Generation Expansion Planning Problem Research Articles

An approach based on robust optimization and decision rules for analyzing real options in engineering systems design

ABSTRACTIn this article, a novel approach to analyze flexibility and real options in engineering systems design is proposed based on robust optimization and decision rules. A semi-infinite robust counterpart is formulated for a worst-case non-flexible Generation Expansion Planning (GEP) problem taken as a demonstration application. An exact solution methodology is proven by converting the model into an explicit mixed-integer programming model. Strategic capacity expansion flexibility—also referred to as real options—is analyzed in the GEP problem formulation and a multi-stage finite adaptability decision rule is developed to solve the resulting model. Finite adaptability relies on uncertainty set partitions, and in order to avoid arbitrary choices of partitions, a novel heuristic partitioning methodology is developed based on upper-bound paths to guide the partitioning of uncertainty sets. The modeling approach and heuristic partitioning methodology are applied to analyze a realistic GEP problem using data from the Midwestern United States. The case study provides insights on the convergence rates of the proposed heuristic partitioning methodology, decision rule performances, and the value of flexibility compared with non-flexible solutions, showing that explicit considerations of flexibility through real options can yield significant cost savings and improved system performance in the face of uncertainty.

Generation expansion planning with high share of renewables of variable output

This study presents a new generation expansion planning (GEP) incorporating the effects of renewables variable generation on thermal power plants efficiency. An hourly unit commitment problem was integrated in the GEP problem with the overall goal of supporting the selection of future mixes of power plants through long term planning. The problem resulted in a binary mixed integer non-linear cost optimization model. The model application was demonstrated for the design of electricity plans for a 10year planning period under different CO2 assumptions for a thermal, hydroelectric and wind power system. The results were compared with the ones obtained using a traditional GEP model, which assumed average operating conditions for thermal power plants. The scenario analysis shows that the impact of renewables variability on the performance of thermal power plants and on the generation expansion planning is non-negligible. The results suggest that assuming average operating conditions can result on the underestimation of the system costs which highlights the importance of the proposed integrated model to strategic decision making.

Chance and service level constraints for stochastic generation expansion planning

We consider the problem of generation expansion planning (GEP) under uncertainty. Existing and planned deployment of demand response mechanisms necessitates extensions of GEP optimization models to minimize planned investment portfolio cost. We propose a combination of service level threshold and chance constraints to abstractly capture the impact of demand response mechanisms in generation expansion planning. We examine the computational properties of solutions from the proposed optimization model using real-world data from South Korea. Our results indicate that the proposed models can be solved to optimality in tractable run-times, allowing for their use in decision support contexts and avoiding the need for heuristic solution procedures.

A linearization and parameterization approach to tri-objective linear programming problems for power generation expansion planning

The present study proposed a new solution to a tri-objective linear programming problem for generation expansion planning by converting the tri-objective linear programming problem (i.e. simultaneously maximizing the total power generation, minimizing the total system cost, and minimizing the total CO2 emission) into an equivalent bi-objective linear fractional programming problem (i.e. simultaneously maximizing the ratio of the total power generation to the total system cost, and the ratio of the total power generation to the total CO2 emission) to produce a better nondominated solution without any preference information from a decision maker. An approach for solving the bi-objective linear fractional programming problem is a newly developed linearization and parameterization approach based on Dinkelbach's theorem and Güzel's approach, which transforms all of linear fractional objective functions into a single objective linear programming problem. The proposed bi-objective fractional programming method was applied to a case study of power generation expansion planning problem. Moreover, comparison of the solutions generated by the proposed linearization and parameterization approach and a traditional weighted sum approach has been conducted to demonstrate the effectiveness of the proposed approach in reflecting the trade-offs among the total power generation, the total system cost and the total CO2 emission.

