Power Generation Expansion Planning Research Articles

Power generation expansion planning approach considering carbon emission constraints

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality. The power sector accounts for approximately 40% of China’s total CO2 emissions. Accordingly, collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic, environmental, and technological concerns as carbon emissions is necessary. This paper proposes a collaborative mixed- integer linear programming optimization approach for GEP. This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies, flexible generation, and resource limitations constraints. This research further analyzes the advantages and disadvantages of current GEP techniques. Results show that the main determinants of new investment decisions are carbon emissions, reserve margins, resource availability, fuel consumption, and fuel price. The proposed optimization method is simulated and validated based on China’s power system data. Finally, this study provides policy recommendations on the flexible management of traditional power sources, the market-oriented mechanism of new energy sources, and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

Generation expansion planning for Guizhou province based on the complementary characteristics of wind and solar

With the growth of wind power and solar power, power source clean transformation has become a feasible way for achieving the carbon peak target. As a hydropower resource-abundant province, Guizhou has already built Wujiang river cascade hydropower plants. This paper introduces the basic information of the Guizhou provincial power system and geometrical renewable resource distribution characteristics, especially the power source structure. Then, the wind and solar power complementary characteristics are analyzed by different copula conjunction functions. Then, the power structure transformation and generation expansion plan are provided with both carbon peak restriction and curtailment restrictions. At last, the future Guizhou provincial power system in 2029 operation simulation cases are provided, which can reduce the thermal power generation ratio to 42% and increase wind and solar power generation ratio to 8.5% and 13% approximately. The conclusion indicates that coal-fired power plant flexible reform and larger capacity energy storage installation effectively solve the larger power fluctuation and peak regulation demand with more renewable energy integration target.

A coupled non-deterministic optimization and mixed-level factorial analysis model for power generation expansion planning – A case study of Jing-Jin-Ji metropolitan region, China

In this study, a coupled non-deterministic optimization and mixed-level factorial analysis model (NOMFA) is proposed for supporting power generation expansion planning. By integrating interval-parameter programming, multistage stochastic programming and mixed-level factorial analysis within a general system optimization framework, this model can not only help determine the optimized schemes for power generation and capacity expansion under various uncertainties, but also reflect dynamic variations of system conditions; moreover, it can take into account the effects of external interferences and their interactions on the system outputs in the decision-making process. A case study of Jing-Jin-Ji (JJJ) metropolitan region is provided to demonstrate the effectiveness of the proposed approach. The results indicate that although renewable technologies are becoming increasingly significant, electricity generated by fossil fuels would still dominateJJJ’s power systems during the planning horizon. It is also found that the import price of electricity is the most influential factor on both total system cost and total CO2 emissions; meanwhile, the interaction between the import price of electricity and the price of gas impacts CO2 emissions to a certain extent. It is expected that the modeling results will provide solid bases for formulating power generation expansion plans for JJJ Metropolitan Region.

The power generation expansion planning in Brazil: Considering the impact of greenhouse gas emissions in an Investment Decision Model

The Generation Expansion Planning (GEP) process in Brazil involves computational models with a range of variables and constraints inherent in a mostly hydrothermal system. Such models generally use technical and economic parameters to represent the system, but with the growing concern about environmental and climatic issues, the need has arisen for environmental parameter analysis to promote a better representation of a more comprehensive optimum. In view of the recent Paris Agreement, through which the Brazilian government committed itself to significantly reduce its greenhouse gas emissions (GHG) by 2030, it has become essential that the GEP take into account the emission of gases. In this work, scenarios that penalize or limit the emission of greenhouse gases are created and compared inside the existent Investment Decision Model (MDI) used in Brazil. The main objective is to verify how the objective of reducing emissions would change the optimum expansion planning originally provided by the investment model. The results show that within the created scenarios, the largest reduction is achieved when considering a monetary penalty of US$ 25.00/tCO2eq in the burning of fossil fuels for power generation. In this case, to reduce the emissions, the investment model chooses a larger expansion of wind and biomass sources with a corresponding raise in the cost of approximately 5.3%, allowing a total reduction of about 39 MtCO2eq in relation to the reference scenario at the end of the planning horizon.

