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Gathering knowledge of supply curves in electricity markets is critical to both energy producers and regulators. Indeed, power producers strategically plan their generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market’s performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market’s specific features.Against this background, this article introduces a hierarchical clustering method based on a novel weighted-distance that is specially tailored to non-bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables. Additionally, the proposed distance is used in a learning procedure by which explanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered.

The aim of this study was to assess the Grade 10 learners' misconceptions of the supply curve in Market Dynamics graphs and finding ways to minimise them. This aspect of Market Dynamics tends to pose a challenge to many learners. Guided by the constructivist theoretical framework, this qualitative study followed a purposeful sampling method of 36 economics learners in the 10th grade. A test item of 15 questions was given to the learners using an observation method, but only five questions were used to generate the data, which was analysed thematically. The themes generated helped to group learners into three different categories (1 to 3) according to the level of misconceptions. One of our key findings was that most learners tend to overgeneralise economic concepts in a new setting. Another key finding was that learners demonstrated difficulties in using similar economic concepts and showed misconceptions about the terminologies that look similar in meaning. We also found that the traditional teaching method, especially in graphic presentations by the teacher, resulted in most misconceptions exhibited by the learners. This study contributes to the teaching and learning of graphs in economics in South Africa. More specifically, a framework was proposed to help minimise learners' misconceptions of the supply curve and perhaps other graphical concepts in Market Dynamics. The study further contributes to teachers' knowledge of the different areas where learners have misconceptions in understanding the supply curve graph as it prepares economics teachers to pay particular attention to those identified areas. The study concludes that learners indeed experience misconceptions in Market Dynamics supply curve graphs; however, those misconceptions can be minimised using a different approach to teaching. It is recommended that more instructional time should be allocated to the teaching of supply curve graphs and teachers could perhaps find alternative teaching strategies to help mitigate learners' misconceptions in those identified areas. [ABSTRACT FROM AUTHOR]

Renewable energy plays a crucial role in achieving net-zero emissions. As an island with a high population density, Taiwan has increasingly favored rooftop solar photovoltaic (PV) systems over ground-mounted installations due to land-use competition. However, illegal rooftop dwellings in urban areas pose significant obstacles to renewable energy deployment. This study proposes a market-based incentive scheme for rooftop solar adoption to encourage property owners to shift away from illegal rooftop dwellings. We construct market supply curves for illegal rooftop rental housing based on real-world rental data and conduct a techno-economic analysis to examine the required government subsidy. The results provide empirical evidence for an important nonlinearity in the supply curves, implying that linearity-based policy frameworks may not be effective in achieving optimal results. We also identify that opportunity costs linked to rental loss constitute a substantial portion of total costs, deterring the owners from adopting the shift. The findings indicate that the existing feed-in-tariff policy is inadequate to incentivize property owners to deploy rooftop solar panels. The proposed scheme embraces the three dimensions of sustainable development—economic, social, and environmental, which could benefit cities worldwide with illegal rooftop structures.

California state government has established a series of mandated targets for reducing the greenhouse gas (GHG) emissions that contribute to climate change. With a multiplicity of emissions sources and economic sectors, it is clear that no single change the state can make will enable it to achieve the ambitious goals set by executive orders and legislation. Instead, many actors within the state’s economy—including state agencies such as the California Department of Transportation (Caltrans)—must make multiple changes to their own internal operations. The focus of this study and technical memorandum is to examine several strategic options that Caltrans could adopt to lower its GHG emissions in operating the California (CA) state highway network and other transportation assets so it can help meet the state’s GHG reduction goals. Although many GHG reduction strategies appear to be attractive, simple, and effective, most also have limitations, trade-offs, and unintended consequences that cannot be identified without a preliminary identification and examination of the full system they operate in and their full life cycle. To achieve the most rapid and cost-effective changes possible, the costs, times to implement, and difficulty of implementation should also be considered when the alternative strategies are being prioritized. This project first developed an emissions reduction “supply curve” framework by using life cycle assessment (LCA) to evaluate full-system life cycle environmental impacts and life cycle cost analysis (LCCA) to prioritize the alternative GHG-reduction strategies based on benefit and cost. This framework was then applied to an example set of strategies and cases for Caltrans operations. This technical memorandum presents the results of the supply curve framework’s development and its application to six strategies for changing several Caltrans operations identified by the research team. The six strategies were: (1) pavement roughness and maintenance prioritization, (2) energy harvesting using piezoelectric technology, (3) automation of bridge tolling systems, (4) increased use of reclaimed asphalt pavement, (5) alternative fuel technologies for the Caltrans vehicle fleet, and (6) solar and wind energy production on state right-of-ways. A summary of the methodology and the resulting supply curve that includes all the strategies considered and ranked is published in a separate white paper. This technical memorandum provides the details, assumptions, calculation methods, and results of the development of the GHG reduction supply curve for each strategy. Although this current study’s scope is limited to development of a supply curve for GHG emissions only, there are plans to expand the study’s scope to include other environmental impacts and to develop supply curves for them as well.

