Generation Expansion Planning Model 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.

Developing a new flexibility-oriented model for generation expansion planning studies of renewable-based energy systems

This paper presents a flexibility-oriented generation expansion planning (GEP) model with the goal of increasing renewable energy penetration. The model incorporates GEP and unit commitment while considering improved reserve requirements. Utilizing GEP and unit commitment results in considering short-term operational constraints. Furthermore, given the increase in renewable portfolio standards (RPS) and the need for accurate reserve requirements determination, it is essential to consider improved reserve requirements in the proposed model. Moreover, this model utilizes a metric that measures power system flexibility deficiency and leads to flexible power system expansion by integrating it into the objective function. The flexibility-oriented GEP model is developed using mixed-integer linear programming and applied to a scaled-sized case study representing the national power system of Iran with a twenty-year planning horizon. The results show that neglecting improved reserve requirements leads to plans short on flexibility, and considering these requirements substantially declines the upward flexibility violation costs by 68% under the base scenario and 87% under increasing non-hydro RPS scenario. Furthermore, using the flexibility metric coupled with improved reserve requirements changes the optimal path for power system expansion and reduces the risk of flexibility deficiency. Also, increasing RPS by 35% would strongly depend on developing fast-response dispatchable generation units in the years before renewable energy resources expansion; however it doesn’t impose a significant extra cost of planning and the total cost will increase just by 9.43%.

Water and emission constrained generation expansion planning for Iran power system

This paper analyzes the impact of water and emission limitations on expansion planning of thermal power plants (TPPs), in Iran. TPPs need considerable amount of water for their cooling system. In Iran, water crisis of recent years has made it impossible to operate existing steam power plants in some areas, during summer season. This paper considers water availability and cost in generation expansion planning (GEP) of Iran. There are different types of water resources such as rivers, dams, treated wastewater, seawater, seawater transmission line, saline groundwater, and groundwater. Considering available capacity of water resources in each area and sorting their extraction cost in ascending order, some water consumption cost curves are derived. For this purpose, data of about 200 water resources located in different regions of Iran are analyzed. Provided GEP model includes a 15-years planning horizon and analyzes several scenarios of water availability. Although, in low water price scenarios, air temperature and height above sea level have more impact on placement of TPPs, it is different for high water price scenarios. Also, the effect of emission-related policies on GEP decisions is analyzed by considering various fuel types of TPPs, penalties for emissions, and global restrictions on greenhouse gas production.

Generation expansion planning with a renewable energy target and interconnection option: A case study of the Sulawesi region, Indonesia

Sulawesi is a strategic economic region in eastern Indonesia that is facing high growth in electricity demand. Two challenges need to be addressed to achieve its renewable energy (RE) target. The region comprises two unconnected electricity systems, and its RE resources are scattered geographically. It is therefore harnessing RE resources and interconnecting these two electricity systems can be an optimal scenario for generation expansion planning (GEP). This study aims to develop an open-source GEP model for the Sulawesi region as well as identify the optimal electricity development pathway while considering not only the need to achieve higher penetration of renewables and reduce CO2 emissions but also the option and associated benefits and costs of interconnecting the two distinct electricity systems. The results show that the optimal RE development pathway is the interconnection scenario, which ensures a lower generation cost with reduced emissions. This scenario reduces the cost by USD 258.8 million compared with the total cost of the isolated scenario and ensures the RE target of 31% by 2050. This study recommends that investment in hydro and wind energy should be simultaneously encouraged, and robust policies are required to support investments in RE sources, such as geothermal, biomass, and solar energy.

Optimal sizing of energy storage in generation expansion planning of new power system with high penetration of renewable energies

With the consumption of fossil fuels and the impact of the greenhouse effect, renewable energies are ushering in a huge development opportunity, thus the optimal configuration of energy storage is essential to cope with the intermittence and fluctuation of renewable energies. This paper establishes a mathematical model for optimal sizing of energy storage in generation expansion planning (GEP) of new power system with high penetration of renewable energies. The objective function is to achieve the lowest total cost of investment and operation under the comprehensive consideration of various generation technologies and energy storage technologies. Carbon peaking and carbon neutrality goals are transformed into constraints of the GEP model to achieve high penetration of renewable energies in a power system. And 8760h operation curve are adopted to deal with the intermittence and fluctuation of renewable energies and obtains a more reasonable and realistic GEP results. Meanwhile, the optimal sizing of energy storage is solved in GEP model by detailed operation optimization and constraints of penetration rate and curtailment rate of renewable energies. Finally, the solving flow chart of GEP model and flow chart of optimal sizing of energy storage are given and the validity of this GEP model is proved in case analysis. In addition, carbon peaking goal may achieve in advance by vigorously developing wind power and PV power and configuring appropriate energy storage.

