Expansion Planning Research Articles

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

Identifying global conservation priorities for terrestrial vertebrates based on multiple dimensions of biodiversity.

The Kunming-Montreal Global Biodiversity Framework of the Convention on Biological Diversity calls for an expansion of the current protected areas (PAs) to cover at least 30% of global land and water areas by 2030 (i.e., the 30×30 target). Efficient spatial planning for PA expansion is an urgent need for global conservation practice. A spatial prioritization framework considering multiple dimensions of biodiversity is critical for improving the efficiency of the spatial planning of PAs, yet it remains a challenge. We developed an index for the identification of priority areas based on functionally rare, evolutionarily distinct, and globally endangered species (FREDGE) and applied it to 21,536 terrestrial vertebrates. We determined species distributions, conservation status (global endangerment), molecular phylogenies (evolutionary distinctiveness), and life-history traits (functional rarity). Madagascar, Central America, and the Andes were of high priority for the conservation of multiple dimensions of terrestrial vertebrate biodiversity. However, 68.8% of grid cells in these priority areas had <17% of their area covered by PAs, and these priority areas were under intense anthropogenic and climate change threats. These results highlight the difficulties of conserving multiple dimensions of biodiversity. Our global analyses of the geographical patterns of multiple dimensions of terrestrial vertebrate biodiversity demonstrate the insufficiency of the conservation of different biodiversity dimensions, and our index, based on multiple dimensions of biodiversity, provides a useful tool for guiding future spatial prioritization of PA expansion to achieve the 30×30 target under serious pressures.

Social conflict in Pati: Identifying causes and analyzing dynamics

This section discusses a social conflict that emerged in Pati, Central Java, Indonesia, sparked by the expansion plans of cement factories in the region. The conflict revolved around contrasting perspectives on environmental preservation, social harmony, economic interests, and sustainability. The clash of values led to protests, legal battles, and a deep-rooted conflict that persisted for years. The analysis explores the factors contributing to the conflict, the roles of social institutions in conflict resolution, and the broader implications for sustainable development and community engagement. The section also emphasizes the importance of understanding and addressing social conflicts in the context of contemporary state concepts and community dynamics.

Coordinated expansion planning of coupled power and transportation networks considering dynamic network equilibrium

As electric vehicles (EVs) rapidly proliferate, they intensify the demands on coupled power and transportation networks (CPTNs), leading to operational challenges such as congestion and overload. To address these challenges and enhance the dynamic performance of CPTNs, this paper proposes a coordinated expansion planning model based on a dynamic modeling method to support the upgrading of CPTNs (in distribution lines, distributed generators, chargers and roads). The spatial–temporal evolution and interaction of power flow in power distribution systems and traffic flow in transportation networks is incorporated into a dynamic network equilibrium, revealing the impact of network expansion on system performance. Uniquely, it replaces static link models with a novel point queue model to better track queuing and charging dynamics at fast charging stations. A scenario-based bi-level stochastic optimization model is formulated to determine the optimal coordinated expansion strategies, considering the diversity of electric and traffic demand scenarios. The optimization problem is solved using a designed marginal-cost-based particle swarm optimization algorithm. Case studies demonstrate the model’s effectiveness in alleviating 95.89% traffic congestion and reducing 4.75% low voltage risk and 28.86% overload risk, marking a significant advancement in CPTN management.

A Test System for Transmission Expansion Planning Studies

This paper introduces a 17-bus 500 kV test system intended for transmission expansion planning (TEP) studies. The overhead lines used in the system are based on an actual 500 kV transmission line geometry. Although several test systems have been developed for various forms of power system analysis, few are specifically tailored for TEP studies at the transmission voltage level, as opposed to the distribution voltage level. Current test systems for TEP studies are limited to single loading conditions only for normal operating conditions, and the majority of these systems are intertwined with issues related to the energy market or devised specifically for integrating new generations and loads into the existing power systems. However, ensuring a test system satisfies both voltage drop and line loading criteria during both normal and all single contingency operations is crucial in TEP studies, and addressing these issues under contingency conditions poses notable challenges. Moreover, practical TEP scenarios involve varied loadings, including peak load and dominant loading (60% of peak load) scenarios, while the existing test systems are configured solely for single loading conditions. To address these technical gaps, this paper introduces the 17-bus test system operating at a transmission voltage level of 500 kV, meeting technical requirements under normal and all single contingency operations for both peak load and dominant load scenarios. Detailed specifications of the proposed test system and load flow analysis at both normal and contingency conditions for different loading conditions are presented. This test system serves as an invaluable resource for TEP studies.

