Accepted Manuscript The hybrid MPC-MINLP algorithm for optimal operation of coal-fired power plants with solvent based post-combustion CO2 capture Norhuda Abdul Manaf, Abdul Qadir, Ali Abbas PII:
S2405-6561(16)30068-2
DOI:
10.1016/j.petlm.2016.11.009
Reference:
PETLM 119
To appear in:
Petroleum
Received Date: 17 May 2016 Revised Date:
26 August 2016
Accepted Date: 9 November 2016
Please cite this article as: N. Abdul Manaf, A. Qadir, A. Abbas, The hybrid MPC-MINLP algorithm for optimal operation of coal-fired power plants with solvent based post-combustion CO2 capture, Petroleum (2017), doi: 10.1016/j.petlm.2016.11.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT The hybrid MPC-MINLP algorithm for optimal operation of coal-fired power plants with
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solvent based post-combustion CO2 capture
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Norhuda Abdul Manaf, Abdul Qadir, Ali Abbas*
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School of Chemical and Biomolecular Engineering, The University of Sydney, Sydney 2006,
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Australia
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ABSTRACT
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This paper presents an algorithm that combines model predictive control (MPC) with MINLP
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optimization and demonstrates its application for coal-fired power plants retrofitted with solvent
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based post-combustion CO2 capture (PCC) plant. The objective function of the optimisation
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algorithm works at a primary level to maximize plant economic revenue while considering an
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optimal carbon capture profile. At a secondary level, The MPC algorithm is used to control the
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performance of the PCC plant. Two techno-economic scenarios based on fixed (capture rate is
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constant) and flexible (capture rate is variable) operation modes are developed using actual
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electricity prices (2011) with fixed carbon prices ($AUD 5, 25, 50/tonne-CO2) for 24 hour
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periods. Results show that fixed operation mode can bring about a ratio of net operating revenue
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deficit at an average of 6% against the superior flexible operation mode.
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Keywords: Carbon capture; PCC; flexible operation; modelling; algorithm; optimisation.
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1.
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The implementation of low emissions technologies such as amine-based post combustion CO2
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capture process (PCC) at coal-fired power generation is of significant importance for the short
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and long term global energy securities. According to the International Energy Agency (IEA),
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long-term energy security focuses on perpetual energy supply concurrent with economic *
Introduction
Corresponding Author: Tel: +61 2 9351 3002; Fax: +61 2 9351 2854; E-mail:
[email protected]
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ACCEPTED MANUSCRIPT enhancement and environmental sustainability. While short term energy security emphasizes on
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the robust and flexible operation of energy systems towards abrupt perturbations within the
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supply-demand balance [1]. Both perspectives require systematic carbon emissions control and
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planning in power generations (retrofitted with PCC system) which involve implementation of
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optimal techno-economic strategies and highly flexible operations.
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To date, many studies have proposed carbon constrained energy planning as a method to meet
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obligatory emissions targets over time [2-5] in order to meet part of the objective for long-term
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energy security. Economic feasibility in terms of plant revenue and cost savings in response to
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electricity demand, carbon and electricity prices have recently been explored in [6-9]. Numerous
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flexible operational strategies in power plants associated with PCC have been proposed, for
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example, time varying regeneration [7], CO2 venting [8, 9] and solvent storage [8, 9]. Mac
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Dowell et al. [7] identified that the time-varying solvent regeneration strategy generated surplus
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cumulative profit compared with the base case strategy (power plant load following associated
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with PCC) and other flexible operational strategies (exhaust gas venting, solvent storage) with
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approximately 16% over the base case. They have observed that the combination of fuel prices,
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carbon prices and duration of electricity at off- and high-peak hours contributed to the
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performance of each operational strategy. Meanwhile, a parallel flexible operation of capture
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level reduction (reduce CO2 venting) and solvent storage revealed higher cost saving than the
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individual strategies which were up to 5% of the total cost [8]. From another angle, The
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integration of renewable energy with coal-fired power generation with PCC may provide
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financial benefit to the system depending on the sources, demand and economic stability. For
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instance, Qadir et al. [6] showed that via injection of solar thermal energy for repowering of the
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PCC-integrated power plant, the system was capable of generating higher revenue than the base
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case technology (power plant without PCC) and other solar assisted technologies (eg. solar
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ACCEPTED MANUSCRIPT assisted capture). Their study was applied based on the prevailing scenario in Australia.
