Hybrid Mean-Variance Mapping Optimization for Non-Convex Economic Dispatch Problems

2017 ◽  
Vol 8 (4) ◽  
pp. 34-59
Author(s):  
Truong H. Khoa ◽  
Pandian M. Vasant ◽  
Balbir Singh Mahinder Singh ◽  
V. N. Dieu
Keyword(s):  
Cost Saving ◽  
Power Systems ◽  
Optimization Problems ◽  
Economic Dispatch ◽  
Objective Functions ◽  
Fuel Cost ◽  
Solution Quality ◽  
Prohibited Operating Zones ◽  
System Generation ◽  
Mean Variance

The economic dispatch (ED) is one of the important optimization problems in power system generation for fuel cost saving. This paper proposes a hybrid variant of mean-variance mapping optimization (MVMO-SH) for solving such problem considering the non-convex objective functions. The new proposed method is a hybrid variant of the original mean-variance mapping optimization algorithm (MVMO) with the embedded local search and multi-parent crossover to enhance its global search ability and improve solution quality for optimization problems. The proposed MVMO-SH is tested on different non-convex ED problem including valve point effects, multiple fuels and prohibited operating zones characteristics. The result comparisons from the proposed method with other methods in the literature have indicated that the proposed method is more robust and provides better solution quality than the others. Therefore, the proposed MVMO-SH is a promising method for solving the complex ED problems in power systems.

Multiobjective Programming

2014 ◽  
pp. 1585-1604
Author(s):  
Minghe Sun
Keyword(s):  
Programming Problem ◽  
Multiobjective Programming ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Objective Functions ◽  
Solution Quality ◽  
Solution Methods ◽  
Multiobjective Programming Problem ◽  
Feasible Solutions ◽  
Multiobjective Programming Problems

Optimization problems with multiple criteria measuring solution quality can be modeled as multiobjective programming problems. Because the objective functions are usually in conflict, there is not a single feasible solution that can optimize all objective functions simultaneously. An optimal solution is one that is most preferred by the decision maker (DM) among all feasible solutions. An optimal solution must be nondominated but a multiobjective programming problem may have, possibly infinitely, many nondominated solutions. Therefore, tradeoffs must be made in searching for an optimal solution. Hence, the DM's preference information is elicited and used when a multiobjective programming problem is solved. The model, concepts and definitions of multiobjective programming are presented and solution methods are briefly discussed. Examples are used to demonstrate the concepts and solution methods. Graphics are used in these examples to facilitate understanding.

scholarly journals Optimal economic dispatch of power generation solution using lightning search algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 371
Author(s):  
Murad Yahya Nassar ◽  
Mohd Noor Abdullah ◽  
Asif Ahmed Rahimoon
Keyword(s):  
Power Generation ◽  
Power System ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Thermal Power ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Fuel Price ◽  
Solution Quality ◽  
Generation Cost

Economic dispatch (ED) is the power demand allocating process for the committed units at minimum generation cost while satisfying system and operational constraints. Increasing cost of fuel price and electricity demand can increase the cost of thermal power generation. Therefore, robust and efficient optimization algorithm is required to determine the optimal solution for ED problem in power system operation and planning. In this paper the lightning search algorithm (LSA) is proposed to solve the ED problem. The system constraints such as power balance, generator limits, system transmission losses and valve-points effects (VPE) are considered in this paper. To verify the effectiveness of LSA in terms of convergence characteristic, robustness, simulation time and solution quality, the two case studies consists of 6 and 13 units have been tested. The simulation results show that the LSA can provide optimal cost than many methods reported in literature. Therefore, it has potential to solve many optimization problems in power dispatch and power system applications.

A Strategic Review on Economic Dispatch Distribution and Environmental Considerations

2019 ◽  
Vol 118 (12) ◽  
pp. 166-176
Author(s):  
Dr. Karim MH ◽  
Seied Beniamin Hosseini ◽  
Dr. Ayesha Farooq ◽  
Hossein (Adib) Arab ◽  
Ali Takroosta
Keyword(s):  
Power Systems ◽  
Economic Dispatch ◽  
Environmental Concerns ◽  
External Costs ◽  
Fuel Cost ◽  
Strategic Review ◽  
Environmental Considerations ◽  
Distribution Problems

Power systems contain four generic parts, including production, transmission, dispatch distribution, and consumption. Generally, dispatch distribution between powerhouses modelled with the goal of minimisation in utilisation cost. However, Environmental concerns were given more attention to powerhouse emissions such as SO2, CO2 and NO cause to investigate modelling in recent researches. However consideration of the objectives of fuel cost and emission value in the dispatch distribution problems known as the eco-environmental dispatch distribution. Although Due to the paradox between the reduction of utility costs and external costs, different methods used.

