Expansion Planning Research Articles

The Greek-Turkish Rivalry: A Bayesian VAR Approach

ABSTRACT The persistent Greek-Turkish rivalry has garnered considerable attention among defense economists, prompting an exploration of whether these two nations are locked in an arms race. The longstanding divisions between them are deeply rooted in historical conflict and mutual distrust, and diplomatic efforts aimed at reconciliation have proven largely ineffective, with brief rapprochements typically ending swiftly. Recent discoveries of natural gas in the eastern Mediterranean have exacerbated tensions, leading to military expansion plans that have heightened the risk of escalation. Given the lack of consensus in empirical studies on the relationship between their military expenditures, we employ a linear Bayesian Vector Autoregression (BVAR) modeling approach to explore potential interdependencies among four distinct proxies of the states’ physical arms build-up. Utilizing an annual dataset running from 1960 to 2020, our findings reveal that shocks to one nation’s military expenditure do not significantly affect the opponent’s defense spending. This suggests that contrary to strategies focused on the costly maintenance of strategic balance, these rivals should prioritize cooperation to jointly foster peace and economic development across the broader Mediterranean region.

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.

STRATEGIC SELECTION OF GREEN TECHNOLOGIES FOR SUSTAINABLE DEVELOPMENT IN KAZAKHSTAN: A COMPREHENSIVE ANALYSIS OF THE GREEN BUILDINGS CONCEPT FOR THE CITY OF TURKESTAN

Explores the importance of adopting green technology in Kazakhstan for energy efficiency and operational sustainability in the business sector, in response to the growing global trend towards environmentally conscious practices. This research aims to analyse the movement towards green technology and its important role in promoting sustainable development in modern economies. The inquiry focuses on the conceptualization and implementation of green buildings, recognizing them as environmentally friendly constructions and commercially important assets that enhance sustainable business development. By analysing worldwide data, we explore the wider consequences of embracing green building principles, highlighting their ability to create economic benefits and encourage sustainable behaviours. Turkestan, with its potential for significant development as a prominent tourist destination, was emphasised as an important and advantageous city for incorporating green building principles into the expansion plans. Our analysis highlights the strategic alignment between Turkestan's development initiatives and the adoption of green buildings in the city. Turkestan's environmentally sustainable architectural designs, together with the integration of cultural, historical, natural, and modern components, make it a model for sustainable urban development and increase its attractiveness to foreign tourists. The diverse advantages that green buildings provide to different stakeholders, such as government agencies, enterprises, hotels, and tourist destinations, have been examined. Meanwhile, the attractiveness of the city and the practical benefits of green architecture concepts to appeal to foreign tourists highlight a unique experience that blends architectural innovation with the historic legacy of Turkestan. Research recommends strategically choosing green technology, namely integrating green building principles, to enhance Turkestan's status as a premier tourist destination in Kazakhstan.

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.

Rational capacity investment for renewable hydrogen-based steelmaking systems: A multi-stage expansion planning strategy

To improve energy efficiency and mitigate environmental impact, the iron and steel industry (ISI) is responsible for implementing a low-carbon transition. The renewable hydrogen-based steelmaking system (RHSS) has emerged as an innovative energy system in the pathway of reducing the carbon footprint of ISI. Nonetheless, the sustainable development requirements of RHSS emphasize the critical need for well-planned capacity investments to boost its economic viability. This study introduces a novel planning strategy for RHSS referred to as a multi-stage expansion planning (MSEP) model, which is aimed to minimize the levelized cost of crude steel over the planning horizon from 2025 to 2050 (EU climate neutrality year). The MSEP model incorporates the construction sequence and dynamic techno-economic parameters to formulate investment strategies that adapt to changing system requirements and external conditions over the planning horizon. To address the complexity of the MSEP model, a rolling-horizon approach (RHA) is proposed to solve the model sequentially while integrating updated information. The proposed approach is benchmarked against both the single-stage approach and the perfect-sight approach through case studies. The numerical results show that the proposed RHA (i) guarantees a rational capacity investment strategy for RHSS considering the construction sequence and dynamic techno-economic parameters over multiple planning stages; (ii) achieves a reduction in the levelized cost of crude steel by approximately 50% relative to the single-stage approach; (iii) resolves the convergence issues encountered with the perfect-sight approach while achieving an optimal solution in a reasonable time frame. Additionally, sensitivity analysis evaluates the uncertainties of multiple factors on the planning results, while the robustness analysis confirms the applicability and adaptability of RHA across various scenarios.

