Multiobjective Environmental/economic Dispatch Research Articles

A multi-objective multi-verse optimizer algorithm to solve environmental and economic dispatch

The combustion and emission of coal have always been a concern. A class of multi-objective Environmental Economic Dispatch (EED) problems has been widely studied to reduce the pollution problem of fossil fuel power plants. In this study, a multi-objective Multi-Verse Optimization algorithm based on Gridded Knee Points and Plane Measurement technique (GKPPM-MVO) is proposed for the multi-objective EED problems. Knee points are usually considered as the most critical points in unbiased decision-making, while plane measurement can find the largest distant points in the population neighborhood. We apply the knee and maximum plane distance points in the local search phase. The original mechanism of parameter control in local search is replaced by using the two above points to exploit the pretty information and inherit it to the next generation. The algorithm is applied to various EED problems. The four algorithms, including MOMVO, NSGA-ii, MOABC, and MOEGO are also used to compare the performance of the algorithms thoroughly. Results show that the GKPPM-MVO algorithm has good convergence performance, high stability, and high uniformity of the Pareto Front.

Multi-Objective Environmental Economic Dispatch of an Electricity System Considering Integrated Natural Gas Units and Variable Renewable Energy Sources

This paper presents a multi-objective economic-environmental dispatch (MOEED) model for integrated thermal, natural gas, and renewable energy systems considering both pollutant emission levels and total fuel or generation cost aspects. Two cases are carried out with the IEEE 30-bus system by replacing thermal generation units into natural gas units to minimize the amount of toxin emission and fuel cost. Equality, inequality like active, reactive powers, prohibited operating zones (POZs) which represents poor operation in the generation cost function, and security constraints are considered as system constraints. Natural gas units (NGUs) are modeled in detail. Therefore, the flow velocity of gas and pressure pipelines are also considered as system constraints. Multi-objective optimization algorithms, namely multi-objective Harris hawks optimization (MOHHO) and multi-objective flower pollination algorithm (MOFPA) are employed to find Pareto optimal solutions of fuel or generation cost and emission together. Furthermore, the technique for order preference by similarity to ideal solution (TOPSIS) is proposed to obtain the best value of Pareto optimal solutions. Three scenarios are investigated to validate the effectiveness of the proposed model applied to the IEEE 30-bus system with the integration of variable renewable energy sources (VRESs) and natural gas units. The results obtained from Scenario III with NGUs installed instead of two thermal units reveal that the economic dispatching approach presented in this work can greatly minimize emission levels as 0.421 t/h and achieve lower fuel cost as 796.35 $/h. Finally, the results obtained show that the MOHHO outperforms the MOFPA in solving the MOEED problem.

Environmental Economic Dispatch Strategy for Power-Gas Interconnection System Considering Spatiotemporal Diffusion of Air Pollutant and P2G in Coastal Areas

This paper proposes an environmental economic dispatch strategy for power-gas interconnection system considering spatiotemporal diffusion of air pollutant and P2G in coastal areas. Firstly, considering the complex and changeable meteorological conditions in coastal areas and the regional characteristics of the atmospheric layer, an air pollutant diffusion model considering the local sea-land circulation and thermal inner boundary layer is studied. Then, a multi-objective environmental economic dispatch model is employed, and the economic cost, carbon emission quantities, and pollutant concentration contribution values are set as optimization objectives. Finally, we conduct the optimization with the multi-objective distributed algorithm based on ADMM. Cases study is carried out on IEEE39 power network and Belgium 20 gas network, which validate the effectiveness of the proposed strategy.

Solution of Multi Objective Environmental Economic Dispatch by Grey Wolf Optimization Algorithm

This paper presents the recently developed Grey Wolf Optimization (GWO) algorithm, which is based on the food collecting behavior of grey wolves to determining the feasible optimal solution of the multi objective environmental economic dispatch (MOEED) problem. Nonlinear characteristics of alternators and exponential emissions and loss minimization are considered in the problem. While searching for a better solution, GWO does not require any statistics about the gradient of the objective function. The GWO algorithm effectiveness has been tested on four different systems as 6-unit (IEEE 30-bus), 10-unit, 11-unit and 14-unit (IEEE 118-bus) test systems to solve the MOEED problems. The result of the test systems shows, for practical power systems GWO as a better option to solve the MOEED problems. Both the optimality of the solution to test systems and the convergence speed of the GWO algorithm are promising.

Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints

As the low-carbon economy continues to expand, wind power, as one form of clean energy, promotes the low-carbon power development process. In this paper, a multi-objective environmental economic dispatch (EED) model is proposed considering multiple uncertainties of the system. Carbon trading costs and green certificate trading costs are introduced into the economic costs. Meanwhile, the objective function of pollutant emissions is taken into account in the model, which can further promote the reduction of pollutant emissions in the system scheduling. The output of wind turbines is uncertain and volatile, so it brings new challenges to the power system EED once the large-scale wind power accesses the power grid. For the multiple uncertainties of the system, fuzzy chance-constrained programming is introduced, and the output of the wind turbines and the load are regarded as fuzzy variables. We use the clear equivalence forms to clarify the fuzzy chance constraints. The improved multi-objective standard particle swarm optimization (SPSO) algorithm is used to solve the optimization problem effectively. The feasibility and effectiveness of the proposed model and algorithm are verified by an example of a 10-unit system with two wind farms.

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.

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.

A multiobjective optimization method to environmental economic dispatch

An optimization technique based on progressive articulation of preference information is presented to solve the multiobjective environmental economic dispatch. For the multiobjective optimization problem, the use of weights to form a composite objective function reduces a multiple problem to a single problem. However, it also obviously “loses” some information in the conversion and this strategy is not expected to provide a robust solution or to even help trace the efficient frontier of solutions. Our main thrust is to facilitate a string of solutions of the problem without converting to the original problem to a simpler case. The proposed method handles the problem in an interactive way and does not need to know any global preference structure or some type of initial goals of the decision maker for the objectives. Numerical results for two case studies have been presented to illustrate the performance and applicability of the proposed method. The proposed method is compared with the genetic algorithm with arithmetic crossover and a neural network approach.