Transmission Expansion Research Articles

A quasi-static time-series approach to assess the impact of high distributed energy resources penetration on transmission system expansion planning

Due to advancements in solar cell fabrication and inverter-based technology, as well as decreasing acquisition costs, solar distributed generation systems have emerged as a promising renewable source. Furthermore, electrification of the transportation system is an alternative promoted by many governments to mitigate the dependency on fossil fuels and achieve the goals of decarbonizing the economy. However, interconnecting a large quantity of distributed energy resources (DERs) to the power system reveals technical problems affecting the entire system. Planning coordinators and grid operators need to understand the full-spectrum impact of DERs on the power system to ensure secure and reliable grid planning and operations. The operation of a Hydro-Québec transmission system planned for 2030 is simulated in quasi-static time series mode with a massive integration of 1 million of electric vehicles and 1000 – 3000 MW of distributed photovoltaics. This paper provided findings on assessment of high DER penetrations impacts on the transmission system in terms of voltage control, mitigation means used, MVAR availability and consumption margin, generating unit optimal operation, and switching actions of several equipment.

A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

Interpreting the value of flexibility in AC security-constrained transmission expansion planning via a cooperative game framework

Security-constrained transmission expansion planning (SCTEP) is an inherently complex problem that requires simultaneously solving multiple contingency states of the system (usually corresponding to N-1 security criterion). Existing studies focus on effectively finding optimal solutions; however, single optimal solutions are not sufficient to interpret the value of flexibility (e.g., from energy storage systems) and support system planners in well-informed decision making. In view of planning uncertainties, it is necessary to estimate the contributions of flexibility to various objectives and prioritise the most effective investments. In this regard, this work introduces a SCTEP tool that enables interpreting the value of flexibility in terms of contributions to avoided load curtailment and total expected system cost reduction. Inspired by cooperative game theory, the tool ranks the contributions of flexibility providers and compares them against traditional line reinforcements. This information can be used by system planners to prioritise investments with higher contributions and synergistic capabilities.

Co-optimizing transmission and BESS expansions with system strength constraints

The increased penetration of renewables through converter interfaced generation (CIG) and the associated displacement of synchronous generators (SGs) can significantly reduce system strength. Although some network enhancements, such as the installation of battery energy storage systems (BESS), can partially counterbalance the reduction in strength, current network planning methodologies overlook system strength effects. In this context, this work proposes a new optimization model for co-optimizing investments in network expansions and battery systems to meet, at minimum cost, system strength requirements quantified through short circuit levels (SCL). Recognizing that SCL needs vary on a case-by-case basis, this model incorporates a logistic regression analysis trained on offline time-domain dynamic simulations; this approach effectively correlates SCL with system stability, providing a tailored strategy for each power system. To solve the optimization, we also propose an algorithm to iteratively find the optimal solution for the co-optimized portfolio of new transmission and energy storage investments. Through various case studies, we demonstrate the importance of considering system strength requirements in planning studies with significant penetration of CIG.

Robust co-planning of transmission expansion and merchant energy storage investments considering long- and short-term uncertainties

The decreasing cost of energy storage technologies and the increasing utilization of renewable energy sources have made distributed transmission-scale energy storage economically viable. Merchant energy storage investors can collect significant profits through spatiotemporal energy arbitrage from congested transmission lines. This paper formulates a three-stage, four-level min–min–max–min problem from the viewpoint of power system operators and planners such that they will build new transmission lines and identify potential locations for profitable merchant energy storage investments. The first level determines transmission expansion decisions from the system operator’s perspective. The second level plans potential investments in merchant energy storage to ensure profitability at a desired rate of return. The third level realizes the worst-case situations of long-term uncertainties related to the expansion of wind farms and peak load growth in each regional zone, while the fourth level simulates market clearing for representative days, modeling short-term uncertainties. A novel iterative nested column and constraint generation (C&CG) technique is adopted to cope with numerical issues caused by binary variables of transmission lines and storage blocks and the enormous size of the problem. The case study of a modified IEEE 24-bus test system was used to assess the effectiveness of the proposed algorithm.

A comprehensive stochastic-based adaptive robust model for transmission expansion planning

To address various uncertainties appearing in the transmission expansion planning (TEP) problem, appropriate tools such as stochastic programming (SP) or robust optimization (RO) are required. On the other hand, both long-term (LT) and short-term (ST) uncertainties must be well captured to acquire the most reliable and robust solution. The current works model the LT uncertainty by RO and the ST uncertainty by SP, forming a two-stage adaptive robust optimization problem. In contrast, this paper presents a novel approach in which the SP and RO are simultaneously utilized to cope with each type of uncertainty, i.e., LT and ST uncertainty. Unlike the current methods with bilinear terms appearing in the subproblems, the trilinear terms appear in the objective function of the proposed approach, which are linearized accordingly. In addition, we propose a mixed-integer bilinear programming (MIBP) model to specify the rectangles used as uncertainty sets, which is further solved using Konno's algorithm. A standard column-and-constraint (CCG) generation solves the established tri-level structure. Two case studies are used to implement the formulation developed in this work. Results signify the effectiveness of the proposed method.