The time-advance effect of China's rooftop solar photovoltaics program on the dual carbon targets and its implication on the globe

Rooftop solar photovoltaics (RSPV) plays an important role in energy transition and climate goals. However, the contribution of RSPV to the dual carbon targets (DCTs) has not yet been quantitatively investigated at the national or global scale. Here, we investigate this contribution with an improved Stochastic Impacts by Regression on the Population, Affluence, and Technology (STIRPAT) model. We find out the time-advance effect of China's pilot RSPV program, i.e., doubling expansion of the current pilot area helps that the DCTs will be achieved 5 years ahead of schedule; a full expansion plan from merely six identified key provinces can be enough to guarantee that China will achieve carbon peak and carbon neutrality 5 and 4 years ahead of their respective time schedules. This effect implies global RSPV development; about 25 % of the world's buildable rooftops for RSPV installation will help reduce 1.4 billion tons of carbon emissions per year and thus achieve global carbon neutrality by 2050. The results will attract a widespread new energy community interested in expanding RSPV and helping governments formulate carbon reduction policies.

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.

Green hydrogen exports in New Zealand and Chile can improve electricity supply security if configured as local energy insurance

Extreme weather events, for example, prolonged droughts, are increasingly stressing electricity systems and threatening the achievement of energy transition goals. At the same time, countries such as Chile and New Zealand are developing strategies to export renewable energy through green hydrogen. This paper economically evaluates the use of future green hydrogen exports as strategic storage under the logic of energy insurance, based on the assumption that Chile and New Zealand will adopt a hydrogen economy. Our first contribution is to compare the marginal costs of re-electrified green hydrogen, for different scenarios, to the value of lost load in New Zealand and Chile. We then determine the marginal cost of energy produced from green hydrogen based on projected production, transportation, and re-electrification costs for the year 2030. Finally, we estimate the eventual penalties that exporters would incur in the case of breaking their contracts and the overall resulting system costs of using hydrogen for system security. We found that re-electrifying hydrogen can be competitive with conventional technologies at 3 USD/kg. At 1.5 USD/kg, hydrogen is more competitive than fossil fuel-based technologies, for both New Zealand and Chile. Thus, in 2030, it could make economic sense for New Zealand and Chile to use hydrogen as a strategic reserve. The system cost of the proposed insurance scheme in 2030 ranges from 0.53 to 1.76 USD/MWh, which is small compared to total electricity costs. These values can be used for power system expansion and operation planning.

Mental COP: Mental Health Cohort Of Police officers in Korea

PurposeThis cohort study addresses the need for comprehensive and longitudinal research on police officers’ mental health, focusing on the relationship between occupational factors, individual characteristics and mental health status or...

A generation expansion planning method considering conditional value-at-risk

Abstract The uncertainty of load forecasting can lead to certain risks in generation expansion planning. Different from the traditional large-capacity unit planning in the past, renewable energy has the characteristics of small single-machine capacity and fast construction speed, etc. The uncertainty of load forecasting will have a more significant impact on the results of generation expansion planning including renewable energy. For this reason, this paper proposes a generation expansion planning idea considering load forecast uncertainty based on conditional value-at-risk (CVaR). Firstly, scenario technology is used to simulate the uncertainty, and CVaR is introduced to quantify the risk of generation expansion planning. Then, considering the coordinated development of traditional and renewable energy sources, a generation expansion planning model that considers risk is established to minimize the total costs. Finally, the effectiveness of the model was verified through a case study, and this model has reference significance for balancing the economy and reliability in generation expansion planning.

