Environmental Economic Dispatch Research Articles

Coordinated consensus for smart grid economic environmental power dispatch with dynamic communication network

Combined economic environmental dispatch problem (CEEDP) is one of the greatest challenges of the future smart grids. It aims at reducing the total cost during the power production process considering the growing environmental impact due to the emission of gaseous pollutants of fossil fuels. This study develops a robust distributed algorithm based on consensus protocols in multi-agent systems, to solve the smart grid CEEDP with a practical communication network consisting of a dynamic communication network, randomly communication failure, transmission delay and noise in communication channels. The proposed algorithm is fully distributed and cooperative in such a way that it eliminates the need for a central energy-management unit, or a leader. The performance of the fully decentralised consensus protocol was evaluated on the IEEE 30-bus and the IEEE 118-bus test system. A comparison with previous consensus algorithms proves the supremacy of the proposed approach in terms of its robustness under dynamic communication network with randomly link failure.

Environmental Economic Hydrothermal System Dispatch by Using a Novel Differential Evolution

This paper proposes the Novel Differential Evolution (NDE) method for solving the environmental economic hydrothermal system dispatch (EEHTSD) problem with the aim to reduce electricity generation fuel costs and emissions of thermal units. The EEHTSD problem is constrained by limitations on generations, active power balance, and amount of available water. NDE applies two modified techniques. The first one is modified mutation, which is used to balance global and local search. The second one is modified selection, which is used to keep the best solutions. When performing this modified selection, the proposed method completely reduces the impact of crossover by setting it to one. Moreover, the task of tuning this factor can be canceled. Original Differential Evolution (ODE), ODE with the first modification (MMDE), and ODE with the second modification (MSDE), and NDE were tested on two different hydrothermal systems for comparison and evaluation purposes. The performance of NDE was also compared to existing methods. It was indicated that the proposed NDE is a very promising method for solving the EEHTSD problem.

Chaotic crow search algorithm for fractional optimization problems

This paper presents a chaotic crow search algorithm (CCSA) for solving fractional optimization problems (FOPs). To refine the global convergence speed and enhance the exploration/exploitation tendencies, the proposed CCSA integrates chaos theory (CT) into the CSA. CT is introduced to tune the parameters of the standard CSA, yielding four variants, with the best chaotic variant being investigated. The performance of the proposed CCSA is validated on twenty well-known fractional benchmark problems. Moreover, it is validated on a fractional economic environmental power dispatch problem by attempting to minimize the ratio of total emissions to total fuel cost. Finally, the proposed CCSA is compared with the standard CSA, particle swarm optimization (PSO), firefly algorithm (FFA), dragonfly algorithm (DA) and grey wolf algorithm (GWA). Additionally, the efficiency of the proposed CCSA is justified using the non parametric Wilcoxon signed-rank test. The experimental results prove that the proposed CCSA outperforms other algorithms in terms of quality and reliability.

A Modified Artificial Bee Colony Algorithm Application for Economic Environmental Dispatch

In conventional fossil-fuel power systems, the economic environmental dispatch (EED) problem is a major problem that optimally determines the output power of generating units in a way that cost of total production and emission level be minimized simultaneously, and at the same time all the constraints of units and system are satisfied properly. To solve EED problem which is a non-convex optimization problem, a modified artificial bee colony (MABC) algorithm is proposed in this paper. This algorithm by implementing weighted sum method is applied on two test systems, and eventually, obtained results are compared with other reported results. Comparison of results confirms superiority and efficiency of proposed method clearly.

Multiobjective Environment/Economic Power Dispatch Using Evolutionary Multiobjective Optimization

Environmental/economic dispatch (EED) problems play a salient role in the power system, which can be defined as a complex constrained optimization problem. Many different methods have been introduced to handle EED problems and got some inspiring positive results in the research. In this paper, a new multiobjective global best artificial bee colony (ABC) algorithm is proposed to tackle multiobjective EED problems. To manipulate this problem effectively, we propose a global best ABC algorithm to generate the new individual to speed up the convergence of the proposed algorithm. Afterwards, a crowding distance assignment approach is employed to evolve the population. Finally, a straightforward constraint checking procedure is used to tackle those different constraints of EED problems. Experimental results can conclude that MOGABC can provide best solutions in solving multiobjective EED problems.

Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem

The collective decision optimization algorithm (CDOA) is a new stochastic population‐based evolutionary algorithm which simulates the decision behavior of human. In this paper, a multiobjective collective decision optimization algorithm (MOCDOA) is first proposed to solve the environmental/economic dispatch (EED) problem. MOCDOA uses three novel learning strategies, that is, a leader‐updating strategy based on the maximum distance of each solution in an external archive, a wise random perturbation strategy based on the sparse mark around a leader, and a geometric center‐updating strategy based on an extreme point. The proposed three learning strategies benefit the improvement of the uniformity and the diversity of Pareto optimal solutions. Several experiments have been carried out on the IEEE 30‐bus 6‐unit test system and 10‐unit test system to investigate the performance of MOCDOA. In terms of extreme solutions, compromise solution, and three metrics (SP, HV, and CM), MOCDOA is compared with other existing multiobjective optimization algorithms. It is demonstrated that MOCDOA can generate the well‐distributed and the high‐quality Pareto optimal solutions for the EED problem and has the potential to solve the multiobjective optimization problems of other power systems.

NMEP based Gaussian Mutation Process on Optimizing Fitness Function for MOEED

The increment of Economic Dispatch (ED) problem is very distressing today. In view of countless of the researchers doing the research to minimize the ED problem day after day, the multi objective New Meta Heuristic Evolutionary Programming (NMEP) techniques are proposed to optimize the multi objective function in ED problem called as Multi Objective Environmental Economic Dispatch (MOEED). The techniques mimic the original Meta Heuristic Evolutionary Programming (Meta-EP) and merge with Artificial Immune System (AIS) with some improvement in Gaussian mutation process and cloning process. The NMEP produced two objective function result simultaneously by exercising the weighted sum method. In order to justify the result, the comparison between the NMEP and Meta-EP techniques is conducted with difference case number of alpha. Therefore, the outcome of the simulation shows the NMEP approach is better than Meta-EP in the both case numbers of alpha. The simulation is operated using MATLAB simulation based on standard IEEE 26 bus system in the laboratory.

Solving the Environmental Economic Dispatch Problem using Whale Optimization Algorithm

In this work, the Whale Optimization Algorithm method used to solve the Environmental Economic Dispatch Problem. The performance of the used algorithm is substantiated using standard test system of three thermal generating units. The proposed algorithm produced optimum or near optimum solutions. The obtained results in this study using the Whale Optimization Algorithm are compared with the obtained results using other intelligent methods such as Particle Swarm Optimization, Simple Genetic Algorithm and Genetic Algorithm. The comparison demonstrated the obtained results in this research are close to these obtained using the above revealed approaches.

A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems

Abstract Development of efficient multi-objective evolutionary algorithms (MOEAs) has provided effective tools to solve environmental/economic dispatch (EED) problems. EED is a highly constrained complex bi-objective optimization problem. Since 1990s, numerous publications have reported the applications of MOEAs to solve the EED problems. This paper surveys the state-of-the-art of research related to this direction. It covers topics of typical MOEAs, classical EED problems, Dynamic EED problems, EED problems incorporating wind power, EED problems incorporating electric vehicles and EED problems within micro-grids. In addition, some potential directions for future research are also presented.

Environmental economic dispatch using improved artificial bee colony algorithm

Due to emissions from fossil fuel consumption in power plants, not only operational costs, but the minimization of the resulting pollution should be also considered in environmental economic dispatch problem. In this research, environmental economic dispatch problem is solved by minimization of operational cost and environmental pollution considering nonlinear constraints of generating units, forbidden regions, and ramp-rate of generating units using an improved artificial bee colony technique. With the proposed approach, data transactions among bees have been conducted using Newton’s and gravitational laws, leading to a full employment of honey bees mating optimization’s capability in finding the optimum solution. In order to show the effectiveness of the proposed algorithm, it is tested on IEEE 6-bus and IEEE 11-bus power systems in different load levels. Then, the obtained results are compared with those of other previously validated techniques. It is revealed that the proposed technique is superior in terms of accuracy and speed in solving power system complex problems over the other methods. In addition, it is unlikely for this approach to be trapped in local minima. Results compared to many recent competitive methods confirm the efficiency of the proposed method in term of solution quality and convergence characteristics.