South Africa's energy network is under severe pressure due to low supply and overwhelming demand. With an increase in renewable energy providers, specifically wind energy, knowing how the supply can satisfy the electricity demand may relieve apprehensions. This research aims to provide insight into the wind energy supply of South Africa and question how well this supply meets the demand of South Africa. The methodology used in this work highlights the importance of access to public datasets to dispel misconceptions in the energy industry. Additionally, the work supports network planning and the arguments for increasing wind energy penetration on the South African grid. Wind profiles and the typical energy production of South African wind farms are compared to electricity demand. The geographical spacing of the operational wind farms is considered. It is observed that wind energy supply assists in the peak electricity hourly demand as well as seasonal peaks. Furthermore, South Africa's coast is analysed to determine the offshore wind power potential, where shallow and deep waters are considered. It is observed that South Africa has a high potential for offshore wind, even after losses are applied. [ABSTRACT FROM AUTHOR]

Gathering knowledge of supply curves in electricity markets is critical to both energy producers and regulators. Indeed, power producers strategically plan their generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market's performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market's specific features. Against this background, this article introduces a hierarchical clustering method based on a novel weighted-distance that is specially tailored to non-bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables. Additionally, the proposed distance is used in a learning procedure by which explanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered. • We study the supply curves of several electricity markets in a five-year period. • We introduce a novel market-based distance to measure the curves' dissimilarity. • Clusters are provided with an insightful characterization of the resulting groups. • A distance-based learning procedure is proposed to forecast the supply curves. [ABSTRACT FROM AUTHOR]

South Africa’s energy network is under severe pressure due to low supply and overwhelming demand. With an increase in renewable energy providers, specifically wind energy, knowing how the supply can satisfy the electricity demand may relieve apprehensions. This research aims to provide insight into the wind energy supply of South Africa and question how well this supply meets the demand of South Africa. The methodology used in this work highlights the importance of access to public datasets to dispel misconceptions in the energy industry. Additionally, the work supports network planning and the arguments for increasing wind energy penetration on the South African grid. Wind profiles and the typical energy production of South African wind farms are compared to electricity demand. The geographical spacing of the operational wind farms is considered. It is observed that wind energy supply assists in the peak electricity hourly demand as well as seasonal peaks. Furthermore, South Africa’s coast is analysed to determine the offshore wind power potential, where shallow and deep waters are considered. It is observed that South Africa has a high potential for offshore wind, even after losses are applied.

Energy system planning tools suggest that the cost and feasibility of climate-stabilizing energy transitions are sensitive to the cost of CO2 capture and storage processes (CCS), but the representation of CO2 transportation and geologic storage in these tools is often simple or non-existent. We develop the capability of producing dynamic-reservoir-simulation-based geologic CO2 storage supply curves with the Sequestration of CO2 Tool (SCO2T) and use it with the ReEDS electric sector planning model to investigate the effects of CO2 transportation and geologic storage representation on energy system planning tool results. We use a locational case study of the Electric Reliability Council of Texas (ERCOT) region. Our results suggest that the cost of geologic CO2 storage may be as low as $3/tCO2 and that site-level assumptions may affect this cost by several dollars per tonne. At the grid level, the cost of geologic CO2 storage has generally smaller effects compared to other assumptions (e.g., natural gas price), but small variations in this cost can change results (e.g., capacity deployment decisions) when policy renders CCS marginally competitive. The cost of CO2 transportation generally affects the location of geologic CO2 storage investment more than the quantity of CO2 captured or the location of electricity generation investment. We conclude with a few recommendations for future energy system researchers when modeling CCS. For example, assuming a cost for geologic CO2 storage (e.g., $5/tCO2) may be less consequential compared to assuming free storage by excluding it from the model.

