Industrial Flexibility Investment Under Uncertainty: A Multi-Stage Stochastic Framework Considering Energy and Reserve Market Participation
The global energy transition toward net-zero emissions by 2050 is expected to increase the share of variable renewable energy sources (VRES) in the energy mix. As a result, industrial actors will encounter more complex market conditions, characterized by volatile electricity prices, rising carbon costs, and stricter regulations. This situation calls for the industry to capitalize on opportunities in both spot-price arbitrage and reserve market participation, while also meeting future regulatory demands. This paper presents a multi-stage optimization framework that supports investment decisions in flexible assets and enables reserve market participation by delivering ancillary services. The framework incorporates investment decisions, spot- and reserve-market bidding, and real-time operation. Uncertainty in market prices and operational conditions is handled through a nodal formulation. A case study of a large industrial site in Norway is performed, comparing the investment decisions with future technology- and carbon pricing scenarios under varying market conditions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Risk aversion in flexible electricity markets
Flexibility options, such as demand response, energy storage and interconnection, have the potential to reduce variation in electricity prices between different future scenarios, therefore reducing investment risk. Moreo…
A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
This paper presents a scenario based robust optimization framework for short term energy scheduling in electricity intensive industrial plants, explicitly addressing uncertainty in planning decisions. The model is formul…
SchedulingMachine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010)
This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study invest…
QuantBench: Benchmarking AI Methods for Quantitative Investment
The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices. This gap hinders research progress and limits…
BenchmarkingContinual LearningStrategic Planning of Carbon-Neutral Heating Demand Coverage Under Uncertainty in a Coupled Multi-Energy Grid
Integrating the gas and district heating with the electrical grid in a multi-energy grid has been shown to provide flexibility and prevent bottlenecks in the operation of electrical distribution grids. This integration a…
Decision Making