Swarm-Based Mean-Variance Mapping Optimization (MVMOS) for Solving Non-Convex Economic Dispatch Problems

2015 ◽  
pp. 211-251 ◽  
Author(s):  
Truong Hoang Khoa ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
Vo Ngoc Dieu
Keyword(s):  
Optimization Technique ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Mapping Function ◽  
Population Based ◽  
Optimization Techniques ◽  
Solution Quality ◽  
Complex Constraints ◽  
Global Optimal ◽  
Mean Variance

The practical Economic Dispatch (ED) problems have non-convex objective functions with complex constraints due to the effects of valve point loadings, multiple fuels, and prohibited zones. This leads to difficulty in finding the global optimal solution of the ED problems. This chapter proposes a new swarm-based Mean-Variance Mapping Optimization (MVMOS) for solving the non-convex ED. The proposed algorithm is a new population-based meta-heuristic optimization technique. Its special feature is a mapping function applied for the mutation. The proposed MVMOS is tested on several test systems and the comparisons of numerical obtained results between MVMOS and other optimization techniques are carried out. The comparisons show that the proposed method is more robust and provides better solution quality than most of the other methods. Therefore, the MVMOS is very favorable for solving non-convex ED problems.

scholarly journals A Brief Look at Multi-Criteria Problems: Multi-Threshold Optimization versus Pareto-Optimization

2020 ◽  
Author(s):  
Nodari Vakhania ◽  
Frank Werner
Keyword(s):  
Optimization Problems ◽  
Optimality Criteria ◽  
General Notion ◽  
Objective Functions ◽  
Objective Criterion ◽  
Solution Quality ◽  
Pareto Optimal Set ◽  
Threshold Optimization ◽  
Feasible Solutions

Multi-objective optimization problems are important as they arise in many practical circumstances. In such problems, there is no general notion of optimality, as there are different objective criteria which can be contradictory. In practice, often there is no unique optimality criterion for measuring the solution quality. The latter is rather determined by the value of the solution for each objective criterion. In fact, a practitioner seeks for a solution that has an acceptable value of each of the objective functions and, in practice, there may be different tolerances to the quality of the delivered solution for different objective functions: for some objective criteria, solutions that are far away from an optimal one can be acceptable. Traditional Pareto-optimality approach aims to create all non-dominated feasible solutions in respect to all the optimality criteria. This often requires an inadmissible time. Besides, it is not evident how to choose an appropriate solution from the Pareto-optimal set of feasible solutions, which can be very large. Here we propose a new approach and call it multi-threshold optimization setting that takes into account different requirements for different objective criteria and so is more flexible and can often be solved in a more efficient way.

Fuel Cost Optimization of Power Systems Inclouding HVDC Line

2012 ◽  
Vol 488-489 ◽  
pp. 1788-1792 ◽  
Author(s):  
Babak Abdi ◽  
Arash Alimardani ◽  
Reza Ghasemi
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transmission Lines ◽  
Optimal Power Flow ◽  
Cost Optimization ◽  
Problem Definition ◽  
Objective Functions ◽  
Fuel Cost ◽  
Hvdc Transmission ◽  
Hvdc Transmission Lines

Effect of HVDC transmission lines in a power system on different optimal power flow (OPF) objective functions is discussed in this paper. In this study differential evolution optimization algorithm is applied in AC-DC OPF problem, and compared with OPF in the same power system with no HVDC transmission lines to demonstrate the effect of this type of transmission line on the objective functions. In OPF problem definition, generator fuel cost considering valve effect is considered as objective function. The results of the proposed method on IEEE 30-bus power system illustrate that HVDC transmission lines improves the OPF from fuel cost point of view.