Empowering Entrepreneurship: Strategies and Challenges Faced by Saudi Women in Diverse Business Sectors

Purpose: The primary objective of this research is to investigate the strategies employed by female entrepreneurs in Saudi Arabia to overcome the challenges associated with running successful businesses across various sectors. Theoretical Framework: The literature reviews shows that the entrepreneurial endeavors of Saudi women appear to be dynamic and demanding, showcasing their resilience in choosing entrepreneurship as a career path. Design/Methodology/Approach: To conduct the analysis, a combination of qualitative and quantitative methods was utilized. Both primary and secondary data were gathered from women entrepreneurs in different states of Saudi Arabia. In-depth interviews were conducted with 100 female entrepreneurs involved in various small-scale business activities, such as apparel, fragrances, coffee shops, and boutiques. The major findings of the study is that majority of women (almost all 100 sample) taken for study have plans for future business expansion. Findings: The Saudi women also receive a lot of Government support for their business operations. It was also found that majority of Saudi women have invested more than 200000SAR initially. However, it is important to note the limitations of this research. The findings may not be readily generalizable beyond the selected sample. Research/Practical and Social Implications: The success of the Saudi Vision 2030 plan is believed to depend on a higher percentage of women in the labor force, and this objective has been achieved ten years ahead of schedule as more women are joining the workforce in recent years. Originality/Value: The article highlights the value of women's participation in business for the improvement of society at large. The study sheds light on the often-overlooked role of Saudi women as small business owners.

A Multiple Objective Decision Analysis Model for Capacity Expansion Plans Selection in Manufacturing

Capacity expansion is a critical decision with potential circumstances of risks and opportunities. This decision is further complicated given the conflicting decision measures and stakeholders’ preferences. Previous research is limited in providing a systematic framework to deal with such decisions in manufacturing environments. In this paper, decision analysis is used for the assessment of the appropriate capacity expansion plans of a mechanical contracting and fabrication company serving aluminum smelting industry. Repairing anodes used in aluminum smelting is a critical work that is outsourced to qualified companies. The increased demand for this kind of repair creates an opportunity for the contracting company to expand capacity. The expansion will qualify the company to receive more orders for anode maintenance and other mechanical services and respond more effectively to client demands, allowing for the advancement of expertise through the acquisition of large projects. However, the decision to expand may be a complex problem for a number of reasons. One of the reasons is ascribed to the appropriate form and the time required for expansion. Factors such as market rivalry, investment risk, budget constraints, bidding policies, and operational capabilities all complicate the decision making process, necessitating the use of a decision analysis model. Therefore, this article proposes the multiple objective decision analysis (MODA) approach for studying capacity expansion problem. MODA's cost-value model examines expansion plans within acceptable budgets and allows for trade-offs between value and cost, whilst the value gap analysis identifies opportunities to improve the values of the expansion plans. The sensitivity analysis demonstrates the model's robustness to changes in swing weights and value curves. The proposed model provides manufacturers with further investigation of the alternative expansion plans with insights into the associated cost and gaps for improvements.

Stochastic programming based coordinated expansion planning of generation, transmission, demand side resources, and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies, the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems. A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations. Multiple types of system components, including demand response service entities, converter stations, DC transmission systems, cascade hydropower stations, and other traditional components, have been extensively modeled. Moreover, energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence. Demand-response service entities from the load side are used to reduce and move the demand during peak load periods. The uncertainties in wind, solar energy, and loads were simulated using stochastic programming. Finally, the effectiveness of the proposed model is verified through numerical simulations.

Unveiling the transforming landscape: exploring patterns and drivers of land use/land cover change in Dar es Salaam Metropolitan City, Tanzania

ABSTRACT This study employs Landsat images from 1995, 2009, and 2022, utilizing Google Earth Engine and Random Forest algorithm in R software for land use and land cover change analysis in Dar es Salaam Metropolitan City. Results show a substantial shift, notably in bushland and forest, with a 14.57% and 2.9% decline, respectively. Drivers of change include urban (14.87%) and agricultural (4.47%) growth. Overall, 64.3% of land cover changed, primarily transitioning from bushland to agriculture (25.7%) and forest to agriculture (9.2%). Qualitative insights underscore unregulated urban expansion, informal settlements, migration, human activities, and inadequate planning as significant contributors, aiding sustainable urban governance.

