Generation Expansion Planning Problem Research Articles

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.

Low-cost and flexible generation expansion planning with localization of power plants in a test bus system

ABSTRACT Generation Expansion Planning (GEP) is a challenge in electrical power systems because the size of the generating unit is large in scale, non-linear, long-term, and discontinuous. The existing GEP models use an array of methodological techniques. These models, however, primarily focus on the type of generation unit to be installed and when to be installed so as to reduce pollution and overall costs. They do not focus on the optimal location for installation. This research work proposes an IEEE bus-30 and IEEE bus-14 merged bus systems to fulfil the electrical load demand during the 5th and 10th years of planning. In stage 1, the GEP problem is resolved using Black Widow Optimization (BWO). In stage 2, the optimal location for generating units in the proposed bus system is resolved using a Chimp Optimization Algorithm (ChoA). The best location reduces the objective function (real power loss) and satisfies the voltage and power flow limits of the electrical power system. The performance of the proposed model is compared to that of existing optimization models. The results demonstrate that the proposed work reduces costs and provides flexible operations with reduced real power loss.

Reliability constrained dynamic generation expansion planning using honey badger algorithm

Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms.

Recuperation based multi-objective generation expansion planning for a real-world power system

This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions.

Multi-Objective Based Generation Expansion Planning for Utility Power System of Tamil Nadu State Considering Recuperation of Older Power Plants

Generation Expansion Planning (GEP) is a problem that comprises multiple contradictory features while planning to construct new generating units. It must be solved by considering cost, reliability and environmental emission. Hence the mathematical representations have been developed to be accurate, and to improve the understanding of the multifaceted and contradictory aspects of the GEP problem. In this study, the Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) algorithm is implemented for solving Multi-Objective GEP (MOGEP) problem. The objectives such as minimization of overall cost, decrement of the pollutant emission and enhancement of reliability have been considered by considering the constraints. The real-world MOGEP problem has been solved for seven-year (from the year 2020 to 2027) and fourteen-year (from the year 2020 to 2034) planning span for the utility power system of Tamil Nadu state, India. The problem is solved for four different cases with the consideration of retirement and recuperation of the older generating units. coal, gas, oil, nuclear, hydel, wind, Solar-photovoltaic (SPV) and biomass power plants were considered in this planning study. The results establish the competence of MOCLPSO to produce well-spread Pareto optimal non-dominated solutions of the MOGEP problem.

Application of hierarchical clustering on electricity demand of electric vehicles for GEP problems

Increasing fossil fuel consumption and consequently the effects of greenhouse gases (GHGs) on the environment and economy are a major concern for all nations and governments. Electric vehicles (EVs) with plug-in capabilities have the potential to ease such problems. However, the extracted power from the grid for charging the EVs' batteries will significantly impact daily power demand. To satisfy the increasing demand and ensure generation capacity adequacy, the generation expansion planning (GEP) problem is solved to determine the investment decisions for electricity generation sources. Even though there are no centralized utilities for generation planning in most markets, there is still a need to realistically solve the GEP problems and find the optimal investment decisions to tailor the incentives used by most governments to guide the market. There is also a need for a tool to analyze the effect of different charging power levels, charging policies, and penetration levels. The main goal of this paper is to provide a tool to determine realistic optimal investment plans and evaluate different cases. It is also very important to consider the stochastic nature of the electricity demand in GEP problems. We propose a scenario-based stochastic programming model to incorporate the variability in the electricity demand due to EV charging through a set of scenarios generated by Monte Carlo Simulation. The methodology starts with applying a simulation method to generate the electricity demand of EVs by considering all the possible factors affecting EVs' demand. Each iteration of this simulation represents a possible demand profile as a result of penetrating the EVs into the market. Using all these demand profiles in GEP is preferable, but it is not computationally efficient. Computational tractability is achieved by using the clustering technique to reduce the size of such scenarios. We propose clustering methods to select a representative set from the data sets generated by the simulation and integrate EVs into GEP problems by using the selected set. The GEP models are defined to represent EVs' demand explicitly and then solved to imply the benefit of the suggested methods. The results show that GEP models with a representative set produce more realistic solutions than the GEP models including only average EVs demand. To select representative sets, different clustering techniques and distance measurements are used and compared with respect to their performances. Two different methods are defined to choose the best number of clusters: the silhouette coefficient method and the

A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively.

