Combined heat and power economic dispatch problem using the invasive weed optimization algorithm

Cogeneration units which produce both heat and electric power are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units which produce either heat or power exclusively. Hence the economic dispatch problem for these plants optimizing the fuel cost is quite complex and several classical and meta-heuristic algorithms have been proposed earlier. This paper applies the invasive weed optimization algorithm which is inspired by the ecological process of weed colonization and distribution. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over earlier ones.     

A solution to unit commitment problem using invasive weed optimization algorithm

Unit commitment (UC) is one of the most important aspect of power generation in the world today. Though, there is no method to find the exact optimized solution, there exists several meta-heuristic algorithms to determine the close to exact solution. This paper proposes a novel solution to effectively determine UC and generation cost using the technique of invasive weed optimization (IWO). The existing technique distributes the load demand among all the generating units. The method proposed here utilizes the output of UC obtained by using the Lagrangian relaxation (LR) method and calculates the required generation from only the plants that are ON discarding the OFF generator units and thereby giving a faster and more accurate response. Moreover, the results show the comparison between the LR-particle swarm optimization (PSO) and LR-IWO, and prove that the cost of generation for a 4 unit, 8 hour schedule is much less in the case of IWO when compared to PSO.     

A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem

The increasing costs of fuel and operation of thermal power generating units warrant development of optimization methodologies for economic dispatch (ED) problems. Optimization methodologies that are based on meta-heuristic procedures could assist power generation policy analysts to achieve the goal of minimizing the generation costs. In this context, the objective of this study is to present a novel approach based on harmony search (HS) algorithm for solving ED problems, aiming to provide a practical alternative for conventional methods. To demonstrate the efficiency and applicability of the proposed method and for the purposes of comparison, various types of ED problems are examined. The results of this study show that the new proposed approach is able to find more economical loads than those determined by other methods.     

A new memetic algorithm approach for the price based unit commitment problem

Unit commitment (UC) is a very important optimization task, which plays a major role in the daily operation planning of electric power systems that is why UC is a core research topic attracting a lot of research efforts. An innovative method based on an advanced memetic algorithm (MA) for the solution of price based unit commitment (PBUC) problem is proposed. The main contributions of this paper are: (i) an innovative two-level tournament selection, (ii) a new multiple window crossover, (iii) a novel window in window mutation operator, (iv) an innovative local search scheme called elite mutation, (v) new population initialization algorithm that is specific to PBUC problem, and (vi) new PBUC test systems including ramp up and ramp down constraints so as to provide new PBUC benchmarks for future research. The innovative two-level tournament selection mechanism contributes to the reduction of the required CPU time. The method has been applied to systems of up to 110 units and the results show that the proposed memetic algorithm is superior to other methods since it finds the optimal solution with a high success rate and within a reasonable execution time.     

A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead (NM) simplex search called FAPSO-NM. In the resulting hybrid algorithm, the NM algorithm is used as a local search algorithm around the global solution found by FAPSO at each iteration. Therefore, the proposed approach improves the performance of the FAPSO algorithm significantly. The algorithm is tested on two typical systems consisting of 13 and 40 thermal units whose incremental fuel cost functions take into account the valve-point loading effects.     

A new hybrid approach for nonconvex economic dispatch problem with valve-point effect

In literature, economic dispatch problems are generally categorized as convex and nonconvex optimization problems. In this study, a solution is proposed for economic dispatch problem with valve point effect, which is one of the nonconvex optimization problems. For this reason the hybrid approach used for solution of this problem is formed as a combination of modified subgradient (MSG) and harmony search (HS) algorithms. This approach (MSG-HS) is applied in three different lossy test systems (Three machines 6-bus, IEEE 5-machines 14-bus, IEEE 6-machines 30-bus systems) solved with different methods in the literature. System losses are calculated by using B loss matrix. The resulting optimal solution values are compared with the solution values in the literature and the results are discussed.     

Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder-Mead algorithm

In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder-Mead (NM) method (called BF-NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system.     