Cost-Effective Target Capacity Assessment in the Energy Transition: The Italian Methodology

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained.

Integrated transmission expansion planning incorporating fault current limiting devices and thyristor-controlled series compensation using meta-heuristic optimization techniques

Transmission expansion planning (TEP) is a vital process of ensuring power systems' reliable and efficient operation. The optimization of TEP is a complex challenge, necessitating the application of mathematical programming techniques and meta-heuristics. However, selecting the right optimization algorithm is crucial, as each algorithm has its strengths and limitations. Therefore, testing new optimization algorithms is essential to enhance the toolbox of methods. This paper presents a comprehensive study on the application of ten recent meta-heuristic algorithms for solving the TEP problem across three distinct power networks varying in scale. The ten meta-heuristic algorithms considered in this study include Sinh Cosh Optimizer, Walrus Optimizer, Snow Geese Algorithm, Triangulation Topology Aggregation Optimizer, Electric Eel Foraging Optimization, Kepler Optimization Algorithm (KOA), Dung Beetle Optimizer, Sea-Horse Optimizer, Special Relativity Search, and White Shark Optimizer (WSO). Three TEP models incorporating fault current limiters and thyristor-controlled series compensation devices are utilized to evaluate the performance of the meta-heuristic algorithms, each representing a different scale and complexity level. Factors such as convergence speed, solution quality, and scalability are considered in evaluating the algorithms’ performance. The results demonstrated that KOA achieved the best performance across all tested systems in terms of solution quality. KOA’s average value was 6.8% lower than the second-best algorithm in some case studies. Additionally, the results indicated that WSO required approximately 2–3 times less time than the other algorithms. However, despite WSO’s rapid convergence, its average solution value was comparatively higher than that of some other algorithms. In TEP, prioritizing solution quality is paramount over algorithm speed.

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO.

Antimicrobial resistance and population genomics of emerging multidrug-resistant Salmonella 4,[5],12:i:- in Guangdong, China.

Salmonella 4,[5],12:i:-, a monophasic variant of Salmonella Typhimurium, has emerged as a global cause of multidrug-resistant salmonellosis and has become endemic in many developing and developed countries, especially in China. Here, we have sequenced 352 clinical isolates in Guangdong, China, during 2009-2019 and performed a large-scale collection of Salmonella 4,[5],12:i:- with whole genome sequencing (WGS) data across the globe, to better understand the population structure, antimicrobial resistance (AMR) genomic characterization, and transmission routes of Salmonella 4,[5],12:i:- across Guangdong. Salmonella 4,[5],12:i:- strains showed broad genetic diversity; Guangdong isolates were found to be widely distributed among the global lineages. Of note, we identified the formation of a novel Guangdong clade (Bayesian analysis of population structure lineage 1 [BAPS1]) genetically diversified from the global isolates and likely emerged around 1990s. BAPS1 exhibits unique genomic features, including large pan-genome, decreased ciprofloxacin susceptibility due to mutation in gyrA and carriage of plasmid-mediated quinolone resistance (PMQR) genes, and the multidrug-resistant IncHI2 plasmid. Furthermore, high genetic similarity was found between strains collected from Guangdong, Europe, and North America, indicating the association with multiple introductions from overseas. These results suggested that global dissemination and local clonal expansion simultaneously occurred in Guangdong, China, and horizontally acquired resistance to first-line and last-line antimicrobials at local level, underlying emergences of extensive drug and pan-drug resistance. Our findings have increased the knowledge of global and local epidemics of Salmonella 4,[5],12:i:- in Guangdong, China, and provided a comprehensive baseline data set essential for future molecular surveillance.IMPORTANCESalmonella 4,[5],12:i:- has been regarded as the predominant pandemic serotype causing diarrheal diseases globally, while multidrug resistance (MDR) constitutes great public health concerns. This study provided a detailed and comprehensive genome-scale analysis of this important Salmonella serovar in the past decade in Guangdong, China. Our results revealed the complexity of two distinct transmission modes, namely global transmission and local expansion, circulating in Guangdong over a decade. Using phylogeography models, the origin of Salmonella 4,[5],12:i:- was predicted from two aspects, year and country, that is, Salmonella 4,[5],12:i:- emerged in 1983, and was introduced from the UK, and subsequently differentiated into the local endemic lineage circa 1991. Additionally, based on the pan-genome analysis, it was found that the gene accumulation rate in local endemic BAPS 1 lineage was higher than in other lineages, and the horizontal transmission of MDR IncHI2 plasmid associated with high resistance played a major role, which showed the potential threat to public health.

