Generation Capacity Expansion Research Articles

A Factorial Fractional Chance-Constrained Programming Model for Regional Electricity Systems Management under GHG Emission Mitigation — A Case Study of Saskatchewan, Canada

In this study, a factorial fractional chance-constrained programming model (FFCC) is developed for managing Saskatche- wan’s electricity systems under the pressure of greenhouse gas (GHG) emissions reduction. Through integrating multiple programming methods (i.e., linear fractional, mixed-integer linear, and chance-constrained) with factorial analysis into an optimization framework, FFCC could effectively (1) tackle multi-objective problems; (2) manage stochastic features of system parameters expressed as probability distributions and facilitate constraint-violation analysis; (3) reflect the impacts of various economic and environmental factors and their interactions on system response. Optimal electricity generation schemes, capacity expansion plans, and electricity import/export strate- gies under different policy scenarios and risk levels are explored with the objective of maximizing low-carbon power generation per unit of system cost. Results find that small modular nuclear reactor power would have the potential to replace fossil fuel-fired technologies and aid Saskatchewan in achieving net-zero carbon emissions by 2050. It is expected that the modelling results can support regional ef- forts in proposing effective power generation capacity expansion plans and relevant environmental policies.

Adaptive Two-Stage Stochastic Programming with an Analysis on Capacity Expansion Planning Problem

Problem definition: Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expansion planning problem with different generation resources including renewable energy. We demonstrate the value of adopting adaptive two-stage approach against the existing policies under limited flexibility and highlight the efficiency of the proposed heuristics along with practical implications on the studied problem. Funding: This work was supported by the National Science Foundation [Grant 1633196] and the Office of Naval Research [Grant N00014-18-1-2075]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0157 .

Electricity security in Uganda: Measurement and policy priorities

Reliable, accessible, affordable, and sustainable electricity systems are fundamental to modern and progressive economies. For developing societies, particularly those with fragile power generation capabilities and electricity networks, the electricity security metrics are incomparable and remain untested. A Multi-Criteria Decision-Making (MCDM) method that follows an entropy-based weighting system is employed to quantitatively evaluate and construct an Electricity Security Index (ESI) for Uganda. Results reveal that Uganda is “moderately” electricity secure. Out of the maximum possible score of 100, a year-on-year analysis indicates that the ESI score was highest in 2007 (56) and the lowest was registered in 2015 (39). Based on the VIKOR technique, it is observed that electricity reliability contributes the most to the ESI, while electricity access contributes the least. Although expansion of electricity generation capacity is necessary, energy policies should, in equal measure, prioritize adequate investments in robust and modern transmission and distribution infrastructure. A riveting policy implication is that governance quality enhances a country's electricity system by reinforcing the other dimensions of electricity security.

Exploring the effect of Guarantees of Origin on the decarbonization of corporate electricity procurement: A case study of Germany and Norway

AbstractIncreasing social, economic, and political pressure causes many companies to pledge to decarbonize. A common measure involves the use of Energy Attribute Certificates (EAC), such as the European Guarantees of Origin (GO), to reduce emissions from electricity procurement (Scope 2). However, previous studies find no effect on additional renewable energy capacity. Focusing on Norway and Germany as dominant contributors to net GO exports and imports, this study examines the GO trade alongside corporate carbon accounting data to answer the research question: Does the decarbonization of corporate electricity procurement using Guarantees of Origin contribute to the expansion of renewable electricity generation capacity in Norway and Germany? The analysis of CDP and Association of Issuing Bodies data reveals Norway's consumption mix is more carbon intensive than Germany's because Norway exports GO and imports fossil electricity attributes. German companies report predominantly market‐based approach, mainly using green tariffs and GO for zero emission claims, while Norwegian companies favor the location‐based approach. The largest share of GO issued in Norway comes from hydropower plants aged 41 to 70 years. The results highlight the urgency to revise corporate carbon accounting standards. GO lack additionality due to double counting of renewable attributes. Potential solutions include additionality criteria, GO trade restrictions based on physical capacities, and age limits for eligible power plants. This study's novelty is linking CDP data with GO trade, to assess the integrity of corporate decarbonization strategies. It contributes valuable insights to ongoing discourse on the role of EAC.