Energy efficiency resource modeling in generation expansion planning

Energy efficiency plays an important role in mitigating energy security risks and emission problems. In this paper, energy efficiency resources are modeled as efficiency power plants (EPP) to evaluate their impacts on generation expansion planning (GEP). The supply curve of EPP is proposed using the production function of electricity consumption. A decision making framework is also presented to include EPP in GEP problem from an investor's point of view. The revenue of EPP investor is obtained from energy cost reduction of consumers and does not earn any income from electricity market. In each stage of GEP, a bi-level model for operation problem is suggested: the upper-level represents profit maximization of EPP investor and the lower-level corresponds to maximize the social welfare. To solve the bi-level problem, a fixed-point iteration algorithm is used known as diagonalization method. Energy efficiency feed-in tariff is investigated as a regulatory support scheme to encourage the investor. Results pertaining to a case study are simulated and discussed.

Optimal generation expansion planning considering maximum loadability limit

This paper describes a new approach to generation expansion planning (GEP) problem to optimise simultaneously multiple objectives such as cost and stability margin. It is formulated as a combinational optimisation problem in order to determine the type and capacity of the new generations as well as their timing and placement. This study presents a comprehensive model of hierarchical optimisation problem to solve the GEP problem. The proposed method has investigated cost index and security margin of voltage stability of power systems by using loadability index. In this new approach, the GEP is done via expansion coordination pattern, and a trade-off between cost and stability has taken place. To solve the modelled optimisation problem, the imperialist competitive algorithm is used and tested on two test (standard) power systems consisting of 30 and 57 buses for a 15-year planning horizon. The results have been compared with hybrid algorithm genetic-particle swarm.

Comprehensive review of generation and transmission expansion planning

Investment on generation system and transmission network is an important issue in power systems, and investment reversibility closely depends on performing an optimal planning. In this regard, generation expansion planning (GEP) and transmission expansion planning (TEP) have been presented by researchers to manage an optimal planning on generation and transmission systems. In recent years, a large number of research works have been carried out on GEP and TEP. These problems have been investigated with different views, methods, constraints and objectives. The evaluation of researches in these fields and categorising their different aspects are necessary to manage further works. This study presents a comprehensive review of GEP and TEP problems from different aspects and views such as modelling, solving methods, reliability, distributed generation, electricity market, uncertainties, line congestion, reactive power planning, demand-side management and so on. The review results provide a comprehensive background to find out further ideas in these fields.

Application of Opposition-based Differential Evolution Algorithm to Generation Expansion Planning Problem

Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Opposition- based Differential Evolution (ODE) algorithms have been applied to the GEP problem. The original GEP problem has been modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units have been considered. The results have been compared with Dynamic Programming (DP) method. The ODE performs well and converges faster than DE.

Reliability constrained multi-period generation expansion planning of electrical energy resources using MILP

SUMMARY The main goal of Generation Expansion Planning (GEP) is to minimize total costs associated with new power generating units’ installation subject to technical and economical constraints. This paper addresses the GEP with probabilistic reliability criteria. The Loss-of-Load Probability reliability index is explicitly augmented as a new constraint which takes into account the reserve requirements. The outage cost is represented by the Expected Energy Not Served index. Due to nonlinear nature of these reliability indices, the GEP optimization problem with reliability criteria is very complicated to solve. Accordingly, the focus of this work is to deal with the reliability constrained multi-period GEP problem as a Mixed Integer Linear Programming (MILP). The results in the case study indicate the effect of reliability considerations on decision-making process. The simulation results also show the superiority of the proposed MILP-based method in comparison with the well-known metaheuristic algorithms and Dynamic Programming approach in the viewpoints of the accuracy and computational speed. Copyright © 2012 John Wiley & Sons, Ltd.

Optimization Generation Expansion Planning by HBMO

The generation expansion planning (GEP) problem is a largescale mixed integer nonlinear programming (MINLP) problem cited as one of the most complex optimization problems. In this paper, an application of honey bee mating optimization for solving the generation expansion planning problem is presented. In the formulation, the objective is to minimize investment cost. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

Application of Shuffled Frog Leaping Algorithm to Long Term Generation Expansion Planning

 Abstract—This paper presents the application of an efficient shuffled frog leaping algorithm (SFLA) to solve the optimal generation expansion planning (GEP) problem. The SFLA is a meta-heuristic search method inspired by natural memetics. It combines the advantages of both genetic-based memetic algorithms and social behavior based algorithm of particle swarm optimization. Least-cost GEP is concerned with a highly constrained non-linear discrete dynamic optimization problem. In this paper the proposed formulation of problem, determines the optimal investment plan for adding power plants over a planning horizon to meet the demand criteria, fuel mix ratio, and the reliability criteria. To test the proposed SFLA method, it is simulated for two test systems in a time horizon of 10 and 20 years respectively. The obtained results show that compared to the traditional methods, the SFLA method can provide better solutions for the GEP problem, especially for a longer time horizon.