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Contrarily, based on the current situation in northwest Europe, power plant with wind power
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integrated with flexible operation of PCC (CO2 venting and solvent storage) was found to be
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capable of increasing reserve capacity by 20–300% compared to non-flexible operation [9].
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However, implementation of flexible PCC in this location did not lead to additional revenue due
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to the high projection of CO2 prices (€43/tonne CO2 in 2020 and €112/tonne CO2 in 2030) and
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regeneration constraint of the base-load power plant.
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Other relevant works involved with various techno-economic studies are available in [10-14].
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Cohen et al. [10] optimized the operating scenarios of carbon capture in response to electricity
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price and carbon price with the options proposed by Chalmers et al. [11] and concluded that
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flexible operation can result in over 10% savings over the inflexible case. Wiley et al. [12]
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analysed the carbon capture opportunities from a black coal fired power plant in Australia and
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concluded that by using mixed operation strategies like partial, part-time and variable capture
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strategies, it is possible to capture up to 50% of total emissions while still meeting the grid
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demand. Additionally, Arce et al. [13] present a multilevel control and optimization strategy for
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flexible operation of a solvent based carbon capture plant, aiming to minimize the operational
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cost of the carbon capture plant. They demonstrated savings up to 10% in energy cost for solvent
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regeneration. A compilation of previous studies pertaining to the management decision-making
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(planning and scheduling) of various energy generations retrofitted with various techniques of
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CO2 capture systems are available in the Appendix. In comparison with the previous studies, the
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novel features of this present work are as follows:
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(a) This study offers multi-level decision making from the perspective of plant manager
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(enterprise level) to the operator/engineer viewpoint (instrumentation level) by
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integrating a superstructure optimization-based algorithm (applied to a power plant) with
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ACCEPTED MANUSCRIPT an advanced control strategy embedded into dynamic PCC model. Whereby, most of the
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previous studies (as listed in the Appendix) focused on the management decision
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(planning and scheduling) at the single level (e.g. enterprise and policy levels
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respectively) without considering responses arising from the downstream CO2 capture
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process.
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(b) This study is beneficial for the implementation of large-scale PCC plants in which the
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installation of control systems is required and especially in term of installation cost and
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performance of control scheme. These are illustrated by the capability of the control
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scheme to track plant objective (that is to obtain maximum plant net operating revenue)
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while rejecting internal and external plant disturbances.
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(c) The hybrid control-optimization algorithm developed in this work is practical for
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application in a 660 MW coal-fired power plant with PCC and demonstrated its usability
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when using real-time temporal data of electricity and carbon prices.
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Specifically, the purpose of this study is twofold: first is to obtain maximum plant net operating
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revenue under real-time electricity prices through controlling the power plant load and CO2
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capture rate. Secondly, it is to ensure the robustness of the PCC control strategy under real-time
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perturbation pattern from the upstream process (power plant). The structure of this paper is as
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follows: Section 2 presents a brief explanation for each level of the hybrid MPC-MINLP
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algorithm (control-optimization algorithm). The control-optimization scenarios are examined in
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Section 3. Finally, results and conclusions from this study are presented in Sections 4 and 5
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respectively.
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ACCEPTED MANUSCRIPT 2.
Development of the hybrid MPC-MINLP algorithm (control-optimization algorithm)
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In this study, a control-optimization algorithm encompasses of coal-fired power plant with PCC
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models are simulated to evaluate the capability and reliability of the developed algorithm. The
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algorithm consists of three levels that linked together, namely enterprise, plant and
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instrumentation levels as illustrated in Fig. 1. The inputs/outputs and methodology of each level
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are briefly explained below.
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1.
Enterprise level (optimization algorithm)
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Inputs: Power plant gross load (t), electricity price (t) and carbon price (t)
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Outputs: Optimal power plant load (t) and ideal CO2 capture rate (t)
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Interval time: 30 minute
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Planning horizon: 24 hour
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Methodology: Implementation of a mixed integer non-linear programming (MINLP) using
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genetic algorithm (GA) function to determine the optimal operation of coal-fired power plant
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associated with PCC by predicting optimal power plant loads and ideal CO2 capture rates over
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time. This is subject to maximum net operating revenue of the integrated plant (coal-fired power
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plant associated with PCC) as delineated in Eq. (1).