Distributed Power Generation Scheduling, Modeling, and Expansion Planning

This volume contains the successful invited submissions [...]

Baseline Model for Electricity Generation Expansion Planning for The Philippines: A Leap Model Application

Philippines is one of the fastest-growing economies and most populated countries globally, greatly affecting its energy demand. The increase in demand leads to an increase in the electricity generation sector's capacity to maintain the supply-demand balance. Thus, this may lead to power shortage, high generation costs, and increased greenhouse gas emissions without proper energy planning. This study aims to create a Long-Range Energy Alternatives Planning System (LEAP)-based Baseline Model for the Philippines' generation expansion planning. The model will serve as a tool for the government and energy planners to develop a long-term generation expansion planning model. The model simulates the technological, economic and environmental aspect of the existing Philippine power generation system that can be used as a baseline for developing a Business-As-Usual Scenario and Alternative Scenario models to assess different energy policy pathways. The developed LEAP model following an independent multi-regional and national scale structure that is based on the Philippine grid and off-grid based regions showed an accurate result with the actual data. The model was able to replicate the actual power generation through the set-up of basic parameters. Development of Philippine LEAP structure; and the establishment of the demand, transformation, and resources module’s structure are based on the set of data input parameters. The baseline model developed can be used for continuing studies such as impact evaluation of energy policy for long term power generation expansion planning.

The impact of development priorities on power system expansion planning in sub-Saharan Africa

Sub-Saharan Africa faces unique barriers to electricity development due to the large proportion of the population that is un-electrified and the prevalence of rural populations. Typically, power system expansion planning models assume all potential consumers can be immediately electrified. This assumption is unrealistic in sub-Saharan Africa, where electrification will likely be a gradual process over a number of years. Furthermore, since a large proportion of the population in sub-Saharan Africa is located in rural regions, the prioritization of these regions may impact how the grid develops. In this research, we develop a multi-period optimization model for power generation and transmission system expansion planning in sub-Saharan Africa. In contrast to existing models, which assume full electrification, we consider a variety of electrification policies and analyze the impact of varying the electrification rate and policy on the cost and resources selected for power system expansion. We test our model on a case study of Rwanda. We find that varying the year in which full electrification is reached has a larger impact on cost and generation capacity than varying the electrification policy does, although, when urban and rural regions are considered equitably, more rooftop solar is built. Varying the electrification policies has a larger impact on transmission expansion than on generation expansion and this impact is amplified when starting from zero initial system capacity rather than the original Rwanda system. Additionally, a sensitivity analysis shows that tightening the bounds on CO2eq emissions has a large impact on the generation portfolio and cost.

Electrical power crisis solution by the developing renewable energy based power generation expansion

This paper proposes a different hybrid system that considers the intelligible and complex to understand the problem of regulating the expansion of conventional power plants. The current reality is determined on a long-term power generation expansion plan for Bangladesh from 2016 to 2030. The discontent of the framework and weaknesses in the power systems are facing the main problem by the energy crisis. Thus, long-term renewable energy based power generation model and the use of inhabited coal are inspected in the extent of the costs of natural expansion and green impact. The results demonstrate that in 2030 the power generation needed to meet future demand is assessed at 160TWh. The proposed structure includes two steps. In the first step, the assessment of the importance of technology is carried out using a hybrid methodology and benefits from a system of excellent descriptions, starting points, and risk assessments in the second step that will make a multi-objective show to improve our generation’s expansion plans. Two objective capacities are considered in this model. That is, adding weight to the overall placement trend and reducing overall costs. The implementation of renewable power is a great decision to describe the lake of power in Bangladesh because it requires minimal effort and less danger. This activity is essential for developing sustainable power generation in Bangladesh. The numerical model is solved using the NSGA-II algorithm, and the effect analysis is carried out on the key parameters. The results show an expanded offer of constant renewable sources in power generation for ensuring sufficient power supply in Bangladesh.