Generation expansion planning using game theory approach to reduce carbon emission: A case study of Iran

Global warming and climate change caused by human activities are some of the major environmental problems that the most important cause of this phenomenon is greenhouse gases, especially CO2. Due to the many consequences of this global phenomenon in the last two decades, the attention of many scientists and policymakers in the world has been drawn to this issue. Thus, they set various regulations to reduce greenhouse gases and carbon footprints. On the other hand, one of the major causes of increasing carbon emissions is electricity generation power plants; as a result, the most important solution is to reduce CO2 gas generation in these power plants. Nowadays, generation expansion planning (GEP) is an important and inevitable issue for power system designers due to energy consumption growth. Besides, finding a solution in order to satisfy the increasing electricity demand in the future years and considering carbon reduction as a new constraint to this problem is a challenging subject. This paper modelled the GEP problem with a game theory approach by considering government regulations, including carbon tax and subsidy. Therefore, four possible strategies have been assumed to achieve the goal of carbon reduction. We have considered Iran as our case study, and the results have been analyzed and discussed. Eventually, a comprehensive sensitivity analysis is carried out based on the carbon tax and government subsidy.

Flexibility and real options analysis in power system generation expansion planning under uncertainty

Over many years, there has been a drive in the electricity industry towards better integration of environmentally friendly and renewable generation resources for power systems. Such resources show highly variable availability, impacting the design and performance of power systems. In this article, we propose using a stochastic programming approach to optimize Generation Expansion Planning (GEP), with explicit consideration of generator output capacity uncertainty. Flexibility implementation - via real options exercised in response to uncertainty realizations - is considered as an important design approach to the GEP problem. It more effectively captures upside opportunities, while reducing exposure to downside risks. A decision-rule-based approach to real options modeling is used, combining conditional-go and finite adaptability principles. The solutions provide decision makers with easy-to-use guidelines with threshold values from which to exercise the options in operations. To demonstrate application of the proposed methodologies and decision rules, a case study situated in the Midwest United States is used. The case study demonstrates how to quantify the value of flexibility, and showcases the usefulness of the proposed approach.

Energy Source Expansion Planning for a Real-world Deregulated Electric Utility Considering Emission

The Electricity Demand Prediction (EDP) and Generation Expansion Planning (GEP) make a real-world planning problem more optimal. In this study, the EDP problem has been solved using the optimization techniques such as Differential Evolution (DE), Genetic Algorithm (GA) and Artificial Immune System (AIS) for Tamil Nadu state, India. The electricity demand has been predicted and validated in terms of Mean Absolute Percentage Error (MAPE). The results reveal that DE outperformed other algorithms in solving the EDP problem. Continually, the GEP problem under partial deregulated environmental has been solved using the DE. In the GEP model, utilities and Independent Power Producer (IPP) have acted as the generating sources where IPP sells the power to utilities and utilities sell the power to the consumers. The utilities have been modeled to ensure the profit of all IPP, fuel mix ratio, system security, reliability, etc. The problem has been solved for the 6-year (till 2024) and 12-year (till 2030) planning horizons by considering three different scenarios, such as (i) without budget constraint of utilities, (ii) with budget constraint of utilities and (iii) penetration of huge Renewable Energy Source (RES) with budget constraint of utilities. The expansion capacities of each power plants were estimated over the planning horizon by maximizing the profit to the generating sources. The values of reliability indices and CO2 emission have been obtained.

