Power optimization for a hydrocarbon industrial plant using a genetic algorithm

In this paper, a genetic algorithm (GA) is considered for optimizing electrical power loss for a real hydrocarbon industrial plant as a single objective problem. The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses (J1) objective is used to guide the optimization process, and, consequently, the injected power into the grid (PRInject) is increased. The results obtained demonstrate the potential and effectiveness of the proposed approach to optimize the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving will be addressed

Simultaneous design of damping controllers and internal controllers of a unified power flow controller

In this paper, the use of a supplementary controller of a unified power flow controller (UPFC) to damp low frequency oscillations is investigated. A new technique to design UPFC damping controllers simultaneously with UPFC internal controllers is proposed. An optimization problem to search for the optimal controller settings is formulated so as to optimize a time-domain based objective function that considers all the controllers simultaneously. The effectiveness of the proposed controllers in damping low frequency oscillations is verified through eigenvalue analysis and non-linear time simulation. A comparison with a sequential design of the controllers under study is also .included

A new multiobjective evolutionary algorithm forenvironmental/economic power dispatch

In this paper, a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) optimization problem is presented. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. A new nondominated sorting genetic algorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving technique to overcome the premature convergence and search bias problems and produce a well-distributed Pareto-optimal set of nondominated solutions. A hierarchical clustering technique is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Several optimization runs of the proposed approach are carried out on a standard IEEE test system. The results demonstrate the capabilities of the proposed NSGA based approach to generate the true Pareto-optimal set of nondominated solutions of the multiobjective EED problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison shows the superiority of the proposed NSGA based approach and confirms its potential to solve the multiobjective EED proble

Optimal design of power-system stabilizers using particle swarm optimization

In this paper, a novel evolutionary algorithm-based approach to optimal design of multimachine power-system stabilizers (PSSs) is proposed. The proposed approach employs a particle-swarm-optimization (PSO) technique to search for optimal settings of PSS parameters. Two eigenvalue-based objective functions to enhance system damping of electromechanical modes are considered. The robustness of the proposed approach to the initial guess is demonstrated. The performance of the proposed PSO-based PSS (PSOPSS) under different disturbances, loading conditions, and system configurations is tested and examined for different multimachine power systems. Eigenvalue analysis and nonlinear simulation results show the effectiveness of the proposed PSOPSSs to damp out the local and interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations. In addition, the potential and superiority of the proposed approach over the conventional approaches is demonstrated

Challenges and potential solutions for developing a reliable and sustainable energy system towards reducing atmospheric carbon dioxide

Carbon dioxide emissions represent the primary driver behind the accelerated global warming problem. While mitigating these emissions from key sectors may necessitate a protracted timeframe, several promising technologies offer the potential for short-term advancements in full or partial decarbonization efforts. In pursuit of establishing a sustainable and reliable zero-carbon energy system, the deployment of energy storage systems integrated with clean and renewable resources becomes imperative. Solar and wind technologies are ubiquitously available across numerous nations and have attained a level of maturity conducive to widespread deployment on a grid scale. The combination of solar and wind energy with energy storage serves to expedite the decarbonization of electricity generation processes. Notably, the industrial and transportation sectors are difficult to be decarbonized, requiring decades to decarbonize due to the imperative of either developing novel technologies or optimizing existing ones to boost efficiency and cost-effectiveness. Giving priority to decarbonizing processes or sectors that can be mitigated relatively easily accelerates the broader decarbonization of more complex counterparts, thereby promoting a comprehensive approach to carbon mitigation strategies.The electricity generation is an important first step in decarbonizing the rest of sectors. In this dissertation, I investigated the challenges and the potential solutions for decarbonizing the electricity generation using clean and renewable sources of energy. Furthermore, I explored the possibility of decarbonizing some of the industrial processes using solar thermal technology. Eventually, I proposed the possible opportunity for Direct Air Carbon Capture (DACC) for indirect decarbonization of other hard-to-be-decarbonized processes/sectors like steel and cement industry and aviation sector. To achieve 100% renewable electricity grid, all the carbon emitting resources are replaced by a renewable resources like solar and wind for all the years from 2015 to 2020. The real historical demand and generation data are used. We explored various 100% renewable electricity grid scenarios by using different mixes between the available renewable resources (solar, onshore wind, offshore wind, and geothermal) with different overbuild capacities. Our findings indicate that while summer currently poses the greatest challenge, a solar-dominant grid shifts this challenge to the winter, contingent upon solar and storage capacities. To reduce the storage size and decrease the severity of the winter challenge, we investigated the potential of winter-dominant onshore wind and the usage of a clean dispatchable source of energy like the Allam cycle sequentially. We found that the storage size can be reduced by 30%-40% and we can generate about 37% of the total annual electricity consumption using the available winter-dominant onshore wind. Further analysis of the energy storage indicates that part of it is used frequently every day to supply the electricity demand during the nights (diurnal storage) and another big part of it is used to compensate the limited solar generation during the winter (seasonal storage). The rest of the energy storage is used to cover the cloudy days across the year (cross-day storage). Decarbonizing the industrial sector will add to the current electricity demand. Thus, we investigated the possibility of decarbonizing some of the industrial processes using solar thermal technology that does not rely on the electricity grid. We presented a comprehensive assessment of the performance of a novel solar thermal system, the Non-tracking Asymmetric Shadeless (NASH) concentrator, highlighting its efficiency and energy generation capabilities. A steady-state model developed for the system offers valuable insights into its operational dynamics and performance trends. Other industrial processes and sectors are hard to decarbonize, (e.g. the steel industry, the cement industry, and the aviation sector). It may take decades to decarbonize these processes/sectors. We proposed that we use Direct Air Carbon Capture (DACC) to capture the carbon emissions from these processes/sector. The DACC will be powered by the surplus electricity generated by a 100% renewable electricity grid. By combining empirical data analysis with theoretical modeling, this dissertation contributes to advancing our understanding of the challenges and the potential solutions for decarbonizing electricity generation, offering crucial insights for policymakers and stakeholders navigating the transition to sustainable and reliable clean energy systems

Genetic-based TCSC damping controller design for power systemstability enhancement

A genetic-based damping controller for a thyristor-controlled series capacitor (GCSC) is presented in this paper. Minimizing the real part of the system eigenvalue associated with low frequency oscillation mode is proposed as the objective function of the design problem. The proposed controller has been examined on a weakly connected power system with different disturbances and loading conditions. Eigenvalue analysis and nonlinear simulation results show that the performance of the proposed GCSC outperforms that of conventional power system stabilizer (CPSS). It is also observed that the proposed GCSC improves greatly the voltage profile of the system under severe disturbance

Design of PSS and STATCOM-based damping stabilizers using genetic algorithms

Power system stability enhancement via coordinated design of power system stabilizers (PSSs) and STATCOM-based damping stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition (SVD) based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The coordination among the proposed damping stabilizers and the STATCOM internal AC and DC voltage controllers has been taken into consideration. The design problem of STATCOM-based stabilizers is formulated as an optimization problem. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions

Environmental/economic power dispatch using multiobjective evolutionary algorithms

This paper presents a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new strength Pareto evolutionary algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving mechanism to overcome the premature convergence and search bias problems. A hierarchical clustering algorithm is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise nondominated solution. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of the multiobjective EED problem in one single run. The comparison with the classical techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem. In addition, the extension of the proposed approach to include more objectives is a straightforward process