Emerging environmental justice issues at the intersection of transportation and electricity systems

Rapid decarbonization of the transportation system is needed to address global climate change, and electrification of the transportation sector will likely be an important strategy to achieve decarbonization goals. While electrification is an effective approach to reducing carbon emissions, it may result in environmental justice consequences that need to be tackled. We discuss four categories of related issues: air quality and health-related equity; technology access; equitable infrastructure development; and a just global supply chain. In regions where grid decarbonization is well under way, transport-related disparities in air quality and health are expected to decrease with electrification. In contrast, in regions that still rely heavily on coal generation, disparities may increase, putting further strain on low-income communities and people of color. The high upfront cost of electric vehicles and limited access to charging present additional challenges for achieving equity in transportation electrification. Meeting the electricity demand of a fully electric vehicle fleet will require rapid expansion of power generation, transmission, and distribution capacity, and the location and design of this infrastructure will have further impacts on communities where it is sited. Here, we offer a perspective on these emerging environmental justice issues at the intersection of transportation and electricity systems and provide policy recommendations and future research directions for electrified transportation. We suggest there is a need for policies targeting electrification and power sector decarbonization in tandem, assessment of barriers to electric vehicle adoption in different groups, development of strategies for community inclusion in infrastructure development decisions, and creation of frameworks to assess equity tradeoffs along the global supply chain supporting electric vehicles and renewable energy technologies.

Renewable energy expansion under taxes and subsidies: A transmission operator’s perspective

This paper investigates the role of a transmission system operator within a carbon footprint reduction strategy incorporating carbon taxes and renewable energy generation subsidies in the decentralised energy market. This is achieved via an optimisation bi-level model in which a welfare-maximising transmission system operator makes investments in transmission lines at the upper level while considering power market dynamics at the lower level. To account for the deregulated energy market structure, this paper assumes that the generation companies at the lower level make capacity investments as price-takers in perfect competition. Considering alternative transmission infrastructure expansion budgets, carbon emission taxes and monetary incentives for renewable energy generation capacity expansion, the impact of alternative compositions of these factors is analysed against three output factors: the share of renewable energy in the generation mix, total generation amount, and social welfare. The proposed modelling assessment is applied to an illustrative three-node instance and a case study considering a simplified representation of the energy system of the Nordic and Baltic countries. The results highlight that, under certain circumstances, renewable energy generation subsidies may lead to an increase of renewable energy in the generation mix followed by a simultaneous fall in the total generation amount. Nevertheless, when applied together, these three measures demonstrated a positive impact on all output factors within Nordics’ and Baltics’ energy systems. The experiments additionally suggest that considering the high value of the carbon tax does not have an impact on the output factors while the composition of high values of renewable energy generation subsidies and budget for transmission infrastructure expansion has the strongest effect.

Generation and Transmission Expansion Planning Using a Nested Decomposition Algorithm

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm’s performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern.

Evaluating the Impact of Renewable Energy Policy Instruments on Capacity Expansion: Insights from the Visegrad Group Countries