Generation expansion planning with wind power plant using DE algorithm

To sustain the economic growth, infrastructure development is only of the essential aspects and also the power sector is the most important infrastructure elements. For the increment of energy demand, apart from the conventional energy sources, the wind, solar etc., of the necessary sources to produce the electrical energy to meet out the demand. The conventional sources of energy associated with many problems like, prices increased for petroleum products, environmental implications, safety from radiations, global warming etc. To overcome the difficulties in association with conventional energy sources, most of the countries including India have shifted the attention for improving renewable energy sources. In practical, the Generation Expansion Planning (GEP) problem is a large scale and complex. Also the nonlinear optimization problem of mixed integer nature where the number of candidate solutions to be evaluated increases exponentially with system size. By the effective planning of power systems can be achieved by the accurate solution of the GEP problem. The aim of this paper is to apply the Differential Evolution (DE) algorithm to the GEP problem and find out the least cost expansion plan. It is applied for a test system of six and fourteen years planning horizons. As the wind energy composition in the system is continuously growing, the impact of the same has been studied. The different cost component with variations and also the reliability indices are studied.

A Methodology for Explicit Representation Of The Stochastic Demand Due To Electric Vehicles in Generation Expansion Planning Problems

Generation expansion planning (GEP) problems are solved to find the optimum investment decisions to satisfy the increasing electricity demand. Integration of electric vehicles (EVs) with the capability of charging from the grid will also increase the electricity demand of the grid. Depending on the charging/driving characteristics of users, demand curves for EVs will be shaped and it will be different on each day. Therefore, it is very crucial to represent this stochastic nature of EVs demand in the associated GEP problems. This paper is proposing a methodology to represent EVs demand realistically on GEP models. The proposed methodology starts with generating random demand patterns to demonstrate possibilities for the EVs demand patterns via Monte Carlo Simulation, then using an optimization-based model to select a representative set. Two stage stochastic programming model is proposed for GEP problems and solved to minimize the expected cost over the entire set, the representative set and the average EVs demand. The results show that GEP models with selected demand curves produce more realistic decisions (closer to the solutions obtained by using the entire demand patterns) than the decisions obtained by the models with average EVs demand. In most cases, the models using average EVs demand fail to capture the new peaks generated by EVs, therefore, they suggest less capacity expansion then the required amount. This results in more unmet demand in the system.

A Novel Optimization Framework for the Least Cost Generation Expansion Planning in the Presence of Renewable Energy Sources considering Regional Connectivity

Generation expansion planning (GEP) is a vital step in power system planning after the load forecast. The integration of renewable energy sources makes the GEP problem less reliable due to intermittent nature. The concept of exploiting the energy sources of multiple regions and cross-border power exchange is gaining immense attention in energy policy strategic planning. The aspect of regional connectivity has been incorporated in the least cost GEP by proposing two new models for large-scale power systems as intra-regional and inter-regional GEP. Intra-regional GEP simulates the composite planning for multiple zones of a region, whereas inter-regional GEP accommodates the energy sources of multiple regions and promotes the import–export of electrical power across the border. To pursue the least cost GEP, a novel meta-heuristic GEP optimization framework has been proposed in this paper. The proposed GEP optimization framework is biogeography-based optimization employing the correction matrix method-with-indicators (BBO-CMMI). In BBO-CMMI, a new parallel constraint handling approach called the correction matrix method-with-indicators (CMMI) has been developed. The proposed optimization framework is applied to reliability-constrained and emission-constrained GEP problems from the literature. The proposed framework shows promising results in terms of the least cost and runtime as compared with the results given by recent approaches presented in the literature. Similarly, the framework outperforms to optimize the large-scale power systems for intra-regional and inter-regional GEP. The applicability of the proposed approach has also been evaluated by applying to a real case study of Pakistan’s power system to devise the feasible generation expansion plan .