Power generation expansion plan and sustainability in a developing country: A multi-criteria decision analysis

Bangladesh has developed a power generation expansion plan up to 2041. However, the plan did not fully consider the importance of sustainable development. This paper addresses this limitation and assesses both historical and future sustainability trends of Bangladesh's power generation system, as well as the sustainability of the Power System Master Plan (PSMP-2016) through multi-criteria decision analysis (MCDA) with respect to social, environmental, and economic dimensions of the sustainability framework. Out of a total of 19 sustainability indicators, there were 10 social, 4 environmental, and 5 economic indicators taken into account. It was found that Bangladesh's future power generation sector's sustainability is going to deteriorate from its present situation. Results of the analysis show that the sustainability index of 0.3769 in 2016 is going to degrade to 0.3249 in 2041. Most importantly, economic indicators dominate the sustainability measures, compared with social and environmental indicators. Thus, improvements in the economic indicators will result in a substantial increase in the sustainability index of the country's power generation sector. This indicates that energy policies need to be designed in such a way that a balance is achieved between all three dimensions to ensure a sustainable future electricity generation system. A similar analysis can also be applied to other countries where future power generation expansion planning is underway.

Effect of Climate Change on wind speed and its impact on optimal power system expansion planning: The case of Chile

This work assesses the changes in power capacity expansion decisions regarding power generation and transmission that occur when the effects of Climate Change on wind speed are captured in the decision model. Considering an 85-year period (2016–2101), we use a Mixed-Integer Linear Program (MILP) model to analyze the optimal power capacity expansion in diverse types of power generation technologies, throughout the years and geographical locations. The optimization model minimizes the total (investment and operational) costs of the power system subject to several technical and economic constraints. We implement our model using the main Chilean power system. We compare two scenarios: one assuming that Climate Change affects wind speeds and hence wind farm capacity factors and the other assuming it does not. Our results reveal that, when taking into account the impact of Climate Change on wind speed, the optimal power generation and transmission expansion plan is different than when ignoring this effect. The variation of wind speed affects not only wind power capacity installed, but also other-technology power capacity installed. In particular, power capacity installed in wind and solar generation plants is higher (measured in MW installed) than the power capacity installed when we ignore the effects of Climate Change; and power capacity installed in diesel and natural gas technologies are lower. We perform sensitivity analyses, changing power capacity expansion limits and the discount rate, to check for the robustness of our results.

A multistage decision-dependent stochastic bilevel programming approach for power generation investment expansion planning

ABSTRACTIn this article, we study the long-term power generation investment expansion planning problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problems. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maximize the expected profit for a profit-oriented power generation investor. To address the future uncertainties in the decision-making process, this article employs a decision-dependent stochastic programming approach. In the scenario tree, we calculate the non-stationary transition probabilities based on discrete choice theory and the economies of scale theory in electricity systems. The model is further reformulated as a single-level optimization problem and solved by decomposition algorithms. The investment decisions, computation times, and optimality of the decision-dependent model are evaluated by case studies on IEEE reliability test systems. The results show that the proposed decision-dependent model provides effective investment plans for long-term power generation expansion planning.

Partially Adaptive Stochastic Optimization for Electric Power Generation Expansion Planning

Electric power generation expansion planning (GEP) is the problem of determining an optimal construction and generation plan of both new and existing electric power plants to meet future electricit...

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.

Techno-economic and environmental analysis of power generation expansion plan of Ghana

This paper examines the current electrical generation expansion plan of Ghana and compares it with proposed expansion pathways with higher penetration of Renewable Energy Technologies. An adaptation of Schwartz's Scenario Methodology was used to develop the scenarios which were then analysed using the Long-range Alternatives Planning (LEAP) model. Each of the scenarios represents policy options for generation expansion in Ghana up to 2040. Energy, economic and environmental analysis of the three alternative scenarios compared to the base scenarios was undertaken. Sensitivity results show that, if the country were to follow the generation expansion path described in the renewable energy scenarios, it could reap economic benefits of 0.5–13.23% depending on the developments in fuel prices and renewable technology capital cost. The analysis further quantifies benefits to be derived from a reduction in Greenhouse gases of the scenarios. Policy implications for the generation system of Ghana based on the results are also discussed.