Renewable energy technologies have reached a pivotal point, demonstrating cost competitiveness with traditional energy sources and a continuing trend of affordability. Nevertheless, the successful deployment of renewable energy capacity crucially depends on the efficacy of renewable energy policy tools, casting a veil of uncertainty over their actual influence on the expansion of renewable electricity generation. This research delves into the array of policy instruments designed to bolster renewable energy capacity. Employing an econometric analysis, this study meticulously examines these policy tools, with a particular emphasis on feed-in tariffs, quotas, tenders, and tax incentives, seeking to facilitate the adoption of renewable energy within the Visegrad group countries, comprising the Czech Republic, Hungary, Poland, and Slovakia. Drawing from a comprehensive panel dataset spanning the years from 1990 to 2020, our findings unveil a nuanced perspective on the effectiveness of these renewable energy policy tools. Notably, our results underscore the varying degrees of success among different policy instruments, all of which significantly contribute to the advancement of renewable energy sources. A key revelation arises from the limited impact of quotas, a frequently employed regulatory measure, in stimulating the expansion of renewable electricity generation capacity. In contrast, feed-in tariffs, tenders, and tax incentives emerge as potent drivers in achieving this pivotal objective. This research sheds light on the dynamic landscape of renewable energy policy instruments, offering valuable insights for policymakers and stakeholders engaged in advancing sustainable and resilient energy ecosystems in the Visegrad group countries and beyond.

Accelerating Renewable Energy Integration in Energy Planning Considering the PV Techno-Economics and Hourly Profile, Case Study: Indonesian Power Sector

In planning for the generation capacity expansion planning, it is crucial to consider the economics of scale and the availability of primary energy sources. Solar power generation or PV PP is a plant that is very dependent on the availability of solar irradiance and land. To achieve Net Zero Emission by 2060, the Indonesian government aims to increase PV capacity to 4.68 GW. To support the accelerated integration of PV power plants into the electrical system and fulfill economic and sustainable aspects, the ideal capacity of PV power plants needs to be considered. This study involves optimization and comparison with various scenarios to determine the optimal combination for PV power plant planning. In the optimization process, Mixed Integer Linear Programming (MILP) is used, assuming a load of 100 MW and various PV profiles. Based on the optimization results, to meet a 100 MW load, 138 MW of PV power plants are required, with a configuration of 125 MW for large-scale PV PP, 10 MW for medium-scale PV PP, and 3 MW for rooftop PV PP. The total cost needed is 11,445 thousand dollars, with an levelized cost of electricity (LCOE) of 4.75 c$/kWh. This value is significantly lower compared to other scenarios. To supply for 24 h, PV PP can utilize BESS, with an LCOE reaching 7.79 c$/kWh when optimal capacity and generation are achieved. The recommendation for determining the capacity of PV PP is to use the large-scale capacity scheme, both for daytime supply systems and for the 24-h scheme.

Accelerating carbon neutrality in China: Sensitive intervention points for the energy and transport sectors in Beijing and Hong Kong

To limit the detrimental impacts of climate change, large-scale and rapid decarbonization is required. China announced their plan to peak carbon emissions before 2030 and to reach carbon neutrality by 2060, which faces many challenges including rising energy consumption and a significant, ongoing expansion of coal-based electricity generation capacity. This study employs mixed methods to explore a portfolio of climate policies related to the transport and energy sectors for two leading Chinese cities: Beijing and Hong Kong. A total of 32 expert interviews were conducted with four stakeholder groups in both cities to canvas opinions on the most important policies for decarbonization. With the aim to understand how local policy measures can be prioritized for disproportionately large emissions reductions, the Sensitive Intervention Points (SIPs) framework was applied to identify city-level policy interventions with the potential for high impact, speed, feasibility, persistence, and low risk, based on these expert interviews and literature review. With all attributes combined, leveraging the global cost declines in renewable energy was identified as a shared accelerated carbon neutrality pathway for both cities, facilitated by policies to promote the import of low-carbon energy and accelerating the electrification of transport. Alignments were found between this final list of SIPs and policies perceived as important by the experts, indicating that SIPs are generally intuitive, with alternative policy prioritizations likely influenced by additional factors such as the national agenda, budgetary constraints, and the availability of co-benefits.