A sea of birds: first bird population assessments in the Saudi Arabian Red Sea

IntroductionSeabirds and other insular birds are an important part of marine ecosystems and are increasingly threatened worldwide. Phenology, abundance, distribution, and breeding success are important baseline parameters needed to evaluate population trends and dynamics, identify biodiversity hotspots and potential breeding sites, and assess habitat selection. In the Red Sea basin, reliable and complete data on birds are lacking for islands in most of the Important Bird Areas (IBA). Such data are now especially important since development projects have started at several of these locations. Here, we assess the distribution, abundance, phenology, and population trends of 13 bird species that breed in the Duba Islands, Al Wajh Bank, and Al Lith Islands. Breeding success and habitat selection for six species were also assessed.MethodsBetween December 2020 and August 2023, more than 90 islands were visited multiple times with different frequencies. All nests were counted, and the area occupied by the different colonies was georeferenced. Breeding success was assessed by visiting selected colonies multiple times until the chicks were ready to fledge. Habitat selection was determined by analyzing the habitat found in the georeferenced colonies.ResultsMore than 25,000 nests were counted each year, mainly in the summer, on more than 75 islands. Of the 13 species studied, the most abundant and widespread species was the White-cheeked tern, and the second most widespread was the Osprey.DiscussionWe estimate that the Al Wajh Bank hosts the following percentages of global breeding populations: Crab plover: 5%, Sooty gull: 17%–35%, White-eyed gull: 15%, Bridled tern: 1%, White-cheeked tern: 4%, and Lesser crested tern: 2%, making the area a regional and global hotspot for these species. Some of the islands occupied by breeding birds are slated for development for tourism activities as part of the Kingdom’s tourism expansion plans. At the same time, there are several ambitious conservation programs underway in these areas. To be effective, such programs require reliable and comprehensive data of the kind presented here.

A two-phase nonlinear optimization method for routing and sizing district heating systems

This paper presents a method to find the optimal topology, pipe sizing, and operational parameters of a district heating system under consideration of one design point. The current high costs of district heating systems set limits regarding the minimum heat demand density required for economic network expansions. Optimized routing with ideal pipe sizing and optimal operating parameters offers a potential for cost reduction. Therefore, this paper introduces a new two-phase method for district heating network expansion planning. This method consists of consecutive optimizations, starting with a mixed-integer linear programming followed by a nonlinear optimization. During the mixed-integer linear programming, the district heating system is optimized with continuous diameters, and the nonlinear pressure and temperature dependencies must be linearized. The resulting topology and the continuous diameters are afterward handed over to a nonlinear sparse sequential quadratic programming. The continuous diameters are discretized using a numerical continuation strategy that gradually forces the continuous diameter variables into discrete diameter choices. As a proof of concept, the district heating system for a small town with 400 consumers is optimized and analyzed. The two-phase optimization is performed in 251.68sec, and in most cases, discrete or near discrete diameters are achieved in a nonlinear continuous optimization.

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.

Composite reliability analysis of electric power systems considering climatic and spatial conditions using self-organizing map

This article presents an innovative approach by integrating different models that represent the failure rates of components related to weather events, considering the geographic location and its variation over time, to a customized Monte Carlo simulation model to ensure the draw synchronized with the load and generation curves, and then quantify the impact of these events through the power flow. The study focuses on using a neural network is trained to classify/filter system states in order to reduce the need for electrical studies and speed up the reliability assessment process. The approach is demonstrated with reference to the IEEE-RTS network test case. The results obtained indicate that the inclusion of renewable sources and climatic conditions have a significant impact on reliability indicators, as failure rates subject to these meteorological conditions tend to increase, providing a greater risk of simultaneous shutdowns, and contributing to the worsening of the system severity index. Therefore, the proposed model allows for a faster and more realistic reliability assessment, directing the planning of expansions and reinforcements and allowing risk monitoring during system operation.

Integrated optimization of multi-machine scheduling and berth allocation in automated ore terminals

As water transportation undergoes a new technological revolution, automated terminal technology has become a key issue in reducing costs and improving efficiency. However, the operation theory limitation makes it problematic for machines to cooperate effectively and operate profitably, which is notably salient at ore terminals with their unique structure. This study focuses on the integrated scheduling of a new automated ore terminal. It comprises three main processes: (a) berth allocation, (b) splitter scheduling, and (c) feeder scheduling. We attempt to tackle this issue by developing a mixed-integer linear programming (MILP) model to minimize the vessels’ maximum departure time. Considering the complexity of the MILP model, we propose a logic-based Benders decomposition algorithm to solve the efficiency problem. The results indicate that the algorithm can solve realistically sized problem instances. The main factors affecting the computation time are the congestion level, total cargo types, and vessel service time. The integrated scheduling methods can effectively solve the actual challenges faced by terminals and provide suggestions and references for terminal expansion planning.