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is the price of electricity and
(1)
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Where
is the carbon price. The first integration term in Eq.
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(1) demonstrates the revenue generated through selling of electricity. The breakdown of net
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operating revenue includes three individual costs which are
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cost,
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integration term). In this study, net load matching mode has been chosen for the optimization
as the power plant operational
as the PCC operational cost and cost of CO2 emission (indicated in the second
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formulation while the cost assumptions are tabulated in Table 1. For more information on the
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optimization algorithm, one can refer to [6].
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2.
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Input: Ideal CO2 capture rate (t)
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Output: Actual CO2 capture rate (t)
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Interval time: 10 second
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Planning horizon: 24 hour
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Methodology: Incorporation of PCC empirical model via multivariable nonlinear autoregressive
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with exogenous input (NLARX) with model predictive control (MPC) by resuming the actual
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profile of CO2 capture rates (CCactual) based on the ideal CO2 capture rates (CCideal). This set
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point tracking scenario (CO2 capture rates) is initiated with the MPC manipulates the lean
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solvent flow rate, u3 and reboiler heat duty, u7 to ensure that the plant meets the control objective
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(CCideal). Here, the CCactual represents the actual output of CO2 capture based on the response
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from the MPC. Finally, the two outputs (optimal power plant loads and actual CO2 capture rates)
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generated from the control-optimization algorithm were used to calculate the actual net operating
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revenue of the integrated plant. For comprehensive description on the workflow of the algorithm,
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one can refer to Abdul Manaf et al. [15].
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Plant and instrumentation levels (control algorithm)
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In both algorithms, the ‘(t)’ represents the data sampling time, where each input/output data point
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was recorded/taken at every 30 minutes/10 seconds throughout the 24 hours of planning horizon.
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(1)
Fig. 1: The control-optimization algorithm (hybrid MPC-MINLP algorithm) for power plant integrated with PCC system. 148
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ACCEPTED MANUSCRIPT Table 1 Operating and maintenance cost assumptions for the power plant and PCC plant. Assumption
O&MPP,coal
$50,000/MW/year [1]
Coal specific cost
$1.5/GJ
Power plant capacity/size
660 MW
O&MPCC
Eq. 2 from Li et al. [14]
Solvent loss
1.5 kg MEA/tonne-CO2 captured
Solvent cost
$2/kg MEA
Sequestration cost
$7/tonne-CO2 captured
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3.
Control-optimization scenarios
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Two control-optimization scenarios were developed based on the electricity prices (year 2011)
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and carbon prices ($AUD 5, 25, 50/ tonne-CO2). Each scenario represents fixed operation mode
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and flexible operation mode respectively. Both scenarios were compared to examine the
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capability of the developed control-optimization algorithm and the financial advantages of both
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operation modes. Electricity prices for a 24-hour period with a highly fluctuating pattern are
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selected as illustrated in Fig. 2. The dynamic profile of electricity prices were chosen to examine
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the capability and sensitivity of the developed control-optimization algorithm. Here, electricity
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prices are collected from [16]. Three different values of carbon price at constant rate were
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evaluated. These include $5/tonne-CO2 (represents the lower price), $25/tonne-CO2 (represents
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the nominal price), and, $50/tonne-CO2 (represents the maximum price). All costs/prices in this
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study are presented in Australian dollars.
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Fig. 2 The electricity prices (regional reference price, RRP) for 2011. 163
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4.
Result and discussion
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The MPC-MINLP algorithm (depicted in Fig. 1) was implemented in Matlab (Mathworks, USA)
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and solved using a PC with a dual core i7 processor and 16 GB RAM. The computation time
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required for execution of MINLP algorithm for one scenario (24 hours) was approximately 5
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hours. While, the computation time required for MPC controller is about 10 minutes. The
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optimization formulation for fixed and flexible operation modes is given in Table 2 as below.
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Table 2 Optimization formulation for fixed and flexible operation modes. Fixed operation mode
Flexible operation mode
x2, CP)
CP)
s.t.
s.t.
Process model:
,
Process
CRMin