Spatial representation of renewable technologies in generation expansion planning models

The increasing share of intermittent Renewable Energy Sources (iRES) in the energy mix leads to a growing need for their accurate representation in energy system models. In this paper, we analyze how the results of generation planning models are impacted by the spatial representation used to model intermittent renewable technologies. It is shown that the spatial resolution, and to a lesser extent the spatial aggregation method used in a planning model, have a significant influence on the obtained system cost. The effect is quantified in a case study of European geographical scale, by varying the spatial resolution from highly resolved (hundreds of sites) to highly aggregated (1 site). A resolution-dependency of the system cost is observed under multiple renewable generation targets, and different renewable capacity restrictions. In doing so, we highlight the importance of implementing an appropriate capacity limit, especially when a high spatial resolution is used. Additionally, basic spatial aggregation methods -to process high resolution input data- are compared.

Impact of Electricity Price Expectation in the Planning Period on the Evolution of Generation Expansion Planning in the Market Environment

With the continuous promotion of China’s electricity market reform, the introduction of competition in the power generation market provides a new research direction for the generation expansion planning (GEP) problem, which is of great significance in the promotion of the optimization of the power energy structure. In the context of marketization, the electricity price expectation during the planning period is a key factor of GEP for independent power generation groups. There is some literature showing that the electricity price expectation in the planning period can be estimated according to certain laws of market supply and demand, while it seems to us that a future Pay as Bid (PAB) mechanism is better to determine the electricity price expectation. In this paper, to explore the impact of these two different electricity price formation mechanisms on the evolution of the generation market, a multi-agent framework is first established to describe the interaction process among the generation market agents; then, a GEP model for independent power generation groups is developed in the market competition environment, and four representative scenarios are finally designed for detailed comparative studies. Based on these case studies, the conclusion can be summarized as: (1) the PAB bidding mechanism has a lower electricity price and higher market installed capacity almost all the time during the whole planning period for all four scenarios; (2) it is more important that PAB can reduce the impact of parameter uncertainty in the laws of market supply and demand, which can obtain more reliable and reasonable results regarding the long-term evolution of the generation market.

A Review on the Internalization of Externalities in Electricity Generation Expansion Planning

This work addresses the internalization of externalities in energy decision making and in generation expansion planning (GEP). Although the linkage between externalities and energy is well recognized, the issue of the internalization in GEP models and from a sustainability perspective is still far from being fully explored. A critical literature review is presented, including scientific articles published in the period from 2011 to 2021 and selected from scientific databases according to a set of pre-defined keywords. The literature is vast and quite heterogeneous in the models and methods used to deal with these externalities, and therefore a categorization of these studies was attempted. This categorization was based on the methods used, the geographical scope, the externalities included in the planning model and the strategies for their inclusion. As a result, it was possible to perceive that most studies tend to focus on the internalization of externalities related to CO2 and equivalent emissions from a national perspective. Departing from the critical analysis, pathways for future research were presented, highlighting the need to improve the internalization of social externalities to overcome environmental and economic bias, and also highlighting the importance of recognizing regional specificities and development priorities.

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

Scenario Partitioning Methods for Two-Stage Stochastic Generation Expansion Under Multi-Scale Uncertainty

Generation Expansion Planning (GEP) can inform regulation, electricity market design, and regional system planning by identifying adaptive investment strategies. Relevant uncertainties include hourly variability in load and renewable generation and decadal-scale uncertainty in technology, markets, and regulation. A multi-stage and multi-scale stochastic GEP model that represents these uncertainties at sufficient resolution becomes intractable. We present an approach for representing this multi-scale uncertainty, and compare it to existing methods, applied to a two-stage stochastic GEP model with a cumulative carbon emission target. For long-term uncertainty, we compare partitioning methods, which reduce the number of decision variables but retain all scenarios, to representative scenario methods, which retain only a subset of the original scenarios. For short-term uncertainty, we compare methods that select representative weeks based on distance metrics in the parameter space to methods that use the covariance of outcomes across feasible decisions to select weeks. We find that scenario reduction methods struggle to find the appropriate investment levels for variable renewable generation and consequently produce more costly plans than scenario partitioning methods. While simple approximating methods perform well with larger models, covariance-based approximations have the best performance overall.

Assessing Potential Scenarios for Achieving New and Renewable Energy Targets in Java-Bali Power System, Indonesia

Geographic circumstances, government policies, and power system characteristics face many countries struggling to achieve their new and renewable energy (NRE). In addition, one characteristic of renewable energy (RE) which cannot be moved is a severe problem for archipelagic countries like Indonesia in achieving their NRE targets. Therefore, this research creates a long-term open-source generation expansion planning (GEP) model that considers renewable energy integration between islands, government policies, and power system characteristics of Indonesia. The model proposes a high voltage direct current (HVDC) line to facilitate abundant energy transfer between islands. The research also included multiple scenario analyses based on the potential strategies that could realistically be applied. Based on the long-term GEP model results, possible alternative routes to achieving NRE targets are mapped and assessed by considering power system characteristics and national energy policies. Specifically, the Java-Bali system of Indonesia is employed as a case study to demonstrate the performance of the proposed long-term GEP model. The optimum planning to achieve the targets produces the generation cost of 7.05 cents USD/kWh and the CO2 emission reduction of 2,297 million tons of CO2.