Despite the literature on urban economics has devoted a lot of attention to analyzing housing demand and its adjustment dynamics across markets, not a similar attention has been given to housing supply. In particular, there is limited knowledge on the way the supply curve would help to understand the urban equilibrium in highly dynamic markets as that of Bogota in Colombia. This article characterizes housing supply by stating the relationship between the value of housing and the price of urban land. We estimate Bogota’s housing supply curve using information available for 2008. Results suggest that market adjustment after demand shocks occurs through quantities.

One of the most important tasks in our country is the further development of the economy of industrial production, including increasing the depth of redistribution of domestic industrial products, predetermining the need for automation and computerization of machine-building production, the creation of modern methods of the digital economy in terms of optimizing the supply curve. This leads to the formulation of the tasks of improving the planning and management system of engineering production. Machine-building enterprises need a capacity planning system that would objectively reflect the established economic situation in order to make appropriate management decisions. The main objective of planning is the formation of a production schedule, the maximum load of work centers, taking into account the existing restrictions and priorities of orders. The paper shows enlarged or inter-workshop planning of a production schedule for a long planning horizon and detailed or operational intra-workshop planning, taking into account the production schedule, the availability of work centers and production facilities. A comparison of the planning levels. At the stage of enlarged planning, a production schedule is planned, which is a calendar schedule for the execution of production routing maps and allows to evaluate the feasibility of received orders for production by the date of need, to reschedule the constructed schedule when changes and deviations are made in production plans. At this stage, during the initial placement of the order in the production schedule for each line, the possibility of fulfilling the order by the specified requirement date is determined. If it is impossible to ensure the production of a particular product by the date of need, the date of its intended release is calculated. The production schedule is transferred to the level of detailed planning - the volume of work per planning interval, for the execution of which the availability of groups of work centers is enough for a given interval. Within this interval, a specific execution time is planned for each operation with the definition of specific work centers that will be involved in the execution of operations. As a result of mathematical calculations, the recommended sequence of planning production route maps is obtained. The mathematical model presented in the work of choosing the priority for planning routing maps of production allows us to calculate the feasibility of orders on time, and also to determine the planned time interval in which each routing map should be completed.

• This paper studies the supply curve of ride-hailing systems - the relationship between the throughput of trips of a system and the cost of its riders. • With finite matching radius, the supply curve of an e-hailing system is always backward bending. But a smaller matching radius leads to a weaker bend. • A smaller matching radius leads to a weaker bend, and adapatively adjusting the matching radius can completely avoid the bend. • An indiscriminate matching failure is identified, which can also cause backward bending supply. • Price and rationing discrimination to avoid such a matching failure are discussed. This paper studies the supply curve of ride-hailing systems under different market conditions. The curve defines a relationship between the throughput of trips of the system and the cost of riders it serves. We first focus on isotropic markets by revisiting a matching failure identified recently that matches a requesting rider with an idle driver very far away. The failure will cause the supply curve of an e-hailing market to be backward bending, but it is proved that the backward bend does not arise in the street-hailing market. By constructing a double-ended queuing model, we prove that the supply curve of an e-hailing system with finite matching radius is always backward bending, but a smaller matching radius leads to a weaker bend. We further reveal the possibility of completely avoiding the bend by adaptively adjusting the matching radius. We then turn to the anisotropic markets and identify another type of matching failure due to indiscriminate matching between drivers and riders, which again causes a backward bending supply. Given the prevalence of such a matching failure in real-world operations, we discuss how to avoid it using price or rationing discrimination. A conceptualized two-node network is constructed to facilitate the discussion. [ABSTRACT FROM AUTHOR]

This paper presents an estimate of the potential for energy efficiency improvements in the U.S. building sector by 2030. The analysis uses the Energy Information Administration's AEO 2007 Reference Case as a business-as-usual (BAU) scenario, and applies percentage savings estimates by end use drawn from several prior efficiency potential studies. These prior studies include the U.S. Department of Energy's Scenarios for a Clean Energy Future (CEF) study and a recent study of natural gas savings potential in New York state. For a few end uses for which savings estimates are not readily available, the LBNL study team compiled technical data to estimate savings percentages and costs of conserved energy. The analysis shows that for electricity use in buildings, approximately one-third of the BAU consumption can be saved at a cost of conserved energy of 2.7 cents/kWh (all values in 2007 dollars), while for natural gas approximately the same percentage savings is possible at a cost of between 2.5 and 6.9 $/million Btu. This cost-effective level of savings results in national annual energy bill savings in 2030 of nearly $170 billion. To achieve these savings, the cumulative capital investment needed between 2010 and 2030 is about $440 billion, which translates to a 2-1/2 year simple payback period, or savings over the life of the measures that are nearly 3.5 times larger than the investment required (i.e., a benefit-cost ratio of 3.5).

