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Engineering    2017, Vol. 3 Issue (2) : 257 -265
Research |
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
Ziang Li1,Zhengtao Ding1(),Meihong Wang2
1. School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
2. Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, UK

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.

Keywords Power plants      Post-combustion carbon capture      Chemical absorption      CO2 allowance market      Optimal decision-making      Reinforcement learning     
Corresponding Authors: Zhengtao Ding   
Just Accepted Date: 24 March 2017   Online First Date: 17 April 2017    Issue Date: 27 April 2017
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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[J]. Engineering, 2017, 3(2): 257 -265 .
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