Power Economic Dispatch Problems Considering Prohibited Operating Zones and Spinning Reserve: A Hybrid Differential Evolution

2012 ◽  
Vol 229-231 ◽  
pp. 2701-2707
Author(s):  
Chao Lung Chiang
Keyword(s):  
Differential Evolution ◽  
Economic Dispatch ◽  
Inequality Constraints ◽  
Small Population ◽  
Computational Results ◽  
Fuel Cost ◽  
Spinning Reserve ◽  
Prohibited Operating Zones ◽  
Equality And Inequality Constraints ◽  
Power Economic

This paper proposes a hybrid differential evolution (HDE) for power economic dispatch (PED) considering units with prohibited operating zones (POZ) and spinning reserve. The HDE equipped with an accelerated operation and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the system. To show the advantages of the proposed algorithm, one example is investigated, and the computational results of the proposed method are compared with that of the previous methods. The proposed approach integrates the HDE and the MU, revealing that the proposed approach has the following merits - ease of implementation; applicability to non-convex fuel cost functions; better effectiveness than previous methods; better efficiency than differential evolution with the MU (DE-MU), and the requirement for only a small population in applying the optimal PED problem of generators with POZ and spinning reserve.

scholarly journals METHODS OF SOLVING MULTI-AREA ECONOMIC DISPATCH PROBLEMS- A SURVEY AND PROPOSED CANDIDATE SDP APPROACH

2020 ◽  
Author(s):  
Kolapo Alli ◽  
Haniph Latchman
Keyword(s):  
Power Systems ◽  
Economic Dispatch ◽  
Objective Functions ◽  
Control Power ◽  
Optimal Load ◽  
Running Cost ◽  
Power Limits ◽  
And Performance ◽  
Computational Intelligence Methods ◽  
Tie Line

Computational intelligence methods may be effectively used to control power system settings automatically to achieve optimal operating power systems objective functions and ensure optimal load flows while fulfilling system constraints. Economic dispatch of power systems involving large interconnected areas or zones require optimum and efficient allocation of the power to ensure efficient transferred output power to the systems in the various zones. One approach in achieving optimum dispatch of the power generated is to model the system as a Multi Area Emission Economic Dispatch (MAEED) optimization problem. One such formulation could be the minimization the running cost and emission objective functions subject to generator power limits, power balance and tie-line capacity constraints. This paper provides a survey of some of the existing research on both single area and multi area economic dispatch problems respectively and discusses the associated methods used in solving these different problems. Based on this study, we propose a candidate approach to address multi area economic dispatch problems using a semidefinite programming (SDP) method and we outline the associated computational and performance advantages.

scholarly journals An improved flower pollination solution for economic dispatch with valve point effect

2021 ◽  
Vol 22 (2) ◽  
pp. 629
Author(s):  
Muhammad Iqbal Kamboh ◽  
Nazri Mohd Nawi ◽  
Radiah Bt. Mohamad
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Thermal Power ◽  
Economic Dispatch ◽  
Evolutionary Programming ◽  
Flower Pollination Algorithm ◽  
Cost Curve ◽  
Fuel Cost ◽  
Flower Pollination ◽  
Valve Point Effect

<span>The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions.  However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems.  The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.</span>

scholarly journals Solving Power Economic Dispatch Problem with a Novel Quantum-Behaved Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Li Ping ◽  
Jun Sun ◽  
Qidong Chen
Keyword(s):  
Power Systems ◽  
Local Search ◽  
Optimization Algorithms ◽  
Economic Dispatch ◽  
Ramp Rate ◽  
Solution Quality ◽  
Nonlinear Features ◽  
Gaussian Probability Distribution ◽  
Ramp Rate Limits ◽  
Search Capability

This paper proposes the shrink Gaussian distribution quantum-behaved optimization (SG-QPSO) algorithm to solve economic dispatch (ED) problems from the power systems area. By shrinking the Gaussian probability distribution near the learning inclination point of each particle iteratively, SG-QPSO maintains a strong global search capability at the beginning and strengthen its local search capability gradually. In this way, SG-QPSO improves the weak local search ability of QPSO and meets the needs of solving the ED optimization problem at different stages. The performance of the SG-QPSO algorithm was obtained by evaluating three different power systems containing many nonlinear features such as the ramp rate limits, prohibited operating zones, and nonsmooth cost functions and compared with other existing optimization algorithms in terms of solution quality, convergence, and robustness. Experimental results show that the SG-QPSO algorithm outperforms any other evaluated optimization algorithms in solving ED problems.

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