Transmission Expansion Research Articles

The value of long-duration energy storage under various grid conditions in a zero-emissions future

Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. Using the Switch capacity expansion model, we model a zero-emissions Western Interconnect with high geographical resolution to understand the value of LDES under 39 scenarios with different generation mixes, transmission expansion, storage costs, and storage mandates. We find that a) LDES is particularly valuable in majority wind-powered regions and regions with diminishing hydropower generation, b) seasonal operation of storage becomes cost-effective if storage capital costs fall below US$5 kWh−1, and c) mandating the installation of enough LDES to enable year-long storage cycles would reduce electricity prices during times of high demand by over 70%. Given the asset and resource diversity of the Western Interconnect, our results can provide grid planners in many regions with guidance on how LDES impacts and is impacted by energy storage mandates, investments in LDES research and development, and generation mix and transmission expansion decisions.

Multi-parametric Analysis for Mixed Integer Linear Programming: An Application to Transmission Upgrade and Congestion Management

Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. We compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.

A New Effective Hybrid Meta-heuristic Algorithm for Solving AC Transmission Expansion Planning Problem

A New Effective Hybrid Meta-heuristic Algorithm for Solving AC Transmission Expansion Planning Problem

Coal Phase-Out and Carbon Tax Analysis with Long-Term Planning Models: A Case Study for the Chilean Electric Power System

Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO2. Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme.

An Extra-High Voltage Test System for Transmission Expansion Planning Studies Considering Single Contingency Conditions

This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using the equivalent π circuit model, accurately reflecting the distributed nature of the line parameters. The proposed test system offers technically feasible steady-state operation under normal and all single contingency conditions. By incorporating accurate modeling for long transmission lines and EHV voltage levels, the test system provides a realistic platform for validating models and theories prior to their application in actual power systems. It supports testing new algorithms, control strategies, and grid management techniques, aids in transmission expansion planning and investment decisions, and facilitates comprehensive grid evaluations. Moreover, a TEP study is conducted on this test system and various scenarios are evaluated and compared economically.

Automatic measurement of rebar spacing based on 3D point cloud segmentation using Rebar-YOLOv8-seg and depth data

In power transmission and transformation expansion projects, construction personnel must measure rebar spacing at construction sites to ensure the quality of concrete structures. Due to electrified equipment, high-precision steel rulers are prohibited. We introduced a new method for automatically measuring rebar spacing to improve construction safety and work efficiency. We developed the Rebar-YOLOv8-seg model to accurately extract the rebar image mask from complex backgrounds. Subsequently, the rebar mask was aligned with depth data to create a point cloud, which performed statistical filtering and principal component analysis. Finally, we used the Random Sample Consensus method to fit the point cloud centerline, then extracted key points and calculated rebar spacing. Experiments on 4 × 4 rebar measurements using the proposed method showed that, within a specific range of camera heights and shooting angles, the measured values of rebar spacing meet the engineering measurement requirements. The method achieves an average absolute error of 1.98 mm and an average relative error of 1.76 %. Additionally, multiple adjacent rebar spacings can be measured simultaneously and completed within 5 s, providing a new feasible approach for rebar spacing measurement.

Economic Load Dispatch on a 132 kV Line with Service Potential Transformer Substations: A Case Study of Juja-Rabai Line.

Power outages have created significant challenges for power system networks, particularly in developing countries where the electricity demand continues to rise without a corresponding increase in power generation or the expansion of transmission and distribution networks. In Kenya, while there is a well-established transmission line network, the distribution infrastructure remains inadequate for supplying electricity to end consumers. This paper examines the economic load dispatch (ELD) of power system networks utilizing Service Potential Transformer (SPT) substations to provide electricity to villages located near high voltage (HV) lines. The ELD analysis was conducted to identify the optimal economic power output from the Kipevu, Rabai, and Thika thermal power plants, addressing the demand for both conventional and non-conventional substations. A gradient method was employed to calculate the ELD for these three generating units, and the results were validated using the PowerWorld simulator. Findings indicated that the three generators supplied 20 MW, 37.5 MW, and 12.5 MW, respectively. The results obtained from the gradient method are consistent with those obtained from PowerWorld software. Additionally, this study projected an annual fuel cost savings of USD 17,695.20 when ELD was implemented, compared to a scenario of equal load distribution among generating units. Over a ten-year period, these savings would be sufficient to establish a conventional distribution substation to meet the power demands of villages located further away from high voltage lines.

Bi-level expansion planning of power system considering wind farms based on economic and technical objectives of transmission system operator

Planning for the expansion of generation and transmission infrastructure, with a focus on integrating wind farms is presented in this study according to the goals of economic efficiency, operational performance, security, and reliability of the transmission system operator. The approach incorporates a bi-level optimization framework, with the upper level outlining the formulation of power system expansion planning to reduce construction and operational costs while adhering to the investment budget limits. In the lower-level model, which economic reliable-secure functioning is formulated for the transmission network. The objective function of the problem aims to minimize operational costs, energy losses, and anticipated energy not supplied, and the voltage security index. The constraints of this problem include AC optimal power flow model, security, and reliability limits. Scheme extracts a convex-linear model for the formulation using the conventional linearization technique. Pareto optimization driven by the aggregation of weighted functions results in a single-objective framework for the lower-level problem, and the Karush-Kuhn-Tucker technique presents a single-level model for the scheme. The approach utilizes stochastic optimization has been adopted to model load, wind farms, and network equipment availability uncertainties. Finally, the study cases yielded numerical findings that showcase the efficacy of the proposed scheme in achieving the desired economic, operational, security, and reliability status within the transmission network. This is particularly true in situations where there is appropriate generation and transmission expansion.