Development of an analytical model for generation expansion planning as a tool to provide guidelines for preventing instability in the long‐term electricity market

AbstractThis paper considers the problem of deciding multiperiod investments for generation expansion planning (GEP) in restructured power systems. This problem has presented a challenge for both market managers and suppliers regarding the stability in the electricity market and minimum income for suppliers over the planning period. In this paper, an analytical model for studying the GEP problem from the viewpoint of a central management entity is presented. The aim of this method is to establish a dynamic balance between energy supply and demand by adjustment of GEP over the horizon of planning so that not only the expected profit is provided for all new generating plants but the long‐term stability in the electricity market is also improved. This analytical model can be utilized by regulatory bodies to obtain some guidelines and thereby to set their policies for improving GEP and preventing instability in the long‐term electricity market. To do so, in this study, the uncertainties of demand and supply have been modeled through two stochastic processes. Furthermore, the market price dynamics and their mutual effects on the GEP's results have been considered. Finally, this nonlinear dynamic optimization problem is solved using a modified genetic algorithm (GA). The efficiency and ability of the proposed method are examined on a test power system. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Integer programming model for Generation Expansion Planning problem under fuzzy environment

The growth of population and increasing consumption of electricity exposes countries to build additional power units. One of the most important issues in the field of power system is the generation expansion planning (GEP) problem. The objective of the GEP problem is to evaluate the least cost investment plan for addition of power generating units over a planning period subject to power demand and energy demand constraints. In this paper an optimization model with fuzzy parameters is used to express GEP problem. In proposed model both of power demand and energy demand are taken as fuzzy parameters. A Qualitative analysis of basic stability notions for the problem of concern is defined. These notations are the set of feasible parameters, the solvability set and the stability set of the first kind. A case study is provided to demonstrate the efficiency of proposed model. A comparison study between our approach and the deterministic one is made.

Application of PSO technique for GEP in restructured power systems

In this paper, a particle swarm optimization (PSO) technique is proposed for solution of the generation expansion planning (GEP) problem in a competitive electricity market. GEP is one of the important decision-making activities in electric utilities and acquires a principle role in the deregulated environment for electricity trading. It is highly constrained non-linear dynamic optimization problem that can only be fully solved by complete enumeration and is computationally impossible for a real world GEP problem. The utility has to take both independent power producers participation and environment impact (CO 2 emission) with satisfying all electrical and energy market constraints simultaneously. The proposed PSO based method has a strong ability to find most optimistic results and is robust for solution problem featuring non-linearity, non-differentiability and high-dimensionality. The proposed method is compared with a GA based technique to illustrate its robust performance considering different purchase prices for IPPs and CO 2 emission limits. Analysis reveals that the proposed approach is an inherent, effective and economical tool for the solution of the competitive GEP problem that is easy to implement and is also superior to the GA based technique.

Application of NSGA-II Algorithm to Generation Expansion Planning

This paper describes use of a multiobjective optimization method, elitist nondominated sorting genetic algorithm version II (NSGA-II), to the generation expansion planning (GEP) problem. The proposed model provides for decision maker choice from among the different trade-off solutions. Two different problem formulations are considered. In one formulation, the first objective is to minimize cost; the second objective is to minimize sum of normalized constraint violations. In the other formulation, the first objective is to minimize investment cost; the second objective is to minimize outage cost (or maximize reliability). Virtual mapping procedure is introduced to improve the performance of NSGA-II. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

NSGA-II algorithm for multi-objective generation expansion planning problem

This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II), to multi-objective generation expansion planning (GEP) problem. The GEP problem is considered as a two-objective problem. The first objective is the minimization of investment cost and the second objective is the minimization of outage cost (or maximization of reliability). To improve the performance of NSGA-II, two modifications are proposed. One modification is incorporation of Virtual Mapping Procedure (VMP), and the other is introduction of controlled elitism in NSGA-II. A synthetic test system having 5 types of candidate units is considered here for GEP for a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail.