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets’ planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

Stochastic optimization for electric power generation expansion planning with discrete climate change scenarios

This research is dedicated to the study of electric power system generation expansion planning considering uncertainty of climate change. Policy makers are increasingly concerned about the effects of climate change and its impact on human systems when making decisions. Electric power generation expansion planning (GEP) problems that determine the optimal expansion capacity and technology under particular technical constraints, given cost and policy assumptions, are undoubtedly among those decisions. The best approach to comprehensively model climate change uncertainties, and to optimize the generation planning under uncertainty needs to be rigorously studied. In this research, a preliminary GEP model is proposed with available input data from various sources. Discrete scenarios of possible climate change outcomes are defined and optimization models are formulated to specifically model uncertainty. Relationships between climate change and GEP parameters are defined for each scenario to consider their effects. The preliminary GEP model is then solved under each scenario to identify the climate change impact on generation expansion planning decisions. Two related optimization models are then presented and solved to find the optimal results under uncertainty: Model 1 is expected total cost minimization, and Model 2 is maximum regret minimization. Both models find compromise solutions that are suitable for all scenarios, which avoid the possible risk associated with a poor decision that is only optimal for one particular scenario, or only for an average climate change forecast.

Integrated multiperiod power generation and transmission expansion planning with sustainability aspects in a stochastic environment

This paper presents a multistage stochastic programming model to address sustainable power generation and transmission expansion planning. The model incorporates uncertainties about future electricity demand, fuel prices, greenhouse gas emissions, as well as possible disruptions to which the power system is subject. A number of sustainability regulations and policies are considered to establish a framework for the social responsibility of the power system. The proposed model is applied to a real-world case, and several sensitivity analyses are carried out to provide managerial insights into different aspects of the model. The results emphasize the important role played by sustainability policies on the configuration of the power grid.

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.

A multi-regional model for power generation expansion planning

Restructuring energy industries in developing countries has started with dividing the vertically integrated national systems into regional companies. Analysis of multi-dimensional interactions of these companies especially in long-term is a complex task that necessitates development of analytical tools. In the present paper, we have focused on the problem of GEP in the restructured energy supply system. A new energy supply model based on Cournot equilibrium is presented which could help to analyze long-term expansion planning after restructuring. The formulation of the model in a competitive environment is changed from traditional cost-minimization program to several profit-maximization programs with regional and inter-regional constraints, which leads to a LCP formulation and it can be solved by the well-known Lemke's algorithm. The electricity-supply system of Iran is then used as a case study to analyze the perspective of generation technologies in a 20-year time horizon. Analysis of the results in comparison with the traditional models shows that changes in the structure of electricity supply system of Iran provide more opportunities for innovative and renewable technologies in the electricity market.

Alternative Energy Technologies for Long-term Power Generation Expansion Planning and CO2 Mitigation in Thailand

In 2009 electricity production in Thailand was heavily dependent on fossil fuels (more than 92.73%). Domestic natural gas fields are a plentiful energy resource but it is projected to be available only for the next decade. Using domestic natural gas would reduce national energy security. However, coal will dominate in electricity generation in Thailand due to its low price. Utilization of coal results in increasing CO2 emissions. To comply with the low-carbon society policy of the Royal Thai Government, a high target of achievement in alternative energy share of 20.40% in total energy consumption in 2022 has been proposed. The obvious answer to this challenge is to take advantage of Thailand as an agriculture-based country, especially in domestic renewable energy and clean power generation. Thailand’s renewable power development plan strategy is considered by increasing the share to 2.40% in 2024. Electricity generation in this scenario is compared with future fossil-based power plants with carbon capture and storage and nuclear power in the long-term energy planning. Finally, the low carbon socity scenario for the emission abatement policy is introduced to mitigate emissions. Results are presented in total supply system cost, the incremental cost of CO2 mitigation and the co-benefits of greenhouse gas mitigation when compared to the business-as-usual scenario. Finally, Thailand could reduce CO2 emissions from the power sector by 15.27% in 2026.