Accelerating transmission capacity expansion by using advanced conductors in existing right-of-way

As countries pursue decarbonization goals, the rapid expansion of transmission capacity for renewable energy (RE) integration poses a significant challenge due to hurdles such as permitting and cost allocation. However, we find that large-scale reconductoring with advanced composite-core conductors can cost-effectively double transmission capacity within existing right-of-way, with limited additional permitting. This strategy unlocks a high availability of increasingly economically viable RE resources in close proximity to the existing network. We implement reconductoring in a model of the US power system, showing that reconductoring can help meet over 80% of the new interzonal transmission needed to reach over 90% clean electricity by 2035 given restrictions on greenfield transmission build-out. With $180 billion in system cost savings by 2050, reconductoring presents a cost-effective and time-efficient, yet underutilized, opportunity to accelerate global transmission expansion.

A comprehensive study on energy management, sensitivity analysis, and inertia compliance of feed-in tariff in IEEE bus systems with grid-connected renewable energy sources

The need to incorporate renewable energy generators (REGs) into the electrical grid has become increasingly crucial due to the push for a more sustainable environment. This study advocates an innovative strategy for optimizing inertia-integrated generation and transmission expansion planning (GTEP) to implement feed-in tariffs (FiT). The application of the GAMS CPLEX solver to the model, which tested on an IEEE 6/IEEE 16 system, reveals that using FiT results in a 12.1 % drop in system cost ($599 million to $526 million) and a 7.91 % rise in total system inertia. Sensitivity analysis highlights the correlation between increased REG integration and FiT payment reduction at 50 % penetration. The model outperforms soft computing optimization techniques, showcasing rapid convergence and computational efficiency. The proposed model's validated superiority in rapid convergence and computational efficiency is demonstrated by comparing its results with those obtained from other soft computing optimization techniques.

Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem

The necessary decarbonization efforts in energy sectors entail integrating flexible assets and increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-term system developments and the operation of power grids with intermittent renewable generation. However, the increased modeling complexities of TEP exercises can easily lead to computationally intractable optimization problems. Currently, most techniques that address computational intractability alter the original problem, thus neglecting critical modeling aspects or affecting the structure of the optimal solution. In this paper, we propose an alternative approach to significantly alleviate the computational burden of large-scale TEP problems. Our approach integrates machine learning (ML) with the well-established Benders decomposition to manage the problem size while preserving solution quality. The proposed ML-enhanced Multicut Benders Decomposition algorithm improves computational efficiency by identifying effective and ineffective optimality cuts via supervised learning techniques. We illustrate the benefits of the proposed methodology by solving multi-stage TEP problems of different sizes based on the IEEE24 and IEEE118 test systems, while also considering energy storage investment options..

A Wasserstein distributionally robust model for transmission expansion planning with renewable‐based microgrid penetration

AbstractThis article introduces a Wasserstein distance‐based distributionally robust optimization model to address the transmission expansion planning considering renewable‐based microgrids (MGs) under the impact of uncertainties. The primary objective of the presented methodology is to devise a robust expansion strategy that accounts for both long‐term uncertainty and short‐term variability over the planning horizon from the perspective of a central planner. In this framework, the central planner fosters the construction of appropriate transmission lines and the deployment of optimal MG‐based generating units among profit‐driven private investors. The Wasserstein distance uncertainty set is used to characterize the long‐term uncertainty associated with future load demand. Short‐term uncertainties, stemming from variations in load demands and production levels of stochastic units, are modeled through operating conditions. To ensure the tractability of the proposed planning model, the authors introduce a decomposition framework embedded with a modified application of Bender's method. To validate the efficiency and highlight the potential benefits of the proposed expansion planning methodology, two case studies based on simplified IEEE 6‐bus and IEEE 118‐bus systems are included. These case studies assess the effectiveness of the presented approach, its ability to navigate uncertainties, and its capacity to effectively optimize expansion decisions.

Energy expansion planning with a human evolutionary model

This study presents a novel method for planning the expansion of transmission lines and energy storage systems while considering the interconnectedness of electricity and gas networks. We developed a two-level stochastic planning model that addresses both the expansion of transmission and battery systems in the electrical grid and the behavior of the gas network. This research explores the challenges and effects of integrating high levels of renewable energy sources while ensuring security within both networks. Our model uses a stochastic mixed-integer non-linear programming approach. To solve this complex model, we applied the Human Evolutionary Model (HEM). We tested our approach on two case studies: a simple 6-node network and the more complex IEEE RTS 24-bus network for the electricity grid, combined with 5-node and 10-node gas networks, respectively. The results demonstrate the effectiveness of our model, particularly in scenarios where connections in the power and gas networks are disrupted, preventing load shedding even when integrated network connections are cut.