Generation Expansion Planning Considering the Rehabilitation of Aging Generating Units

Aging generating units can gradually lose their efficiency, reliability, and economic viability, and violate certified levels of carbon emission in power system operations. Retirement is traditionally considered a viable option for managing aging units within the generation expansion planning (GEP) studies; while, the rehabilitation of such units can also be economically beneficial to power system and even outperform other options considering certain planning and operation criteria. Rehabilitation procedure depends on many physical factors including generating unit types and locations, as well as financial, regulatory, and transmission network constraints. This paper presents an optimization model for GEP considering retirement and rehabilitation options for generating unit planning and operation. The proposed GEP problem uses the Conditional Value-at-Risk (CVaR) as a risk index for considering rehabilitation cost and load forecast uncertainties. In order to evaluate performance of the proposed method, the IEEE 24-bus and 118-bus systems are examined in various operating conditions and the results are analyzed.

A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach

This study presents a complementary model for Generation Expansion Planning (GEP). A GEP problem commonly determines optimal investment decisions in new power generation plants by minimizing total cost over a mid towards long planning horizon subjected by a set of constraints. The model aims to capture operational challenges arising when a transition towards higher shares of intermittent renewable generation is considered. It embeds a computationally expensive Operational Cost Simulation Model (OCSM), which may exhibit a high level of temporal and technical representation of the short-term operation of a power system to model the unit commitment. The emerging computationally expensive integer non-linear programming constrained optimization model is solved by a problem-customized Meta-model Assisted Evolutionary Algorithm (MAEA). The MAEA employs, off-line trained and on-line refined, approximation models to estimate the output of an OCSM to attain a near-optimal solution by utilizing a limited number of computationally expensive OCSM simulations. The approach is applied on an illustrative test case for a 15 year planning period considering the short-term operation of thermal, hydroelectric and storage units and generation from renewable energy sources. Moreover, the impact of technical resolution is examined through a simple comparative study. The results reveal the efficiency of the proposed problem-customized MAEA. Moreover, the trained approximation models exhibit a low relative error indicating that they may adequately approximate the true output of the OCSM. It is demonstrated that neglecting technical limitations of thermal units may underestimate the utilization of flexible units, i.e. thermal and non-thermal units, affecting the attained investment decisions.

Least cost generation expansion planning in the presence of renewable energy sources using correction matrix method with indicators-based discrete water cycle algorithm

Generation expansion planning (GEP) is a vital step in power system planning after load forecast. It is a highly constrained, dynamic, combinatorial, and discrete optimization problem. Mathematically, it is modeled as a mixed-integer nonlinear programming problem with high dimensionality and stochastic characteristics. The integration of renewable energy sources makes the GEP problem a complicated task and less reliable due to its intermittent nature. Meta-heuristic approaches are considered as potential solution methodologies to optimize the least cost GEP problem. This paper presents a novel GEP optimization framework to pursue the least cost GEP achieving a certain reliability level according to the forecasted demand for a planning horizon. The proposed GEP optimization framework is a correction matrix method with an indicator-based discrete water cycle algorithm (DWCA-CMMI). In DWCA-CMMI, a new parallel constraint handling approach, called a correction matrix method with indicators (CMMI), has been developed. DWCA-CMMI requires a smaller number of iterations and search agents to minimize the total GEP cost as compared to penalty factor-based metaheuristic approaches. Hence, CMMI enhances the convergence speed of the algorithm, avoids trapping in local optima, and improves both exploration and particularly exploitation. The proposed optimization framework is applied to reliability constrained and emission constrained GEP problems (test systems) from the literature. The proposed framework shows the promising results in terms of least cost and runtime as compared to results given by recent approaches presented in the literature. The applicability of the proposed approach has also been evaluated by applying to a real case study of Pakistan's power system to devise the feasible generation expansion plan.