A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

Monitoring urbanization processes is important because they are often accompanied by intensive landscape pattern transitions and pluralistic socioeconomic changes. To effectively monitor urban expansion and support regional planning, it is essential to develop a fast, accurate and universal urban–rural classification model, especially identifying the dynamic spatial patterns of urban, urban–rural fringe and rural areas. Although deep learning can effectively detect land cover changes, its applications in urban–rural identification have received little attention due to a lack of high-quality training datasets. In this study, we develop a novel transferable full-resolution convolutional neural network (FR-Net) to identify urban-fringe-rural areas. A large-scale training dataset was constructed using field surveys and aerial photography, and a data cube was stacked by multiple typical socio-natural indicators. We took the Beijing-Tianjin-Hebei (BTH) urban agglomeration region in China as a case study and identified spatiotemporal evolutions of urban-fringe-rural areas from 2000 to 2020. The results indicated that over the past two decades, the urban–rural fringe expanded outward with urban areas, and both areas gradually increased, with an inverted U-shaped growth rate. Accurate identification of these fringes can benefit regional urban–rural planning and social governance. Based on the identification results, complex socio-ecological impacts of urbanization could be further explored. Testing demonstrated that the developed FR-Net model has high accuracy and robustness. Our developed open-source FR-Net model exhibits transferability and can be applied to multi-scale urbanized areas.

Multi-period hydrogen supply chain planning for advancing hydrogen transition roadmaps

The global commitment to the Paris Agreement has triggered the development of national hydrogen strategies and roadmaps in numerous countries. For seamless execution of a hydrogen strategy, the model employed in roadmap optimization must consider intricate operational details. This study offers a comprehensive multi-period model for hydrogen supply chain (HSC) expansion planning, spanning production to distribution. The model optimizes facility timing, location, type, and capacity while considering efficient operational strategies. Hydrogen, sourced diversely, anchors the supply chain, supported by precise calculations for road-based transport and pipelines. Storage facilities adapt to supply sources and transportation methods, addressing supply-demand fluctuations and transport interruptions. Hydrogen refueling stations (HRS) are optimized for efficient urban distribution. Analyzing South Korea's HSC in this model reveals a shift towards green hydrogen in later stages, initially relying on refineries and imports. Investments transition from pipelines to liquid (LH2) and gaseous (GH2) hydrogen trucks in the early phases, eventually incorporating NH3 tanker trucks. The model predicts a decreasing levelized cost of hydrogen (LCOH), reaching $3.89/kg through HSC expansion planning. The model predicts a decreasing LCOH, reaching $3.89/kg through strategic HSC expansion. Transportation costs, playing a significant role in the final LCOH, are crucial for South Korea to achieve its target of 3000 KRW/kg. These results offer valuable insights for nations advancing hydrogen initiatives, providing a detailed cost breakdown model to guide policy planning and achieve HSC and LCOH objectives effectively. Also, the study showcases Korea's adeptness in transitioning to a hydrogen-based economy.

The Expansion of the Bureaucratic Structure and Financing Regional Development in Indonesia

This study is about an expansion as a goal used by local elites to expand the bureaucratic structure for politicizing the position of political support and obtaining central funding. The existence of soft requirements which tends to be determined by the local government makes the provisions and process of expansion only for the fulfillment of formal aspects without substance related to the urgency of expansion strategically and economically. This study used a mixed-method that emphasizes a meaningful approach to the situation faced in the expansion by administering a survey on communities’, government administrators, and stakeholders’ opinions in assessing the expansion success. In addition, in-depth interviews were conducted with community leaders, especially those involved in the initial expansion as well as prominent community leaders to find out the expansion plan achievements. This study found that the expansion carried out at the village and sub-district levels had a positive impact on community perception, increased financing, and development. The expansion has allowed each new region the opportunity to finance development through various schemes and the availability of positions for the bureaucracy to gain support for the political power of politicians.

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%.