Optimal Design and Operation of Integrated Energy Systems Incorporating CO2 Capture, With Application to Coal-Gas and Coal-Gas-Solar Thermal Configurations Conference: Carbon Management Technology ConferenceYear: 2013Proceeding: Carbon Management Technology ConferenceGroup: Poster SessionSession: Rapid Fire Poster Session 1 Time: Monday, October 21, 2013 - 6:42pm-6:44pm Authors: Brodrick, P., Stanford University Kang, C., Stanford University Brandt, A., Stanford University Durlofsky, L., Stanford University Because the energy and capital costs associated with CO2 capture from coal-fired power stations are substantial, it is essential that any such facility be optimally designed and operated in order to minimize the of meeting CO2 reduction goals. In previous work we showed that, under carbon-constraint policies, significant improvements in operating economics could be obtained by applying formal optimization techniques to determine hour-to-hour operational decisions (Kang et al., 2011). The system considered in that work consisted of a coal-fired power plant with solvent-based CO2 capture, with process heat provided by a gas turbine with a heat recovery steam generator (HRSG). This work was later extended to include the continuous (though not the discrete) design parameters for the facility in the optimization (Kang et al., 2012). The resulting framework thus represents a first step towards the full optimization of the design and operation of the integrated energy system. In this work we implement a full mixed integer nonlinear programming (MINLP) formulation to determine the optimal design and operation of a CO2 capture-enabled facility operating under a carbon intensity constraint. We extend the capabilities developed in Kang et al. (2012) to allow for the optimization of the discrete design parameters, which include the specific components and detailed configuration of the HRSG in the integrated energy system. The MINLP problem is solved using a procedure that includes Particle Swarm Optimization (a stochastic global search algorithm) hybridized with Mesh Adaptive Direct Search (a local optimization algorithm that guarantees convergence to a local optimum). We additionally apply this general framework to optimize various energy park configurations that include the utilization of solar energy to produce steam for CO2 capture. In this case the solar energy and steam are provided by a new low-cost solar-thermal system developed by GlassPoint, which uses arrays of parabolic troughs to produce low-cost thermal energy. Our MINLP optimization results demonstrate that there is considerable value in allowing discrete design parameters to be determined algorithmically. Our method leads to noticeably different HRSG configurations and to improvement in the net present value of the overall energy park. Results will also be presented, for a range of natural gas and electricity prices, that characterize when and how solar energy should be used, alongside or instead of an optimally designed natural-gas-fired system, for CO2 capture.