Achieving Emission-Reduction Goals: Multi-Period Power-System Expansion Under Short-Term Operational Uncertainty

Stochastic adaptive robust optimization is capable of handling short-term uncertainties in demand and variable renewable-energy sources that affect investment in generation and transmission capacity. We build on this setting by considering a multi-year investment horizon for finding the optimal plan for generation and transmission capacity expansion while reducing greenhouse gas emissions. In addition, we incorporate multiple hours in power-system operations to capture hydropower operations and flexibility requirements for utilizing variable renewable-energy sources such as wind and solar power. To improve the computational performance of existing exact methods for this problem, we employ Benders decomposition and solve a mixed-integer quadratic programming problem to avoid computationally expensive big-M linearizations. The results for a realistic case study for the Nordic and Baltic region indicate which investments in transmission, wind power, and flexible generation capacity are required for reducing greenhouse gas emissions. Through out-of-sample experiments, we show that the stochastic adaptive robust model leads to lower expected costs than a stochastic programming model under increasingly stringent environmental considerations.

The impact of economic development of primary and secondary industries on national CO2 emissions: The case of Russian regions

In today's world, where economic development and environmental sustainability are becoming increasingly important aspects of national strategy, attention to the impact of different economic sectors on climate change is becoming an integral part of scientific research. This article focuses on analyzing the impact of primary and secondary economic sectors development on carbon dioxide (CO2) emissions at the sub-national level in Russia from 2005 to 2019. The aim of the study is to provide an in-depth understanding of the relationships between the dynamics of these sectors and CO2 emission levels in different regions of the country. Weighted regression and panel data methods were applied to better identify the patterns of the impact. The results show that the size of population and electricity consumption have the highest impact on CO2 emissions. So that, the expansion of nuclear and gas generation capacity, as well as significant improvement of energy efficiency, are of crucial importance to reduce the emissions. Other sectors have a heterogeneous impact and requires more differential approaches, considering the specifics of regions. Taking into account the significant differences between the Russian constituent entities, this paper emphasizes the low informativeness of assessments at the national level and their inadequacy in terms of improving the efficiency of domestic management, including decarbonization policies.

Implications of a smart grid-integrated renewable distributed generation capacity expansion strategy: The case of Iraq

The rapid growth of electricity demand in Iraq has consistently outstripped the country's electricity infrastructure, leading to frequent blackouts, especially during peak summer demand. With a heavy reliance on oil 93 % and gas 7 % for electricity generation, the nation is exposed to economic fluctuations associated with global oil prices and environmental impacts from burning fossil fuels. This study explores the implications of integrating smart grids and expanding renewable distributed generation capacity in the country's energy system. Drawing upon the projection of the energy demand by 2035 across four scenarios (Business as Usual, Sustainable Development, High Growth, and Low Carbon), the study underscores solar energy, backed by abundant solar and wind energy, as a key player in the country's renewable transition. However, challenges persist, including the need for technical expertise in renewable energy technologies, financing hurdles, and concerns about policy stability. With the country's policies targeting 25 % renewable electricity by 2030 and the establishment of significant renewable projects, there tangible momentum. The results have drawn out a roadmap for an estimated addition of up to 10 GW of renewable energy capacity by 2030 and 14 GW by 2035. In addition, strategies for distributed generation capacity expansion were highlighted, underscoring the crucial role of solar and wind energy and other renewables in the country's sustainable energy future.

Wind-solar-storage trade-offs in a decarbonizing electricity system

Exploring cost-effective wind-solar-storage combinations to replace conventional fossil-fuelled power generation without compromising grid reliability becomes increasingly important in a steadily decarbonizing electricity system. For a renewable energy-rich state in Southern India (Karnataka), we systematically assess various wind-solar-storage energy mixes for alternate future scenarios, using Pareto frontiers. The simulated scenarios consider assumed growth in electricity demand, and different levels of base generation and supply-side flexibility from fossil fuels and hydropower. Our approach uses hourly load data, simulates generation based on hourly weather reanalysis data, and models the effects of battery charging and discharging on battery lifetimes. In the context of declining base generation and limited flexibility in the state electricity grid, the reliability of meeting demand is limited by the generation curtailment that is permitted. We show that adding battery storage capacity without concomitant expansion of renewable generation capacity is inefficient. Keeping the wind-solar installations within the officially assessed renewable potential, a fully decarbonized grid with limited flexibility can have approximately 63% reliability even after using adequate battery storage. Achieving 99% grid reliability would be expensive, requiring large wind-solar installations that exceed officially assessed potential, constrained by land allocation. The findings highlight the importance of a fresh examination of curtailment thresholds, available renewable potential, and possibilities of demand-side management based on consumers' willingness to modify hourly demand patterns.