An efficient hybrid CS‐DP application for generation expansion planning problem

This article proposes a new hybrid approach for generation expansion planning (GEP) based on cuckoo search (CS) and dynamic programming (DP). Generally, the GEP problem is known as having very large search space and highly nonlinear complex combinatorial optimization. The global optimal solution is to solve with the full enumeration DP. However, the computation time grows exponentially when the planning horizon and candidate options increase, which is called ‘curse of dimensionality’. To solve this problem, a hybrid CS‐DP approach is proposed with three techniques for solution quality and convergence characteristic enhancement. First, the CS is integrated into the DP structure for the optimum solution of the very large search space problem. Second, the feasible search concept can eliminate infeasible solutions from the searching process. Finally, the memory exploration search is applied to prevent repeated searches. To measure its effectiveness, the proposed method is applied to 15 existing power plants, with 5 candidate options and 2 test cases: 14 year and 20 year study periods. The experimental results are compared with classical and metaheuristic optimizations. The test results indicate that the proposed hybrid CS‐DP achieves higher solution quality and superior convergence characteristics than other methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The Impact of Capital Subsidy Incentive on Renewable Energy Deployment in Long-Term Power Generation Expansion Planning

Capital investment cost is the major obstacle to the increasing share of electricity from renewable energy sources (RES-E). Therefore, RES-E incentive mechanisms are incorporated into markets to compensate cost-related barriers and to increase RES-E deployment rate. In this study, the impact of direct capital investment subsidy on RES-E in generation expansion planning (GEP) has been analyzed and deployment rates of renewable power plants have been defined. The effect of current subsidy mechanisms on the installed power capacity of various sources has also been analyzed and policy recommendations have been put forth in the light of the characteristics of Turkey’s current subsidization mechanism and its outcomes. Genetic algorithm was applied to solve the GEP problem. The share of non-hydro renewable power plants for future additions in overall installed power was determined as 9.45% without the proposed incentive, while it was estimated to rise to 13.65% when it was promoted by direct capital investment subsidy of 50%. The deployment rates of renewable power plants are expected to grow as the imported coal share in total installed power is expected to decline after applying the proposed subsidy.

A Linear Model for Dynamic Generation Expansion Planning Considering Loss of Load Probability

Computation of Loss of Load Probability (LOLP) is a challenge in Generation Expansion Planning (GEP) problem. This paper presents a dynamic GEP model considering LOLP as a reliability criterion. The objective function of the proposed dynamic GEP model includes the discounted total costs of investment cost, operation cost, and maintenance cost. The proposed LOLP constrained GEP problem is formulated as a mixed integer nonlinear programming (MINLP) problem. The MINLP formulation of GEP problem is then converted to a mixed integer linear programming (MIP) problem by applying several approximations to LOLP constraint. The utilized approximations are divided into two clusters for low and high order outages. A test case containing 12 types of installed and 5 types of candidate plants with a 14-year planning horizon is used to validate the efficiency of the proposed dynamic GEP problem. The developed MIP-based GEP model is solved using CPLEX solver and the obtained results are compared with the conventional LOLP-constrained GEP model.

Advances in clean and low-carbon power generation planning

Increasing global energy consumption and consequent greenhouse gas emissions pose great challenges to the sustainable development of international human society. Electricity constitutes the largest part of energy carriers, and the power sector is identified as the key sector with great carbon dioxide mitigation potential. Therefore, power generation expansion planning (GEP) problem has drawn great attention due to its important role in global energy supply, renewable energy utilization and carbon dioxide mitigation. Several important issues, including renewable energy sources integration, operating reserve, deregulated power market, demand response and carbon pricing mechanism should be incorporated in a GEP problem. Energy system engineering provides a methodological framework to address the complex energy, economic and environmental problems by adopting an integrated systematic approach, featuring superstructure-based modeling, mixed-integer programming, multi-objective optimization, and optimization under uncertainty. Recent advances of these approaches in GEP problems related to the five issues mentioned above are reviewed and discussed in this article.