• Fair timber prices can easily be obscured, and it threatens adequate management. • An approach to present fair timber prices to supply a given mass is presented. • The approach derives timber supply curves under a proven stable state. • The fair prices in the application region were much lower than the current prices. • The prices can be a guide for promoting fair timber trading and policy designs. The long–time gap between planting and harvesting in forestry obscures necessary timber prices to balance with silviculture costs and often causes disproportionate market timber prices. This discrepancy can lead to stagnant timber production in forestry countries and promote global unsustainable land use and timber production. This study presents an approach to estimate fair timber prices that ensure the economic sustainability of timber-producing forest stands. Supply curves are derived numerically under perfect competition and a proven stable forest state; thereby, the curves suggest the fair timber prices for continuously supplying a given mass. An application in Nagano Prefecture, Japan, suggested that the current timber prices are lower than those required to stably supply the current mass. The supply curves also suggested necessary timber price increments and carbon prices to ensure the current supply without subsidy. Furthermore, the need for selecting timber-producing forest stands to stabilize forest management was indicated. [ABSTRACT FROM AUTHOR]

During polymer melt extrusion from a circular nozzle, the extrudate at low flow rate shows a stable linear shape with smooth surface but the extrudate at higher flow rate shows unstable shapes with spiral, rosary, or zig-zag structures. This phenomenon is known as melt fracture and hampers high throughput production in polymer processing. For linear polymers, synchronized oscillation of flow volume, extrusion pressures, and shape distortion is frequently observed, and it is known as stick-slip melt fracture (SSMF). Since this phenomenon is an oscillation phenomenon, many studies have been performed to analyze the mechanisms based on the oscillation theory. However, most of previous studies have mainly used mathematical calculation, and therefore experimental evaluations of SSMF have rarely been performed. In this study, we conducted an experimental approach to elucidate the mechanism of SSMF by using high density polyethylene as a polymer with varied lengths of the extrusion nozzle. Experimental results were analyzed by the simple model based on the oscillation theory. In the model, we introduced a supply curve representing a relationship between flow rate and pressure at the supplying side and a flow curve representing a flow resistance at the nozzle side. As a result, it was clarified that SSMF is a phenomenon of self-exited oscillation and also relaxation oscillation caused by the compressive elasticity of polymer melt and the negative damping appearing in a flow curve. We verified that SSMF does not occur when the negative damping is lost by repressing slip at inner side of the nozzle. Furthermore, we identified conditions under which SSMF does not occur despite the existence of negative damping; occurrence of SSMF depends on the mode of polymer supplying, a constant flow mode or a constant pressure mode. The former causes SSMF but the latter does not.

Waste-to-energy (WTE) technologies provide opportunities to use waste materials beneficially in producing power, transportation fuels, and chemicals. Using a suite of economic models, this study estimates prices of four WTE resources: food waste; fats, oils, and greases (FOG); animal manure; and sewage sludge. Some of these materials are commoditized (e.g. FOG) thus their price is determined by market demand. For the materials regarded as waste, the study relates price to the avoided cost of disposal through waste management alternatives such as landfilling. This study finds that significant amounts of these feedstocks could be available at negative prices, meaning that a potential bioenergy facility could receive these materials for free or be paid to accept them in some locations. It is estimated that about 61% of sewage sludge, 27% of manure, and 7% of food waste may be available at negative prices. These negative price feedstocks are not uniformly distributed and are most likely to occur in areas with organic waste disposal bans, high population densities, and high landfill tipping fees. This study intends to open an initial discussion into how stakeholders view and value these materials, and how this view is evolving as their potential as WTE feedstocks is realized. • The first study to analyze spatial variance in wet waste-to-energy feedstock prices. • Feedstocks can be viewed as commodities or wastes depending on location and markets. • If a resource has been commoditized (e.g. FOG), its price is determined by market demand. • If treated as a waste, feedstock price is driven by the cost of its disposal. • Negative waste feedstock prices are driven by disposal costs and organic waste bans. [ABSTRACT FROM AUTHOR]

Since the concept of environment production factor proposed, it has been widely concerned. However, it is lacking for the study on its supply, demand curve and price setting model. Several questions have been discussed in this paper. First, we analyze the supply curve without considering reserved self-using. Second, according to its concept and characteristics, we get its "vertical ladle" supply curve in practice. Finally, combine the demand curve of environment production factor; we get its equilibrium price under condition of perfect competition. [ABSTRACT FROM AUTHOR]

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