A New Efficient GA-Benders' Decomposition Method: For Power Generation Expansion Planning With Emission Controls

The power generation expansion planning (PGEP) problem is a large-scale mixed integer nonlinear programming (MINLP) problem cited as one of the most complex optimization problems. In this paper, an application of a new efficient methodology for solving the power generation expansion planning problem is presented. A comprehensive planning production simulation model is introduced toward formulating into an MINLP model. The model evaluates the most economical investment planning for additional thermal power generating units of the optimal mix for long-term power generation expansion planning with emission controls, regarding to the incorporated environmental costs, subject to the integrated requirements of power demands, power capacities, loss of load probability (LOLP) levels, locations, and environmental limitations for emission controls. A GA-heuristic-based method called GA-Benders' decomposition (GA-BD) is proposed for solving this complex problem. Finally, an application of the proposed GA-BD method is discussed and concluded.

Application and comparison of metaheuristic techniques to generation expansion planning in the partially deregulated environment

The deregulation of the electric utility industry is an on-going process in many developing countries. A novel generation expansion planning (GEP) model is proposed that is suitable for developing countries such as India in a partially deregulated environment. In a partially deregulated/restructured environment, both utilities and independent power producers (IPPs) participate in the generation market. In this model, the utility purchases electric power from the IPPs and sells it to the consumer. The utility maximises its profit and ensures profits for all the participating IPPs. In addition, the utility checks under/over investment and considers system security, national security (fuel-mix ratio), social welfare and reliability simultaneously. The budget constraints of the utility are to be taken into consideration during the expansion plan. Metaheuristic techniques, such as genetic algorithms, differential evolution, evolutionary programming, evolutionary strategy, particle swarm optimisation, tabu search, simulated annealing, and the hybrid approach are used to solve the restructured GEP problem, and their performances are evaluated and validated against the dynamic programming (DP) method for a synthetic test system having five types of candidate plant for the utility and three types of candidate plant for IPP, with a 6 year planning horizon. The effectiveness of the proposed modifications and techniques is also addressed.

Application and Comparison of Metaheuristic Techniques to Generation Expansion Planning Incorporating Both Bilateral and Multilateral Transactions

In restructured environment, various transactions such as firm bilateral and multilateral transactions are taking place. An analysis is made on effects of transactions on Generation Expansion Planning (GEP). Some of the metaheuristic techniques such as Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Programming (EP), Evolutionary Strategy (ES), Particle Swarm Optimization (PSO), Tabu Search (TS), Simulated Annealing (SA), and Hybrid Approach (HA) are applied to solve the Transactions-GEP problem with the support of AC-Optimal Power Flow (OPF) for the modified IEEE-30 bus test system with 6-years planning horizon. The original GEP problem is modified using the proposed methods i) Virtual Mapping Procedure (VMP), and ii) Penalty Factor Approach (PFA), to improve the effectiveness of the Metaheuristic techniques. Further, Intelligent Initial Population Generation (IIPG) and 'Store and Retrieve approach' are introduced in the solution techniques to reduce the computational time. PFA is used to convert the constrained problem into an unconstrained one. The results of the metaheuristic techniques are compared and validated with that of Dynamic Programming (DP). The performances of each metaheuristic technique were compared in terms of their Success Rate (SR), Average Number of Generations (ANG), the error percentage and the mean execution time. The effects of various transactions on GEP are also analyzed.

Application and Comparison of Metaheuristic Techniques to Generation Expansion Planning Problem

This work presents both application and comparison of the metaheuristic techniques to generation expansion planning (GEP) problem. The Metaheuristic techniques such as the genetic algorithm, differential evolution, evolutionary programming, evolutionary strategy, ant colony optimization, particle swarm optimization, tabu search, simulated annealing, and hybrid approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods virtual mapping procedure (VMP) and penalty factor approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, intelligent initial population generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional dynamic programming and the effectiveness of each proposed methods has also been illustrated in detail.