Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm
In this paper, a reinforcement learning (RL)-based Sarsa temporal-difference (TD) algorithm is applied to search for a unified bidding and operation strategy for a coal-fired power plant with monoethanolamine (MEA)-based post-combustion carbon capture under different carbon dioxide (CO2) allowance m...
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doaj-0c5a24472de44587ba2a812d1c0b187e2020-11-24T21:15:14ZengElsevierEngineering2095-80992017-04-013225726510.1016/J.ENG.2017.02.014Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference AlgorithmZiang Li0Zhengtao Ding1Meihong Wang2School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UKSchool of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UKDepartment of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, UKIn this paper, a reinforcement learning (RL)-based Sarsa temporal-difference (TD) algorithm is applied to search for a unified bidding and operation strategy for a coal-fired power plant with monoethanolamine (MEA)-based post-combustion carbon capture under different carbon dioxide (CO2) allowance market conditions. The objective of the decision maker for the power plant is to maximize the discounted cumulative profit during the power plant lifetime. Two constraints are considered for the objective formulation. Firstly, the tradeoff between the energy-intensive carbon capture and the electricity generation should be made under presumed fixed fuel consumption. Secondly, the CO2 allowances purchased from the CO2 allowance market should be approximately equal to the quantity of CO2 emission from power generation. Three case studies are demonstrated thereafter. In the first case, we show the convergence of the Sarsa TD algorithm and find a deterministic optimal bidding and operation strategy. In the second case, compared with the independently designed operation and bidding strategies discussed in most of the relevant literature, the Sarsa TD-based unified bidding and operation strategy with time-varying flexible market-oriented CO2 capture levels is demonstrated to help the power plant decision maker gain a higher discounted cumulative profit. In the third case, a competitor operating another power plant identical to the preceding plant is considered under the same CO2 allowance market. The competitor also has carbon capture facilities but applies a different strategy to earn profits. The discounted cumulative profits of the two power plants are then compared, thus exhibiting the competitiveness of the power plant that is using the unified bidding and operation strategy explored by the Sarsa TD algorithm.http://www.sciencedirect.com/science/article/pii/S2095809917303053Power plantsPost-combustion carbon captureChemical absorptionCO2 allowance marketOptimal decision-makingReinforcement learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ziang Li Zhengtao Ding Meihong Wang |
spellingShingle |
Ziang Li Zhengtao Ding Meihong Wang Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm Engineering Power plants Post-combustion carbon capture Chemical absorption CO2 allowance market Optimal decision-making Reinforcement learning |
author_facet |
Ziang Li Zhengtao Ding Meihong Wang |
author_sort |
Ziang Li |
title |
Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm |
title_short |
Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm |
title_full |
Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm |
title_fullStr |
Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm |
title_full_unstemmed |
Optimal Bidding and Operation of a Power Plant with Solvent-Based Carbon Capture under a CO2 Allowance Market: A Solution with a Reinforcement Learning-Based Sarsa Temporal-Difference Algorithm |
title_sort |
optimal bidding and operation of a power plant with solvent-based carbon capture under a co2 allowance market: a solution with a reinforcement learning-based sarsa temporal-difference algorithm |
publisher |
Elsevier |
series |
Engineering |
issn |
2095-8099 |
publishDate |
2017-04-01 |
description |
In this paper, a reinforcement learning (RL)-based Sarsa temporal-difference (TD) algorithm is applied to search for a unified bidding and operation strategy for a coal-fired power plant with monoethanolamine (MEA)-based post-combustion carbon capture under different carbon dioxide (CO2) allowance market conditions. The objective of the decision maker for the power plant is to maximize the discounted cumulative profit during the power plant lifetime. Two constraints are considered for the objective formulation. Firstly, the tradeoff between the energy-intensive carbon capture and the electricity generation should be made under presumed fixed fuel consumption. Secondly, the CO2 allowances purchased from the CO2 allowance market should be approximately equal to the quantity of CO2 emission from power generation. Three case studies are demonstrated thereafter. In the first case, we show the convergence of the Sarsa TD algorithm and find a deterministic optimal bidding and operation strategy. In the second case, compared with the independently designed operation and bidding strategies discussed in most of the relevant literature, the Sarsa TD-based unified bidding and operation strategy with time-varying flexible market-oriented CO2 capture levels is demonstrated to help the power plant decision maker gain a higher discounted cumulative profit. In the third case, a competitor operating another power plant identical to the preceding plant is considered under the same CO2 allowance market. The competitor also has carbon capture facilities but applies a different strategy to earn profits. The discounted cumulative profits of the two power plants are then compared, thus exhibiting the competitiveness of the power plant that is using the unified bidding and operation strategy explored by the Sarsa TD algorithm. |
topic |
Power plants Post-combustion carbon capture Chemical absorption CO2 allowance market Optimal decision-making Reinforcement learning |
url |
http://www.sciencedirect.com/science/article/pii/S2095809917303053 |
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