A Linearized AC Planning Model for Generations and SFCLs Incorporating Transient Stability and Short-Circuit Constraints

Generation expansion planning (GEP) can be a challenging problem when short-circuit (SC) levels and transient stability constraints are considered. We propose a multi-period GEP model in which resistive superconducting fault current limiters (SFCLs) are deployed to limit SC levels, which may be elevated by new generators, and to enhance transient stability at the same time. Through investigating the effect of SFCLs on transient stability and SC levels, efficient linear regions of SFCL deployment are identified and employed to achieve a more cost-effective solution and enhance problem tractability. An effective solution method is also presented by decomposing the main problem into smaller ones. We also propose a linearized AC network framework incorporating bilinear terms based on McCormick envelopes. Relaxation errors are minimized by an exactness loop until the linearized model solution sufficiently matches the original nonlinear model solution. The methodology is illustrated and discussed using the IEEE 118-bus test system.

Incorporating External Flexibility in Generation Expansion Planning

Exploiting flexibilities (e.g., demand response, energy storage, and deep peak regulation.) in generation expansion planning (GEP) is significant for coping with the increase of renewable energy (RE). This letter presents a GEP model with novel consideration of the flexibility of the interconnected external network without data leakage. External flexibility (detailed information) is represented as a feasible region with generation cost functions related to tie-line power, by using multi-parametric programming. Numerical results demonstrate the effectiveness of the proposed model.

Modeling investment decisions from heterogeneous firms under imperfect information and risk in wholesale electricity markets

Investment decisions in the electricity sector are complex and depend on wholesale market and policy structures, attributes of investor firms that impact risk and financing, and the location-specific economics of investment options. This paper introduces the Electricity Markets and Investment Suite - Agent-Based Simulation (EMIS-AS), which models the evolution of the electricity generation mix under various market structures while explicitly capturing the aforementioned investment factors and imperfect information. EMIS-AS advances the state-of-the-art of generation expansion planning and agent-based modeling by incorporating various aspects of investor heterogeneity (e.g., differences in financial characteristics, technology preferences, and attitudes towards risk under uncertainty), a robust price prediction methodology, a methodology for updating investors’ forecast parameters using Kalman Filters, and endogenous representation of a customizable set of wholesale electricity markets including energy, ancillary services, capacity, and renewable energy certificate markets. Implementation of EMIS-AS on a test system highlights the strong role that firms’ heterogeneous attributes have on the investment decisions, generation portfolio, and resulting resource adequacy. In multiple instances, investment and retirement results diverge not only due to each firm’s own parameters, but also due to the actions and characteristics of other firms. Results also demonstrate how imperfect information and risk preferences can lead to suboptimal investment outcomes, which can require firm-level recourse actions with severe profitability implications. In addition, a comparison with a traditional generation expansion planning model highlights the ability of EMIS-AS to capture resource scarcity and early retirements caused by real-world imperfections that traditional models cannot represent.

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.

Long term storage in generation expansion planning models with a reduced temporal scope

To reduce the computation time of Energy System Optimization Models and Generation Expansion Planning Models operational detail is typically limited to several hours, days, or weeks in a year selected using Time Series Aggregation methods. We compare time series aggregation methods and generation expansion planning models which aim to capture the value of long-term storage for the first time in the literature. Time Series Aggregations methods were compared by varying the number of representative periods and then running a full year generation expansion planning model on novel synthetic time series. Generation Expansion Planning Models were run on selections and ordering of representative periods in order to compare them. Our results suggest that approximating the full-year time series does not necessarily translate to approximating the full-year generation expansion planning solution and that selecting hours or days is a greater determinant of performance than the time series aggregation method itself. Two of the generation expansion planning models considered, Enhanced Representative Days and Chronological Time Period Clustering, could capture the value of long-term storage, though over or underinvestment in long-term storage by more than a factor of 2 was also possible and the latter formulation exhibited a clear bias towards long-term storage. Based on these results we formulate recommendations for modelers seeking to include long-term storage in generation expansion planning models.

Generation expansion planning considering efficient linear EENS formulation

Power system equipment outages are one of the most important factors affecting the reliability and economy of power systems. It is crucial to consider the reliability of the planning problems. In this paper, a generation expansion planning (GEP) model is proposed, in which the candidate generating units and energy storage systems (ESSs) are simultaneously planned by minimizing the cost incurred on investment, operation, reserve, and reliability. The reliability cost is computed by multiplying the value of lost load (VOLL) with the expected energy not supplied (EENS), and this model makes a compromise between economy and reliability. Because the computation of EENS makes the major computation impediment of the entire model, a new efficient linear EENS formulation is proposed and applied in a multi-step GEP model. By doing so, the computation efficiency is significantly improved, and the solution accuracy is still desirable. The proposed GEP model is illustrated using the IEEE-RTS system to validate the effectiveness and superiority of the new model.

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.

Closing the void in energy planning modeling: Integrating local realities

Closing the void in energy planning modeling: Integrating local realities