Comparative Study for Risk Criteria of Al-Qudus Plant between the Present and Planning of MOE

The main function of a power system is to supply the customer load demands as economically as possible. Risk criterion is the probability of not meeting the load. This paper presents a methodology to assess probabilistic risk criteria of Al-Qudus plant before and after expansion; as this plant consists of ten generating units presently and the Ministry Of Electricity (MOE) is intending to compact four units to it in order to improve the performance of Iraqi power system especially at Baghdad region. The assessment is calculated by a program using Matlab programming language; version 7.6. Results show that the planned risk is (0.003095) that is (35 times) less than that in the present plant risk; (0.1091); which represents respectable improvement. This probabilistic method can also be used to find the planned risk level of every plant to be compact in the Iraqi electrical network on the future; or any other power systems; and compare it with the present criterion which is very useful to determine the necessary generation capacity expansion.

Generation Capacity Expansion Considering Hydrogen Power Plants and Energy Storage Systems

The design of decarbonized power systems is one of the most relevant and challenging problems that power system planners are facing nowadays. In this sense, the replacement of natural gas turbines by H2-fired gas turbines in future power systems may constitute a solution to reduce greenhouse gas emissions maintaining the dispatchability of the system. This work develops a novel generation capacity expansion formulation that considers the possibility of installing new H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -fired gas turbines, as well as renewable generation and different storage technologies. The proposed model also determines the investment decisions related to the installation of electrolyzers to produce H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , as well as H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> storage facilities. The provision of reserve capacity by generating units, storage and electrolyzers is also considered to determine the optimal investment decisions. The uncertainty related to the demand growth and the capital costs of electrolyzers and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> storage is explicitly considered by using a stochastic programming formulation. A realistic case study based on an actual isolated power system is solved to test the proposed formulation. The obtained results indicate that H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> power plants are installed in all cases in which this technology is available. Additionally, the capacity installed of electrolyzers is over 2.5 times higher than that of H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> power units.

A Linear Chance-Constrained Mixed-Integer Programming Model for Optimizing Regional Electric Power Systems under Carbon Constraints

In view of increasing population size and energy consumption, greenhouse gas (GHG) emissions are increasing and are one of the main causes of climate change. The regional electric power system is one of the main sources of carbon emissions, so there is an urgent need to optimize the regional electric power system to meet the Paris Agreement's long-term temperature goal. Therefore, this study provided a linear chance-constrained mixed-integer programming (LCMI) model with the objective of maximizing the total system profit and applying it to the regional electric power system. Chance-constrained programming and mixed-integer programming were integrated into the LCMI model to address input uncertainties. Including five commonly used power generation technologies, namely coal-fired, natural gas-fired, hydropower, wind power, and solar power, the model can provide the optimal electricity generation schemes and capacity expansion plans for different technologies at the regional level to meet the end-user’s needs while meeting the carbon dioxide emission targets under different risk levels. The outcomes of the research will offer decision-makers a framework for optimizing conventional regional electric power systems for their long-term sustainability in environmental and economic development.