Emission‐economic dispatch of thermal power generation units in the presence of hybrid electric vehicles and correlated wind power plants

With the integration of plug-in hybrid electric vehicles (PHEVs) and renewable energy resources into the power system, the environmental-economic dispatch of conventional power generation units takes new dimensions due to several ineluctable uncertain factors. This study presents a probabilistic optimisation framework for the emission, economic dispatch of thermal power generation units, considering the stochastic charging demand of PHEVs, intermittent wind power generation and uncertain load demand. To characterise the uncertainty in the output variables, the point estimate method and Nataf transformation are employed, considering the correlation between wind power plants. To find Pareto optimal solutions concerning minimisation of the emission and fuel cost, heuristic fitness sharing mechanisms and a meta-heuristic algorithm are proposed. Monte Carlo simulation together with state-of-the-art optimisation techniques (NSGA-II and SPEA-2) are employed in a quantitative performance appraisal to scrutinise the effectiveness of the proposed method in the uncertainty quantification and multi-objective optimisation.

Erratum to: A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch

Multi-objective optimization is an interesting and hot topic in the literature involving the conflicting objectives to be solved simultaneously. In this study, a new multiple optimization method based on a discrete bacterial algorithm is developed to address the multi-objective economic-environmental dispatch problem with non-linear, non-convex, and complexity constraints. In the proposed multi-objective bacterial based algorithm, the existence of bacteria complies with a fitness survival mechanism, in which a health sorting approach is operated to control the chances of reproduction as well as elimination. The performances of bacteria have been recorded and sorted for health evaluation, which can help to group the individuals according to their search capability and improve the overall quality of the population. To speed up the convergence rate and avoid local minima to some extent, a comprehensive learning strategy is embedded to enable the communication exchanges between the bacteria and external archive. The standard IEEE 30-bus, 6-generator test system is adopted to illustrate the efficiency of the proposed method by making the comparison with the other multiple bacterial-based algorithms as well as six other well developed evolutionary algorithms. The effectiveness of the propose method is well validated in experiments by providing similar or superior solutions to environmental/economic power dispatch issues considering the various constraints.

Bi-level Multi-objective Environmental Economic Dispatch with Wind Farm

A bi-level multi-objective environmental economic dispatch model with wind farms is established based on decomposition coordination principle and users’ side resources. Considering the level of system load and users’ satisfaction degree, a multi-objective optimization mathematical model was established to determine fitting load of the system and time sharing price. Considering the randomness of wind power output, a multi-objective environmental economic dispatch model is established to determine the generation plan. A hybrid universal gravitation algorithm and tabu search algorithm is proposed to solve the model. The optimal scheduling scheme is determined by the method of approximate ideal solution based on entropy weight. Simulation results show that: Considering the influence of resources of source side in the scheduling process not only can improve load level of the system operation, the satisfaction of using electricity and absorptive capacity of wind power, but also can effectively in reduce the generation costs and pollution emissions.

Economic Environmental Dispatch for CombinedHeat and Power using CPSO

Objectives: In this paper a comparative analysis is presented for Economic Environmental Dispatch. Methods/Statistical Analysis: An optimization problem Combined Heat and Power Economic Emission Dispatch (CHPEED) is the answer of minimizing the cost and emission while keeping the heat and power demand. Between Construction Particle Swarm Optimization (CPSO) with (RCGA), (NSGAII), (SPEA2), a comparative analysis is presented. Findings: CPSO algorithm provides a competitive effectiveness in terms of solution quality and as far as CPU time is concerned, it leads to top notch performance.

Multi-objective Optimization of Economic Environmental Dispatch

Economic environment dispatch (EED) is a significant optimization problem in conventional fossil fuel fired power system to distribute load demand reasonably and scientifically so that fuel cost and emission issues are optimized simultaneously while satisfying various constraints. In response to this constrained multi-objective optimization problem including competing objectives as well as complex constraints, an novel multi-objective algorithm (MOCA-PSO) is proposed in this paper to solve EED problem. Simulation results of test system demonstrate that the capability of the proposed algorithm to generate well-distributed Pareto optimal solutions in a single run. Compared with some current methods, MOCA-PSO has a good performance in finding a diverse set of solutions and in converging near the true Pareto optimal front with lower fuel cost and emission issues synthetically.