Particle swarm optimization for AC-coupling stand alone hybrid power systems

The main purpose of this study was to present a technique on how to optimize the configuration of a typical AC-coupling stand alone hybrid power system (SAHPS). The design was posed as an optimization problem whose solution allowed obtaining the configuration of the SAHPS that minimized the total cost through the useful life of the system. To verify the system component models, an existing PV/wind/diesel hybrid power system at Chik Island, Thailand, was selected as a reference system, and the in situ monitoring results were compared with the simulation results. The minimization of the objective function was evaluated using TRNSYS 16 in assistance with GenOpt (optimization program). The result showed that the overall best cost reduction has been achieved by the particle swarm optimization (PSO) with constriction coefficient algorithm. This method requires just a few seconds to give the best results (where the number of generations in the algorithm is 46). It is thus believed that the present method would decrease the time required by design engineers to find the SAHPS optimum solution.     

Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode

Photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is intermittent because of depending on weather conditions. Therefore, the wind power can be considered to assist for a stable and reliable output from the PV generation system for loads and improve the dynamic performance of the whole generation system in the grid connected mode. In this paper, a novel topology of an intelligent hybrid generation system with PV and wind turbine is presented. In order to capture the maximum power, a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. The average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison with the conventional methods. The pitch angle of the wind turbine is controlled by radial basis function network-sliding mode (RBFNSM). Different conditions are represented in simulation results that compare the real power values with those of the presented methods. The obtained results verify the effectiveness and superiority of the proposed method which has the advantages of robustness, fast response and good performance. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.     

Artificial bee colony optimization for economic dispatch with valve point effect

In recent years, various heuristic optimization methods have been proposed to solve economic dispatch (ED) problem in power systems. This paper presents the well-known power system ED problem solution considering valve-point effect by a new optimization algorithm called artificial bee colony (ABC). The proposed approach has been applied to various test systems with incremental fuel cost function, taking into account the valve-point effects. The results show that the proposed approach is efficient and robust when compared with other optimization algorithms reported in literature.     

A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm

In this paper, the genetic algorithm (GA) is applied to optimize a grid connected solar photovoltaic (PV)-wind-battery hybrid system using a novel energy filter algorithm. The main objective of this paper is to minimize the total cost of the hybrid system, while maintaining its reliability. Along with the reliability constraint, some of the important parameters, such as full utilization of complementary nature of PV and wind systems, fluctuations of power injected into the grid and the battery’s state of charge (SOC), have also been considered for the effective sizing of the hybrid system. A novel energy filter algorithm for smoothing the power injected into the grid has been proposed. To validate the proposed method, a detailed case study has been conducted. The results of the case study for different cases, with and without employing the energy filter algorithm, have been presented to demonstrate the effectiveness of the proposed sizing strategy.     

A comprehensive review on choice of hybrid vehicles and power converters,control strategies for hybrid electric vehicles

A worldwide shift headed for a greener and low emissions will necessitate remarkable advancement in the way in which the energy is being produced and used. The factors such as climate changes induced by pollution, progressively more strict emissions norms for vehicles, depletion of petrol/diesel along with instability in their prices for transportation systems, play a vital role in the improvisation of technology involved in conventional vehicles. The hybrid electric vehicles (HEVs) are on the peak of the list of choices available for clean vehicle technologies. The various architectures of HEV, different methodologies of hybrid vehicle, are focused in this paper. The design criteria and optimization techniques with reference to the driving cycle is also elucidated. The various electric drives used for HEV are discussed in this paper. Also, the different electric propulsion systems are explained. To improve the fuel economy and emission of hybrid power system, control strategies are very significant. Researchers concentrate in optimizing the performance of HEV.     

Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach

Optimal reactive power dispatch (ORPD) problem is an important problem in the operation of power systems. It is a nonlinear and mixed integer programming problem, which determines optimal values for control parameters of reactive power producers to optimize specific objective functions while satisfying several technical constraints. In this paper, stochastic multi-objective ORPD (SMO-ORPD) problem is studied in a wind integrated power system considering the loads and wind power generation uncertainties. The proposed multi objective optimization problem is solved using ε-constraint method, and fuzzy satisfying approach is employed to select the best compromise solution. Two different objective functions are considered as follow: 1) minimization of the active power losses and 2) minimization of the voltage stability index (named L-index). In this paper VAR compensation devices are modeled as discrete variables. Moreover, to evaluate the performance of the proposed method for solution of multi-objective problem, the obtained results for deterministic case (DMO-ORPD), are compared with the available methods in literature. The proposed method is examined on the IEEE-57 bus system. The proposed models are implemented in GAMS environment. The numerical results substantiate the capability of the proposed SMO-ORPD problem to deal with uncertainties and to determine the best settings of control variables.     