Experimentation with Benders decomposition for solving the two-timescale stochastic generation capacity expansion problem

The main purpose of solving a classical generation capacity expansion problem is to ensure that, in the medium- to long-term time frame, the electric utility has enough capacity available to reliably satisfy the demand for electricity from its customers. However, the ability to operate the newly built power plants also has to be considered. Operation of these plants could be curtailed by fuel availability, environmental constraints, or intermittency of renewable generation. This suggests that when generation capacity expansion problems are solved, along with the yearly timescale necessary to capture the long-term effect of the decisions, it is necessary to include a timescale granular enough to represent operations of generators with a credible fidelity. Additionally, given that the time horizon for a capacity expansion model is long, stochastic modeling of key parameters may generate more insightful, realistic, and judicious results. In the current model, we allow the demand for electricity and natural gas to behave stochastically. Together with the dual timescales, the randomness results in a large problem that is challenging to solve. In this paper, we experiment with synergistically combining elements of several methods that are, for the most part, based on Benders decomposition and construct an algorithm which allows us to find near-optimal solutions to the problem with reasonable run times.

The important contribution of renewable energy technologies in overcoming Pakistan's energy crisis: Present challenges and potential opportunities

In recent years, the environmental and economic consequences of coal, gasoline and other conventional energy sources have been widely discussed. Currently, Pakistan's energy output is highly dependent on these resources; as a consequence, the country is facing a severe energy crisis. The government spends more than $3.7 billion annually on fossil fuel imports, which has a significant impact on an already vulnerable economy. Moreover, the country is ranked as the seventh-most impacted region by climate change, making it critical for the government to take proactive measures. Renewable energy that is both economical and sustainable will become a realistic and viable choice for satisfying Pakistan's current and future energy demands. This article provides a detailed overview of Pakistan's long-standing energy scarcity concerns and discusses the country's current renewable energy challenges and opportunities. Additionally, Pakistan's efforts in the realm of renewable energy are compared to those of other Asian nations in order to understand how these countries are advancing renewable energy sources. Numerous statistics show that despite government initiatives, the amount of power generated from renewable sources falls short of the Indicative Generation Capacity Expansion Plan’s (IGCEP’s) 2025 and 2030 targets. Achieving these aspirational targets will require competent policies, incentives, technological expertise and substantial political and financial commitments. Hence, this article also advised policymakers and city municipalities on how to enhance energy infrastructure, knowledge and the capacity for overcoming future challenges. In contrast, the development of renewable energy in Pakistan has been an extremely successful endeavour, but an effective and efficient approach is necessary to leverage the benefits of rapid progress.

Socio-Economic Prospects of Solar PV Uptake in Energy Policy Landscape of Pakistan

Despite global calls for climate change and its impacts in past decade, energy sector of Pakistan has remained highly dominated by high-cost carbon-intensive resources. Although a significant number of policies have been put forward by both provincial and federal government in last three years, the ground-level implementation of these policies is non-existent, and Pakistan’s progress is still far behind the developed countries. This study therefore performs a socio-economic analysis of solar PV potential in Pakistan and how recent policies can be mobilized to upscale the utilization of solar PV both as an on-grid and off-grid generation source. This also links to solar potential for corporate sector engagements in their Net-Zero Pathways. The methodological approach uses a Low Emission Analysis Platform (LEAP) model designed for Pakistan’s Power System supplies under three different scenarios i.e., Energy Transition Scenario, Conventional Generation Scenarios, and Business as Usual Scenario. Indicative Generation Capacity Expansion Plan (IGCEP 2021) along with recent policies is used as the leading data source for driving the capacity additions. The results obtained from the model indicates that despite having a large potential, under currently policies the share of solar in total grid power generation will remain under 2% by 2030. Under Energy Transition Scenario, the model runs under a least cost optimization plan leading to a higher uptake of solar power. As per this scenario, the share of renewable increase beyond 2030 to achieve a share of around 50% by 2045. This can lead to cumulative carbon reductions of around 2000 Mt by 2030 and economic savings of around $ 5 billion. Based on the model results, this study also identifies the possible pathways for upcoming iterations of Pakistan IGCEP plan that builds around solar PV