Robust Variant of Artificial Bee Colony (JA-ABC4b) Algorithm

The simplicity and robustness of the artificial bee colony (ABC) algorithm has attracted the attention of optimisation researchers. Although ABC has fewer tuned parameters, making it an easy-to-use tool, it has shown better performance than other prominent optimisation algorithms such as differential evolution (DE), evolutionary algorithms (EA) and particle swarm optimisation (PSO) algorithms at solving optimisation problems. Despite these advantages, researchers have found that the standard ABC actually suffers from slow convergence speed on unimodal functions and is often trapped in local minima of multimodal functions. Most problematically, it does not balance the exploitation and exploration stages, leading to various inefficiencies in terms of capability. This paper presents a new ABC variant referred to as JA-ABC4b, which has been formulated to balance exploitation and exploration in order to boost optimisation performance. JA-ABC4b has been experimentally tested on 27 benchmark functions and economic environmental dispatch (EED) problems. The results have revealed a robust performance of JA-ABC4b in comparison to other existing ABC variants and other optimisation algorithms.

Mine blast algorithm for environmental economic load dispatch with valve loading effect

Economic load dispatch (ELD) is the process of allocating the required load between the available generation units such that the cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. The dual-objective combined economic emission dispatch (CEED) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fueled power plants. In this paper, an implementation of mine blast algorithm (MBA) to solve ELD and CEED problems in power systems is discussed. Results obtained by the proposed MBA are compared with other optimization algorithms for various power systems. The results introduced in this paper show that the proposed MBA outlasts other techniques in terms of total cost and computational time.

A fuzzy interval optimization-based approach to optimal generation scheduling in uncertainty environment

This paper proposes a fuzzy interval optimization approach to solve the Environmental/Economic Dispatch (EED) problem with uncertain parameters in the constraints and the objective functions. The objective functions considered are fuel cost and the gaseous emissions of the generating units. Two different types of fuel cost functions are considered in this study, namely, the conventional quadratic function and the augmented quadratic function to introduce more accurate modeling that incorporates the valve loading effects. The latter model presents non-differentiable and nonconvex regions that challenge most gradient-based optimization algorithms. In the proposed approach, objective functions are fuzzified and integrated to represent the fuzzy decision value. On the other hand, load uncertainties are modeled using fuzzy intervals. This fuzzy EED problem formulation provides a modeling flexibility, relaxation in constraints and allows the method to seek a practical solution. The obtained fuzzy multi-objective optimization problem is solved using non-dominated sorting genetic algorithm-II, known for its global searching capabilities, to get the best compromise among all the objectives. The performance of this solution is examined and applied to the standard IEEE 30-bus six-generator test system and the Indian power network of 82-bus by comparing its results with that of the existing methods. Different cases with different complexities have been considered in the study reported in this paper.

Convexification of the Nash Bargaining Based Environmental-Economic Dispatch

The environmental-economic dispatch (EED) is a bi-objective optimization problem. The Nash bargaining theory provides one way to determine a compromising solution without a clear carbon tax or carbon cap, or any subjective attitudes on both objectives, which yields a non-convex program. This letter proposes a convex formulation for Nash bargaining based EED and a linear programming (LP) based algorithm. Case studies show its scalability on real-world large-scale power systems.

Practical multi‐area bi‐objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm

Generation dispatch decision making integrated into a centralised wholesale electricity market has been always a challenging concern for transmission system operators. The reason lies mainly in the complex and highly-interconnected structure of the grid, escalated demand, and the crisis of the amount and price of energy. For the dispatch solutions to be highly reliable with an acceptable level of accuracy, several factors need to be taken into account, among which one can mention the valve-point effect, multiple fuel options, disjoint prohibited zones, up/down ramp rate requirements of units, as well as the spinning reserve constraints which, in turn, intensify the non-smoothness, non-convexity, non-linearity, and dynamic restriction of such combinatorial problems. This study proposes a new optimization toolset for the bi-objective multi-area economic dispatch problem to determine the transmission power flow and power output of units while satisfying system demand and security constraints at each area. The proposed architecture builds on an improved gradient-based Jaya algorithm to generate a feasible set of Pareto-optimal solutions corresponding to the operation cost and emission calculated through a new robust bi-objective gradient-based method. The projected algorithm is proved to be capable of finding the robust global or near-global Pareto solutions fast and accurate.