Climate-Adaptive Transmission Network Expansion Planning Considering Evolutions of Resources

Weather-sensitive resources are the main source of uncertainties in power systems. However, the unpredictable climate change further introduces ambiguity (i.e., unknown probability distribution) into the system, since the weather-sensitive resources would evolve with the climate and gradually exhibit a different probability distribution from the past in an uncertain manner. Lack of considering this climate-induced ambiguity in transmission network expansion planning (TNEP) may cause misunderstanding of future operational scenarios. Aiming at a higher security operation level under climate change yet less line investment, this paper proposes a climate-adaptive TNEP, which is essentially a robust TNEP equipped with a climate-adaptive uncertainty set (CUS) to embody injections from the weather-sensitive resources that have high probabilities in the target year despite the climate-induced ambiguity. Determination of the CUS involves three steps. First, model future unknown distribution under climate change. Specifically, the climate-driven evolution in distributions is quantified by an evolutionary distance between historical and future true distributions, whose upper bound is derived from practical data, while the future unknown distribution is then modeled by a distance-based ambiguity set; Second, determine the CUS which has a minimal volume yet a desired confidence level in the face of the ambiguous future distribution. To that end, a parametric Wasserstein distance-based distributionally robust optimization ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -WDRO) is developed over the ambiguity set; Third, solve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -WDRO by a data-clustering-incorporated reformulation. After the CUS is determined, the overall climate-adaptive TNEP is solved by a column-and-constraint-generation method with an inner multi-loop algorithm tailored for the CUS. Simulations are conducted on three test systems with data from Australia and Coupled Model Intercomparison Project Phase 6 (CMIP6) under scenarios issued by Intergovernmental Panel on Climate Change (IPCC), which demonstrate that the climate-adaptive TNEP can improve operational security under climate change while reducing investment costs.

Low-carbon coordinated expansion planning of carbon capture storage and energy storage systems with VCG-based demand response mechanism

Recently, the decarbonization of the power industry has gained increasing attention as global warming problem has become more acute. However, the existing system planning models targeting to tackle emission reduction rarely consider to improve the flexibility of power system. In this paper, a co-planning model considering the expansion of carbon capture and storage (CCS) units and energy storage systems (ESSs) is proposed, in which the carbon emission target is embedded in the objective function via setting different carbon emission tax. Here, the system flexibility is improved by Source-Load-Storage (SLS) response, which is achieved via CCS units, ESSs and flexible load. In addition, a subsidy method for cost recovery based on Vickrey-Clark-Groves (VCG) mechanism is proposed to stimulate market members participating in SLS response. Numerical results from a modified IEEE 118-bus power system illustrate the impact on system cost and emission results owing to specific carbon emission tax, investment budget and SLS response. Finally, we display the contribution rate and subsidy of different participants in a typical day.

BLYNK RFID and Retinal Lock Access System

The BLYNK RFID AND RETINAL LOCKACCESS SYSTEM describes a digital door lock system that uses an ESP32-CAM module, which is a budget friendly development board with a very small size camera and a micro-SD card slot. The system uses retinal recognition technology to detect the retinal of the person who wants to access the door. The AI-Thinker ESP32-CAM module takes pictures of the person and sends them to the owner via the BLYNK application installed on their mobile phone. The owner can then grant permission to access the door based on the person’s identity. When deploying your BLYNK RFID and retinal scanner project, it's important to consider scalability and maintenance. As your user base and access requirements may change over time, plan for future expansion and updates. Regularly review and update your system's firmware, libraries, and security measures to stay ahead of potential vulnerabilities and evolving best practices in access control. Monitoring and auditing your system's usage is crucial. The Blynk platform can help you gather data on access attempts and system performance, allowing you to analyze the data for any anomalies and potential security breaches. This data can be valuable for compliance, troubleshooting, and performance optimization. Key Word: retinal and RFID scanning for lock to authentic users, using an ESP32-CAM and RFID reader controlling through BLYNK.