An optimal expansion planning of power systems considering cycle-based AC optimal power flow

This paper presents a novel mixed-integer linear optimization formulation of the AC network-constrained, cost-based, integrated expansion planning problem. The formulation is used to determine the investment needs per technology including the location and sizing of new generation, energy storage, and transmission network assets in a future low-carbon power system. To reduce the size of the resulting problem, the AC optimal power flow (AC-OPF) model is represented in a compact way using cycle constraints. A bound tightening procedure is also considered to reduce the search space and improve the solver performance by adjusting the voltage bounds within the AC-OPF. Contrary to typically used formulations of the integrated expansion planning problem, the constraints considered here include all main aspects of system operation, namely unit commitment, energy storage system management, AC-OPF, and reactive power compensation. Thus, in this paper, we examine how both the proposed transmission expansion modeling developments and the interrelation of the integrated planning constraints affect the computation of the solution to the expansion planning problem. The performance of this formulation is assessed on the RTS-GMLC test system by computing the expansion plan and comparing it with the results of three other expansion planning formulations most frequently employed in the recent literature to address the integrated expansion planning problem for medium to large-scale systems. Expansion plans are computed and compared for different case studies and multiple scenarios. According to the comparative analysis, neglecting the AC-OPF or the unit commitment constraints can increase the total system costs by 7.10%–9.57% or 6.29%–8.39%, respectively. Unlike other modeling approaches, the proposed approach does not rely on simplifications that impact the quality of the solution. Thanks to the incorporated cycle-based AC-OPF constraints and the consideration of a bound tightening procedure, the computation time is reduced by 17.67%–27.21%.

Genomic epidemiology of Staphylococcus aureus from the Iberian Peninsula highlights the expansion of livestock associated-CC398 towards wildlife

Staphylococcus aureus is a versatile pathobiont, exhibiting a broad host range, including humans, other mammals, and avian species. Host specificity determinants, virulence, and antimicrobial resistance genes are often shared by strains circulating at the animal-human interface. While transmission dynamics studies have shown strain exchange between humans and livestock, knowledge of the source, genetic diversification, and transmission drivers of S. aureus in wildlife lag behind.In this work, we explore a wide array of S. aureus genomes from different sources in the Iberian Peninsula to understand population structure, gene content and niche adaptation at the human-livestock-wildlife nexus. Through Bayesian inference, we address the hypothesis that S. aureus strains in wildlife originate from humanized landscapes, either from contact with humans or through interactions with livestock. Phylogenetic reconstruction applied to whole genome sequence data was completed with a dataset of 450 isolates featuring multiple clones from the 1990–2022 period and a subset of CC398 strains representing the 2008–2022 period. Phylodynamic signatures of S. aureus from the Iberian Peninsula suggest widespread circulation of most clones among humans before jumping to other hosts. The number of transitions of CC398 strains within each host category (human, livestock, wildlife) was high (88.26 %), while the posterior probability of transitions from livestock to wildlife was remarkably high (0.99). Microbial genome-wide association analysis did not evidence genome rearrangements nor biomarkers suggesting S. aureus niche adaptation to wildlife, thus supporting recent spill overs. Altogether, our findings indicate that S. aureus isolates collected in the past years from wildlife most likely represent multiple introduction events from livestock. The clonal origin of CC398 and its potential to disseminate and evolve through different animal host species are highlighted, calling for management practices at the livestock-wildlife axis to improve biosecurity and thus restrict S. aureus transmission and niche expansion along gradients of human influence.

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.

Transmission planning in an imperfectly competitive power sector with environmental externalities

Policymakers face the challenge of integrating intermittent output from variable renewable energy (VRE). Even in a well-functioning power sector with flexible generation, producers’ incentives may not align with society’s welfare-maximisation objective. At the same time, political pressure can obstruct policymakers from pricing damage from CO2 emissions according to its social costs. In facilitating decarbonisation, transmission planning will have to adapt to such economic and environmental distortions. Using a Stackelberg model of the Nordic power sector, we find that a first-best transmission-expansion plan involves better resource sharing between zones, which actually reduces the need for some VRE adoption. Next, we allow for departures from perfect competition and identify an extended transmission-expansion plan under market power by nuclear plants. By contrast, temporal arbitrage by hydro reservoirs does not necessitate transmission expansion beyond that of perfect competition because it incentivises sufficient VRE adoption using existing lines. Meanwhile, incomplete CO2 pricing under perfect competition requires a transmission plan that matches hydro-rich zones with sites for VRE adoption. However, since incomplete CO2 pricing leaves fossil-fuelled generation economically viable, it reduces the leverage of strategic producers, thereby catalysing less (more) extensive transmission expansion under market power by nuclear (hydro) plants.