Interactive DE for solving combined security environmental economic dispatch considering FACTS technology

This paper presents an efficient interactive differential evolution (IDE) to solve the multi-objective security environmental/economic dispatch (SEED) problem considering multi shunt flexible AC transmission system (FACTS) devices. Two sub problems are proposed.The first one is related to the active power planning to minimize the combined total fuel cost and emissions, while the second is a reactive power planning (RPP) using multi shunt FACTS device based static VAR compensator (SVC) installed at specified buses to make fine corrections to the voltage deviation, voltage phase profiles and reactive power violation. The migration operation inspired from biogeography-based optimization (BBO) algorithm is newly introduced in the proposed approach, thereby effectively exploring and exploiting promising regions in a space search by creating dynamically new efficient partitions. This new mechanism based migration between individuals from different subsystems makes the initial partitions to react more by changing experiences. To validate the robustness of the proposed approach, the proposed algorithm is tested on the Algerian 59-bus electrical network and on a large system, 40 generating units considering valve-point loading effect. Comparison of the results with recent global optimization methods show the superiority of the proposed IDE approach and confirm its potential for solving practical optimal power flow in terms of solution quality and convergence characteristics.     

Accelerated solution of the transmission maintenance schedule problem: A Bayesian optimization approach

To maximize the maintenance willingness of the owner of transmission lines, this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on- site maintenance operations. Considering the computational complexity of the mixed integer programming (MIP) problem, a machine learning (ML) approach is presented to solve the transmission maintenance scheduling model efficiently. The value of the branching score factor value is optimized by Bayesian optimization (BO) in the proposed algorithm, which plays an important role in the size of the branch-and-bound search tree in the solution process. The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.     

Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm

This paper described the production of karanja biodiesel using response surface methodology (RSM) and genetic algorithm (GA). The optimum combination of reaction variables were analyzed for maximizing the biodiesel yield. The yield obtained by the RSM was 65% whereas the predicted value was 70%. The mathematical regression model proposed from the RSM was coupled with the GA. By using this technique, 90% of the yield was obtained at a molar ratio of 38, a reaction time of 8 hours, a reaction temperature of 40 oC, a catalyst concentration of 2% oil, and a mixing speed of 707 r/min. The yield produced was closer to the predicted value of 94.2093%. Hence, 25% of the improvement in the biodiesel yield was reported. Moreover the different properties of karanja biodiesel were found closer to the American Society for Testing & Materials (ASTM) standard of biodiesel.     

Lift-and-project MVEE based convex hull for robust SCED with wind power integration using historical data-driven modeling approach

This paper presents an adjustable robust security constrained economic dispatch (SCED) model with wind power uncertainties. First, the scenario based adjustable robust SCED model is presented. It considers multiple scenarios from historical data as well as the spatial correlation among wind farms. Then, the proposed SCED model becomes an optimization problem with a large amount of constraints which is skillfully solved using a lift-and-project minimum volume enclosing ellipsoid (MVEE) based convex hull. Furthermore, the proposed model is transformed into a second order cone programming (SOCP) model by the use of participation factors to generate adjustable generation outputs and thus guarantee the energy balance. In order to further reduce the computational complexity, the inactive constraints reduction strategy is proposed to quickly eliminate inactive SOC security constraints before solving the model. Numerical results of IEEE 14-bus and 118-bus test systems as well as the practical Polish power systems with several wind farms show that the proposed model can achieve better economies. Moreover, more than 82% of security constraints are identified as inactive in various cases of the simulation, and the proposed inactive constraints reduction strategy is promising for improving the computational performance.     

A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research.     

A smooth co-ordination control for a hybrid autonomous power system (HAPS) with battery energy storage (BES)

The standalone hybrid power system constitutes a synchronous generator driven by a diesel engine, renewable energy source (wind) apart from a battery energy storage system. A coherent control strategy to regulate the voltage and frequency of the standalone grid is proposed in this paper. The system is simulated using Matlab/Simulink for preliminary validation and further tested on a laboratory prototype which involves a TMS320LF2407A DSP controller to digitally implement the control strategy. The dynamic behavior of the system is perused through the direct connection of an induction machine. The control strategy is verified for step changes in load and variation in wind power.