Application of NSGA-II Algorithm to Generation Expansion Planning

Alexandre

Sustainable Cities and Society

et al. 2016

a b s t r a c tEuropean Union (EU) regulations aim to ensure that the energy performance of buildings meets the cost-optimality criteria for energy efficiency measures. The methodological framework proposed in EU Delegated Regulation 244 is addressed to national authorities (not investors); the optimal cost level is calculated to develop regulations applicable at domestic level. Despite the complexity and the large number of possible combinations of economically viable efficiency measures, the real options for improving energy performance available to decision makers in building retrofit can be established. Our study considers a multi-objective optimization approach to identify the minimum global cost and primary energy needs of 154,000 combinations of energy efficiency measures. The proposed model is solved by the NSGA-II multi-objective evolutionary algorithm. As a result, the cost-optimal levels and a return on investment approach are compared for a set of suitable solutions for a reference building. Eighteen combinations of retrofit measures are selected and an analysis of the influence of real options on investments is proposed. We show that a sound methodological approach to determining the advantages of this type of investment should be offered so that Member States can provide valuable information and ensure that the minimum requirements are profitable to most investors.

Section: A Multi-objective Approachmentioning

confidence: 99%

A comparison between cost optimality and return on investment for energy retrofit in buildings-A real options perspective

Alexandre

Sustainable Cities and Society

et al. 2016

Journal of Electrical and Computer Engineering

“…For further analysis, the IHS algorithm proposed in this paper, HS from [13], PSO from [14], Artificial Fish Swarm Algorithm (AFSA) from [15], Improved Ant Colony algorithm (IAC) from [16], Cross-Entropy Method (CE) [17][18][19], and two typical evolutionary multiobjective optimization algorithms, Nondominated Sorting Genetic Algorithm version II (NSGA-II) [20][21][22] and Multiobjective Particle Swarm Optimization Algorithm (MOPSO) [23][24][25], are compared in optimizing the power network planning. In the case study, 50 independent runs were made for each of the optimization methods involving 50 different initial trial solutions for each optimization method.…”

Section: Example Analysismentioning

confidence: 99%

An Improved Harmony Search Algorithm for Power Distribution Network Planning

Sun

Chang

2015

Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS) algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS) algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

“…Reactive power support is limited by the capacity of reactive compensation equipments installed (13). Reactive power curtailment may be correlated with the active power curtailment, which is modeled through (14). The tap changer setting will be optimized and can vary within the bounds given by (15).…”

Section: ) Production Simulationmentioning

confidence: 99%

Generation expansion planning considering integrating large-scale wind generation

Zhang

Ding

Østergaard

et al. 2013

Abstract-Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation while satisfying technical and economical constraints. In the deregulated and competitive environment, large-scale integration of wind generation (WG) in power system has necessitated the inclusion of more innovative and sophisticated approaches in power system investment planning. A bi-level generation expansion planning approach considering large-scale wind generation was proposed in this paper. The first phase is investment decision, while the second phase is production optimization decision. A multi-objective PSO (MOPSO) algorithm was introduced to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of the proposed bi-level planning approach and the MOPSO algorithm have been verified by a numerical test system.