Long-Term Optimal Generation Expansion Planing considering CO2 Reduction Policies and Mechanisms

In a deregulated power system, a well-designed market mechanism could promote the optimal distribution of resources within the system. As one of the major sources of CO2 emissions, power industry can be seriously impacted by carbon emission related policies and regulations. This paper considers three widely promoted emission policies, using a long-term power system planning model to obtain the optimal generation expansion plan for twenty years. By analysing and comparing system investment schemes under different emission-related mechanisms, the processes of how these policies influence generation investment are revealed. The effectiveness of these policies is compared to determine the optimal carbon emission mechanism.

Promoting Emergency Medical Service Infrastructure Equality to Reduce Road Crash Fatalities

Timely emergency medical service (EMS) is critical to mitigate outcomes of severe traffic crashes, especially in rural areas. The urban–rural inequality of the EMS infrastructure has been one of the factors contributing to a higher death rate for patients in rural road crashes. To address the spatial imbalance of EMS infrastructure, this study aimed to provide a methodological framework for evaluating the existing EMS infrastructure by taking South Korea’s EMS infrastructure expansion plans as the case study. Specifically, this study developed a road crash data-driven methodology to promote spatial balance and economical expansion of EMS infrastructure of all types, including EMS stations, hospitals, the helicopter fleet, and landing spots. Geographically weighted binary logit regression and spatial analysis identified strategic locations for prioritizing the EMS infrastructure expansion using crash victim data and road networks to close the gap between urban and rural areas. The analysis of access to existing EMS infrastructure showed that the 16 to 20 min EMS response time, including on-scene time and transport time ranging from 11 to 15 min, are significantly associated with higher crash fatalities. The results also suggested that EMS stations and heliports are inadequate to meet the EMS time thresholds in the central province of Korea. The findings of this research could inform policymakers as they are working toward expanding the EMS infrastructure and creating a more equitable EMS response when it comes to transporting rural road crash patients.

Supply security and the reliability of the Spanish generator system in the next two decades based on the costs of fossil fuels and nuclear option.

The supply security of the primary energy sources (coal, natural gas, enriched uranium…) and the reliability of the system in terms of the oscillations of the frequency and voltage are two aspects to assess in the Generation Expansion Planning. In this article, for the first criterion, the evolution of vulnerability and degree of independence of the generator system with respect to primary energy sources has been assessed by the indexes of Shannon-Weiner and energy independence, respectively. For the second approach, the evolution of the reserve margin by means the coverage index and the progression of the inertia and regulation capacity by means of its percentage regarding the total installed capacity. These four indexes have been applied on the obtained results of a stochastic lineal model developed with the software GAMS. In this model the nuclear technology has been considered as generation option in several scenarios of costs of the fossil fuels, coal and natural gas. Also, two of clean coal technologies with greater possibility of putting into service have been taken into account. The final conclusion would be that each technology carries out several functions in the generator system but not all them. That is, a generator system should have the greatest possible number of types of generation to be reliable and have supply security.

Study protocol: infectious diseases consortium (I3D) for study on integrated and innovative approaches for management of respiratory infections: respiratory infections research and outcome study (RESPIRO)

BackgroundCommunity-acquired respiratory infections are a leading cause of illness and death globally. The aetiologies of community-acquired pneumonia remain poorly defined. The RESPIRO study is an ongoing prospective observational cohort study aimed at developing pragmatic logistical and analytic platforms to accurately identify the causes of moderate-to-severe community-acquired pneumonia in adults and understand the factors influencing disease caused by individual pathogens. The study is currently underway in Singapore and has plans for expansion into the broader region.MethodsRESPIRO is being conducted at three major tertiary hospitals in Singapore. Adults hospitalised with acute community-acquired pneumonia or lower respiratory tract infections, based on established clinical, laboratory and radiological criteria, will be recruited. Over the course of the illness, clinical data and biological samples will be collected longitudinally and stored in a biorepository for future analysis.DiscussionThe RESPIRO study is designed to be hypothesis generating, complementary to and easily integrated with other research projects and clinical trials. The detailed clinical database and biorepository will yield insights into the epidemiology and outcomes of community-acquired lower respiratory tract infections in Singapore and the surrounding region and offers the opportunity to deeply characterise the microbiology and immunopathology of community-acquired pneumonia.

