New metaheuristic methodology for solving security constrained hydrothermal unit commitment based on adaptive genetic algorithm

A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints

This study presents a multi-objective based on a hybrid methodology to apply the problem of economic emission dispatch that is incorporated with hydro, thermal and wind power units. The hybrid method is the combination of both the modified quantum-behaviour lightning search algorithm (MQLSA) and artificial intelligence technique with the help of PSNN. To determine the optimal amounts of the generated powers from the thermal, wind farms and hydro units, the proposed approach is introduced by minimising the cost of generation and the emission level simultaneously. MQLSA is utilised to generate the optimal combination of thermal power with the objective of the minimum error function to minimise the fuel and emission cost of the system with wind speed factor. To capture the optimised wind power with the objective of minimum speed factor, the PSO-artificial neural network technique is utilised. The algorithm is integrated with the feasible solution constraint handling techniques. To validate the effectiveness of the proposed method, the six and ten generating thermal system with wind power is studied. The proposed hybrid technique is implemented in MATLAB/Simulink platform and the simulation result demonstrates the superiority of the proposed method compared to the various existing techniques.

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

The Economic Dispatch (ED) is one of the most important optimization problems in power systems the ultimate goal of the ED is to minimize the cost of operations in a power generation. In this paper, the Woodpecker Mating Algorithm (WMA) is used to solve the ED problem considering the nonlinear properties of generators such as valve point effects (VPE), prohibited operating zones (POZ), ramp rate limits, multiple fuel options, and transmission loss. The WMA algorithm is a novel metaheuristic algorithm inspired by the mating behavior of woodpeckers and sound intensity (a physical quantity). The WMA is implemented on six test systems of different operational dimensions and characteristics to show its capacity for solving the ED problem. The results are compared with the latest and most efficient methods introduced in the literature. Proving the efficiency of the WMA to solve the ED problem, simulation results are promising and offer the optimal fuel cost of production.

Considering different character between hydroelectric power and thermal power contributed to tie-line, as well as the cost function of the whole interconnected system under electric power market environments, this paper presents a new optimization strategy based on benefit analysis to solve the power regulation on tie-line in hydro-thermal interconnected power system. The units relating to tie-line can be procured by an electrical dissection algorithm in the sending and receiving network and matched with different correlative coefficient, while the other units are also due to be coordinated with diverse regulating power capacity at the same time. The effectiveness of the proposed method is demonstrated on IEEE 57-bus system comprising of hydro and thermal units and compared with some conventional approaches. The experimental results show that the proposed new strategy approaches is able to obtain higher quality solutions efficiently than the conventional approaches.

This paper presents new solution methods and results based on a particle swarm optimization (PSO) for solving the hydro generation scheduling problem. Complete solution algorithms and encoding/decoding techniques are proposed in the paper. The optimal schedules of both hydro and thermal units are concurrently obtained within the evolutionary process of evaluation functions. Significantly, no hydro-thermal iteration is needed. The proposed approach is applied with success to an actual utility system, which consists of three hydro plants in cascade and 22 thermal units. The results indicate the attractive properties of the PSO approach in practical application, namely, a highly optimal solution cost and more robust convergence behavior.

A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydro thermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms(GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation is directly controlled during evolution using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behavior of the algorithm.

Optimization model is build for solving the aircraft departure sequencing problem in this paper first. Then, an improved genetic algorithm (GA) using symbolic coding is proposed, where a type of total probability crossover and big probability mutation are performed. In this way, the evolutionary policy of Particle Swarm Optimization (PSO) is absorbed into the improved GA, which reduces the complexity and enhance the efficiency greatly. Last, a simulation program using basic GA, adaptive GA, and improved GA is performed. The simulation result shows that the model is effective and Improved GA has better performance than Basic GA or Adaptive GA.

The Genetic Algorithm (GA) is an explore technique used to solve issues in many different applications. The genetic algorithm has some parameters, including crossover probability, selection mechanism, and mutation probability. In GA, parameter adaptation is an important research topic. This paper proposes a Probabilistic Adaptive Genetic Algorithm in which the mutation and crossover probabilities, as well as the selection mechanism are dynamically adapted throughout the running of the algorithm. A new set of rates is generated for the next iteration based on the differences between fitness values and individual, enhancing the searching global optimum exploitation. We have compared the proposed algorithm with some common and state-of-the-art adaptive strategies such as dynamic adaptive, dynamic deterministic, dynamic self-adaptive, and static on a set of several functions with varying degrees of complexity. Experimental results on several popular test functions have shown that the results of the proposed algorithm are significantly better than these methods on both convergence speed and the solutions' quality.The reason that the proposed method has better results than other methods is the adaptation of each parameter of the genetic algorithm.