Chronological DC transmission expansion planning considering new energy and load uncertainties

Abstract The integration of renewable energy into the grid and the expansion of new loads have significantly increased the uncertainty of the power system, which has brought many new challenges to power system planning. Hence, it is necessary to establish a transmission expansion planning model that adapts to future development to improve the economic benefits of power grid planning. This paper proposes a chronological DC transmission expansion planning method for new energy sources, such as wind and solar energy, considering the time characteristics of source loads. First, the method establishes a chronological DC transmission expansion planning model by simulating long-term grid operations. Among them, the objective function aims to minimize the sum of investment costs and power generation operating costs related costs. Secondly, to simulate unit operation, a loop optimization modeling method is used to optimize the model structure. Finally, simulation analysis is conducted using the improved Garver’s 6 bus system under random planning scenarios as a basis for comparison. By comparing various scenarios, it is concluded that the proposed chronological DC transmission expansion planning can provide a more economical solution than non-chronological planning. This helps ensure that the system operates cost-effectively under uncertain source and load scenarios while enhancing the integration capabilities of wind and solar power generation.

Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration

Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.

Power system loading margin enhancement based on a multi-objective TCSC placement to mitigate the risk of blackouts

In the evolving power systems, loading margin (LM) enhancement has a significant effect on the mitigation of the risk of cascading line outages and thereby the occurrence of large blackouts. On the other hand, flexible alternating current transmission system (FACTS) devices, especially thyristor-controlled series compensated (TCSC) devices, are more effective than other mitigation strategies such as constructing new lines because of environmental and economic reasons. In this paper, a multi-objective framework is presented to maximize the LM with a minimum installation cost of TCSCs. The optimization problem is solved by using the ɛ-constraint method. The benefit of each Pareto solution is calculated by the probabilistic risk assessment, and the most preferred one is selected by a benefit/cost analysis. The proposed method is developed based on a modified alternating current (AC) version of the ORNL–Pserc–Alaska (OPA) model inspired by the self-organized criticality (SOC) theory in complex power grids. Using the benefit/cost criterion, it is revealed that by the proposed approach, a larger value for the most preferred Pareto solution is obtained in comparison with the defined transmission expansion planning (TEP) scenarios. It confirms the suitability of the proposed method to suppress the cascading outages instead of expensive and time-consuming policies such as building new lines.

Exact approaches to solve the Transmission Expansion Planning Problem with Re-design

Expanding electrical transmission networks is a critical challenge faced by many developing economies. This process requires extensive investments, which must be strategically and methodically planned on a large scale, often encompassing regional or national considerations. Differing from typical Network Design Problems, efficiency in a transmission network can sometimes be enhanced by strategically deactivating certain transmission lines. Thanks to that, this paper focuses on a version of the transmission expansion planning problem that allows redesign during the network expansion. We propose using two exact methodologies as alternatives to the direct use of mixed integer programming formulations with commercial solvers. These methods are an application of Benders’ decomposition and a newly developed approach named Ring Space. Through comprehensive computational experiments, we assess and compare the efficacy of these methods against the conventional application of the mathematical formulation. Additionally, this study successfully obtained new high-quality solutions and proved their optimality for a subset of benchmark instances.

Machine learning-based uncertainty analysis in power system planning: Insights and pathways for decarbonization

This study performs an extensive uncertainty analysis of decarbonization pathways for the Canadian power system. By leveraging machine learning (ML) techniques, we represent complexities and nonlinear behaviors of the power system, which are challenging to observe using traditional modeling methods due to computational limitations. We further utilize K-means clustering and statistical methods to identify correlations, investigate the effects of uncertainty in key parameters (capital costs, demand, and carbon tax), and identify opportunities for diversification of the power generation mix. Key findings include the increased flexibility of gas combined cycle (generation mean 4.8 TWh, installed capacity mean 4.8 GW) over gas simple cycle (generation mean 22.3 GWh, 0 installed capacity) in response to carbon tax, highlighting its role in a low-carbon future. Variations in solar and wind deployment rates, influenced by capacity factors and location-specific costs, underscore the necessity of a flexible grid system. This analysis reveals important correlations between renewable deployment, and transmission expansion and gas combined cycle reliance. The results also highlight minimal sensitivity of hydroelectric power generation (∼ 43 % of scenarios) to changes in input parameters. The novelty of our work lies in proposing a comprehensive approach which uses ML techniques to go beyond the capabilities of existing models, specifically by performing extensive uncertainty analysis and provides key insights into system behavior, causative factors, and an expanded breadth of potential pathways. Taken together, this proposed approach lays a foundation for strategic planning and policy formulation.

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Reliability-constrained transmission expansion planning based on simultaneous forecasting method of loads and renewable generations

Reliability-constrained transmission expansion planning based on simultaneous forecasting method of loads and renewable generations