2022 Russian-Ukrainian War and the Future of EU Energy and Transport Security: Analyzing the Implications of Diversifification the Transport and Energy Transit Routes – the Case of the Black Sea/Caspian Region

Given recent events, such as the full invasion of Ukraine’s sovereign territories by the Russian armyin 2022, this article critically analyzes how the use of the Caucasus-Caspian region could serve Brusselsas an alternative means of diversifying energy sources to ensure its energy security. The study iscomprehensive and covers various forms of energy projects. Particular attention is paid to exploringthe various phases regarding Shah Deniz and potential expansion plans. In addition, it carefully examinesall other important factors associated with the production, transportation, and explorationof energy resources in the Caspian reserves.In addition, a key task is to classify and compare the EU’s geopolitical priorities before and after the2022 war, present an analysis of the complex paths of transformation of the EU’s energy supply,and clarify its new policies regarding the relations between the main regional players: the RussianFederation, the EU, Turkey, Azerbaijan, and Georgia.It has been established that the Black Sea-Caspian steppe contains valuable energy reserves. Dueto the critical geopolitical, geostrategic, and geoeconomic importance of the region, the EU has aninterest in the region. The study provides a comparative overview of different aspects of EU energydiversification policies. It examines how these policies have influenced the approach of foreignstates to the Caucasus-Caspian region, taking into account recent events. Moreover, it considersnational goals and takes into account the important interests of leading powers.Keywords: energy security, diversification of fossil fuels, Caucasus-Caspian region, Black Sea-Caspian steppe, EU policy on energy diversification, energy projects.

Integrated energy demand-supply modeling for low-carbon neighborhood planning

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as “shoeboxes” using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.

Hydrothermal simulations in Brazil using batteries - First Results

The share of renewable energy sources has increased worldwide, especially those considered unconventional (i.e. without considering hydropower). Besides proved as economically competitive, these sources cause less environmental impacts when compared to other sources of traditional power generation such as coal and oil. However, solar and wind power plants are intermittent, i.e., the generation varies according to the availability of the respective natural resources and therefore are considered non-dispatchable by the System Operator (SO). Because of that, the electrical systems must be able to somehow compensate the variability from the non-dispatchable energy sources to meet the supply-demand equilibrium. Storage technologies such as electrochemical (e.g. batteries) and electromechanical (e.g. flywheel) could be used in that sense. Recent developments of these technologies opened space for several ancillary services and products to be offered by the storage systems to the electrical system. In addition to that, the sustained reduction in operation and maintenance (O&amp;M) costs summed to the payments for providing ancillary services could make some of these technologies economically attractive, under certain conditions. Given this new scenario, the representation of these storage systems in the expansion planning and power systems mathematical models becomes necessary. In this study, we simulated the Brazilian electrical system at 2035, considering batteries (corresponding to 7% of the installed capacity) and a 30% share of unconventional renewable energy in the system generation capacity.

Charging management of electric vehicles with the presence of renewable resources

Considering the increasing use of electric vehicles, the establishment of charging stations to exchange power between the grid and electric devices, and the integration of charging stations with solar power generation sources, the optimal use of electric vehicle charging stations in the power system. The purpose of cost reduction in the presence of the intelligent environment is a challenge that must be investigated so that this platform is suitable for predicting the behaviour of vehicles and, as a result, optimizing their presence in the power network. This research presents a relatively complete radial distribution network development planning model in two scenarios. In the first scenario, the effects of electric vehicles are not considered, and only the effects of distributed production (renewable and dispatchable) are considered. Studies have been done on a sample 54-bus network, a common system in most Distribution expansion planning (DEP) articles for distribution networks. In addition, the real data of American highways have been used to create raw input data. Also, due to the distance limit, the information on vehicles under 100 miles has been received as electric vehicle information. The clustering method and Capiola multivariate probability distribution functions have created suitable vehicle scenarios during different planning years. Capiola’s method increases the accuracy of vehicle load forecasting according to a predetermined growth rate. The DEP problem in this research is modeled as an optimization problem based on scenario, dynamic, and in 5 one-year time frames (5-year time horizon and one-year accuracy). The results indicate that, in the presence of electric vehicles and distributed production sources, the technical characteristics of the network are improved.Similarly, the use of DGs, in addition to reducing the cost of equipment, has reduced undistributed energy in the system. But 10,000 vehicles, which have been applied to the network as an uncontrolled load, have caused an increase in undistributed energy. The cost of equipment required for the network development is almost as much as 5%.