewable power generation. Volume 13:Issue 14(2019)
• Page Start: 2705
• Page End: 2714
• Publication Date: 2019-10-01
• Subjects: autoregressive moving average processes -- stochastic programming -- solar power -- scheduling -- integer programming -- stochastic processes -- power markets -- pricing -- linear programming -- optimisation -- risk management -- power generation planning; onsite solar generation -- two‐stage stochastic optimisation model -- onsite flexibility resources -- market prices -- customers -- onsite energy storage -- onsite dispatchable generation -- stage decisions -- stochastic risk‐based flexibility scheduling; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2019.0233
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : This study proposes a two‐stage stochastic optimisation model to co‐optimise the onsite flexibility resources of large customers with the decisions to purchase power from bilateral contracts and forward energy market, while considering the uncertainty of onsite solar generation and market prices. In addition to onsite solar generation, the proposed model integrates the energy flexibility provided by the customers' flexible loads, onsite energy storage and onsite dispatchable generation. The uncertainty of onsite solar generation and market prices are characterised by autoregressive integrated moving average models, which are used to generate scenarios for the proposed stochastic optimisation model. The proposed two‐stage stochastic optimisation model, formulated as a mixed‐integer linear programming problem, minimises the expected energy procurement cost of the customers by optimising the first and second stage decisions. The first stage decisions include the utilisation of bilateral contracts, flexible loads and onsite energy storage and dispatchable generation, while decisions to purchase power from, and selling excess solar power to the energy market are optimised in the second stage. The proposed model integrates conditional value‐at‐risk as a risk measure that would enable the customers to manage the financial risks associated with the uncertainty of onsite solar generation and market prices. The proposed model is implemented on a test large industrial customer, where the advantages of the proposed model are investigated through multiple case studies.