Combined heat and power (CHP) is the fuel unbiased method of production of heat energy and electrical power. Increased efficiency to the tune of 80% and above and reduced emission are two key attributes of this method as the production is obtained in a single process. This dissertation considered prohibited operating zones (POZ) and valve point effect (VPE) of generators in the study of Static Optimal load flow of CHP with the objective to reduce production cost while voltage level of buses are maintained within specified limit in order to satisfy the electrical power and heat demands. Optimization of load flow through such a power system is done using bio-inspired Social Spider algorithm. The distance of spider from food is defined as objective function. Two test systems having six generators in each have been used in IEEE 30 bus framework to demonstrate the efficacy of this algorithm. Results show that this method is competent enough to solve problem of multi objective optimization.

This paper proposes an efficient chaotic differential harmony search (CDHS) algorithm to solve the dynamic economic and emission dispatch (DEED) problem with security constraints. The main objective of DEED problem is to determine the optimal power generation schedule for the online generating units over a time horizon at the same time satisfying the environmental and security constraints along with other generator constraints. The DEED problem is formulated as a nonlinear constrained multi-objective optimization problem. The fuel cost and emission are considered as two competing objectives. It is converted into a single objective function by introducing a price penalty factor. The price penalty factor blends the emission with the fuel cost. In the proposed algorithm, chaotic variables are introduced instead of random number generator in the differential mutation operator of differential harmony search (DHS) algorithm to accelerate the DHS algorithm to reach the global optimal solution quickly. The effectiveness of the proposed CDHS algorithm is demonstrated on IEEE-30 bus system with valve point effects, ramp rate limits, prohibited operating zones, and security constraints for a period of 24 hours. The simulation results obtained by the proposed CDHS algorithm are compared with DHS algorithm. The optimal solution obtained by the proposed algorithm is better than DHS algorithm in terms of solution quality, speed of convergence and computational time.

In this paper we describe an efficient approach for multimodal function optimization using genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive genetic algorithm (AGA), the probabilities of crossover and mutation, p/sub c/ and p/sub m/, are varied depending on the fitness values of the solutions. High-fitness solutions are 'protected', while solutions with subaverage fitnesses are totally disrupted. By using adaptively varying p/sub c/ and p/sub ,/ we also provide a solution to the problem of deciding the optimal values of p/sub c/ and p/sub m/, i.e., p/sub c/ and p/sub m/ need not be specified at all. The AGA is compared with previous approaches for adapting operator probabilities in genetic algorithms. The Schema theorem is derived for the AGA, and the working of the AGA is analyzed. We compare the performance of the AGA with that of the standard GA (SGA) in optimizing several nontrivial multimodal functions with varying degrees of complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A comprehensive study of practical economic dispatch problems by a new hybrid evolutionary algorithm

With the rapid increase of penetration level of new energy power generation, the construction of auxiliary service market is facing new challenges. The stricter requirements of the power plant unit AGC are proposed by the power system. How to coordinate and control the output of hydropower and thermal power units to increase the use of hydropower while meeting the requirements of AGC regulation is a problem to be solved. Under the condition of interconnection of units in the whole network, an optimal control strategy for joint commissioning of hydropower and thermal power plants that meets the performance conditions of ‘two rules’ is proposed in this paper. The strategy establishes the connection between hydropower and thermal power units. Combined with the output characteristics and economic characteristics of hydropower units and thermal power units, a coordinated optimization control strategy model of hydropower and thermal power AGC units suitable for the ’two rules’ assessment rules is proposed for each objective and constraint condition in the joint scheduling of hydropower and thermal power. The optimal mathematical model with multiple objectives is constructed to realize the distribution of AGC among different types of power plants and units, which can give full play to the regulation ability of hydropower and thermal power. The proposed strategy achieves the efficient use of clean energy and reduces the grid’s overall operating costs to achieve energy conservation and emission reduction. Simulation results verify the superiority of the proposed strategy.