Transmission expansion planning based on Pareto dominance considering load variation and wind power

Transmission expansion planning based on Pareto dominance considering load variation and wind power

Multistage AC transmission expansion planning including fault current‐limiting high‐temperature superconducting cables and multiple distributed generations to improve short‐circuit level and grid‐scale flexibility

AbstractThis paper proposes a new multistage AC model for transmission expansion planning that finds an optimal combination of transmission lines, fault current‐limiting high‐temperature superconducting cables, and multiple distributed generations (DGs). On this basis, the proposed model, from a new perspective, allows for simultaneous improvement of the short‐circuit level and grid‐scale flexibility (GFLX) under both normal and fault conditions. The objective function to be minimized includes not only the net present worth of the total investment and operation costs but also the congestion‐induced GFLX degradation measure. This model also takes the AC power balance and flow relationships, equipment capacity limits, nodal voltage bounds, DG penetration level limit, as well as discrete logical and financial restrictions together into account with the short‐circuit level constraint. To overcome the complexity of solving the resultant non‐convex mixed‐integer non‐linear optimization problem, a multi‐objective integer‐coded melody search algorithm is employed, followed by a fuzzy satisfying decision‐making mechanism to obtain the final optimal solution. The exhaustive case studies conducted on the IEEE 24‐ and 118‐bus test systems verify the efficacy of the newly developed model in terms of cost‐effectiveness, flexibility, and short‐circuit level suppression when facing different normal and fault conditions.

Smart Transmission Expansion Planning Based on the System Requirements: A Comparative Study with Unconventional Lines

Smart Transmission Expansion Planning Based on the System Requirements: A Comparative Study with Unconventional Lines

Multi-objectives transmission expansion planning considering energy storage systems and high penetration of renewables and electric vehicles under uncertain conditions

Transmission expansion planning (TEP) integrating electric vehicles (EVs) and renewable energy sources (RESs) is pivotal for the transition toward cleaner and more sustainable energy systems. One of the biggest challenges in TEP with EVs and RESs is the uncertainty inherent in their behaviors. Managing this uncertainty is critical for ensuring the grid's reliability and resilience, and for facilitating a seamless transition to EVs. A potent method for addressing uncertainties in power systems is the application of a scenario-based approach. However, the efficiency of this approach is contingent upon an increase in the number of representative scenarios, which, in turn, escalates computational time, making solving the TEP problem a crucial task. To tackle these challenges, this paper proposes a multi-objective resilience model for TEP, to advance towards more sustainable energy systems. The model seeks to strike a balance between economic, environmental, and technical considerations by minimizing planning costs, carbon dioxide emissions, and enhancing the voltage profile. Furthermore, the paper proposes a cascaded intelligent strategy to handle uncertainties and reduce computational complexity by reducing the number of representative scenarios without compromising system reliability. The adaptive neuro-inference system is designed to manage long-term uncertainties, while two fuzzy systems are developed to address short-term uncertainties. The problem is formulated as a complex non-linear multi-objective optimization problem. To address this, a hybrid approach combining the mountain gazelle optimizer and the multi-objective salp swarm optimizer is developed to solve the problem. The efficacy of the proposed method is validated on two separate test systems, demonstrating its superiority in maintaining system reliability while reducing computing time by at least 92 % in comparison with the considered traditional methods. The results also highlight the superior performance of the proposed hybrid algorithm compared to existing meta-heuristic algorithms in solving the TEP.

Transmission Expansion Planning Considering Storage, Flexible AC Transmission System, Losses, and Contingencies to Integrate Wind Power

To meet future load projection with the integration of renewable sources, the transmission system must be planned optimally. Thus, this paper introduces a comparative analysis and comprehensive methodology for transmission expansion planning (TEP), incorporating the combined effects of wind power, losses, N-1 contingency, a FACTS, and storage in a flexible environment. Specifically, the optimal placement of the FACTS, known as series capacitive compensation (SCC) devices, is used. The intraday constraints associated with wind power and energy storage are represented by the methodology of typical days jointly with the load scenarios light, heavy, and medium. The TEP problem is formulated as a mixed-integer nonlinear programming (MINLP) problem through a DC model and is solved using a specialized genetic algorithm. This algorithm is also used to determine the optimal placement of SCC devices and storage systems in expansion planning. The proposed methodology is then used to perform a comparison of the effect of the different technologies on the robustness and cost of the final solution of the TEP problem. Three test systems were used to perform the comparative analyses, namely the Garver system, the IEEE-24 system, and a real-world Colombian power system of 93 buses. The results indicate that energy storage and SCC devices lead to a decrease in transmission requirements and overall investment, enabling the effective integration of wind farms.

Transmission Expansion Planning Considering Storage and Intraday Time Constraints to Integrate Wind Power

Transmission Expansion Planning Considering Storage and Intraday Time Constraints to Integrate Wind Power