Academic literature on the topic 'Supply and demand curves'

Abstract This paper analyzes Internal Aggregate Demand. This aggregate, along with other production indicators, is the main indicator of the country’s economic performance rate. Objective analysis is important for their perspective, as well as for a set of other related indicators, such as inflation rate, unemployment rate, etc. In economic theory, the Aggregate Demand Curve (AD) deals with negative slope. At the point where AD interrupts the AS (aggregate supply curve) there is macroeconomic equilibrium. Creating this equilibrium, shifting curves, creates a number of other figures that show how the level of output, prices and employment will be, and overall the level of economy in the future. In this study, with the data of the Albanian economy, was built, for a period of 17 years, the Internal Demand Curve. Three effects are analyzed: the real balance sheet effect, the interest rates and the external trade effect. The internal demand curve has resulted in a Positive Slope. The equilibrium is not created at the intersection point of the curves. These curves stand facing each other. The equilibrium is set by the different aggregate price level. The Gross Domestic Product Curve (GDP) is the equilibrium curve created by the interaction of Aggregate Demand and Aggregate Supply. This position is real, and creates opportunities for objective analysis of the economy. This paper uses econometric, statistical, comparative and synthesis methods.

Purpose The study aims to propose an appraisal procedure based on the preferences stated by a sample of potential consumers and producers which makes it possible to obtain the hypothetical demand and supply curves and to estimate the most likely market value and transaction quantities for housing markets with unrevealed prices. Design/methodology/approach The procedure is divided into two steps: the first is aimed at selecting the alternatives that are most likely to meet the market’s preference by applying discrete choice (DC) analysis; the second makes it possible to estimate the potential demand and supply curves for the preferable alternatives singled out through DC analysis by using contingent valuation methods. Findings The results obtained considering only the hypothetical demand or the hypothetical supply differ by an average of 10 per cent from the actual sale price. Conversely, the values detected as the intersection of the hypothetical demand curve and the hypothetical supply curve, fall into variation margins that can be considered fully acceptable in real estate appraisal Originality/value As opposed to the applications performed in international real estate operations where reference is made solely to the potential demand estimate, the described procedure estimates the transaction value as the intersection between the hypothetical demand and supply curves, for the purposes of keeping account of the conditions that generally occur in the real market. Furthermore, it is possible to detect the incidence of the characteristics in market price formation, and to identify the market share of possible alternative assets and estimate the optimal quantity to be produced.

This study aimed to analyze the evolution of the production and price of biomass from native and planted forests of the state of Paraná (Brazil), between 1998 and 2015, based on the behavior of the prices of the products, according to variations of their supply or demand. The annual rates for growth of the price and quantity produced were calculated and related to the displacements of the supply and demand curves of the products. The results indicated a decrease in the quantity and an increase in the biomass price for native forests, which caused a shift in the supply curve to the left. For the biomass of planted forests, the demand curve shifted to the right due to the demand increase of this product for energy production. The behavior of both curves indicated a substitution of the biomass from native forests to biomass from planted forests due to factors related to the increase of environmental protection regarding the native forests located in the state of Paraná

Experimental investigation was conducted on a 24 km long segment of the two-lane state road to collect the driver behavior data. The research involved 20 drivers driving their own cars equipped with the GPS device. Considering the impact of path radius and speed on the side friction demand, the design consistency on horizontal curves was evaluated by determining the margins of safety. The analysis showed that the vehicle path radii were mainly smaller than curve radius, on average for 12%. Regression analysis indicated that the percentage difference between the curve radius and vehicle path radius is not affected by the speed, speed differential and geometric characteristics of the curve and surrounding elements. Two different margins of safety were analyzed. One is the difference between maximum permissible side friction (based on design speed) and side friction demand, while another is the difference between side friction supply (based on operating speed) and side friction demand. Generally, demands exceeded supply side friction factors on curves with radii smaller than 150 m, whereas “poor” conditions (in terms of Lamm’s consistency levels) were noted for curves under approximately 220 m. Both values are very close to the critical radius below which higher accident rates were observed according to several accident studies. Based on the results of the research, it is proposed to use a 12% smaller curve radius for the evaluation of margin of safety and that curves with radii smaller than 200 m should be avoided on two-lane state roads outside the built-up area.

I study the dynamics of default-free bond yields and term premia using a novel equilibrium term structure model with a New-Keynesian core and imperfect information about productivity. The model generates term premia that are on average positive with sizable countercyclical variation that arises endogenously. Importantly, demand shocks, in addition to supply shocks, play a key role in the dynamics of term premia. This is in sharp contrast to existing DSGE term structure models with perfect information, which tend to rely on large supply shocks to generate timevariation in yields and term premia. With imperfect information, a shock to productivity is a supply shock, while a shock to signals about productivity that do not lead to actual changes in productivity acts as a demand shock. Nevertheless, an increase in economic activity generates more information about productivity, regardless of which type of shock it arises from. Moreover, a decrease in economic uncertainty leads to a decline in term premia as longer-term bonds are risky on average. This feature helps reconcile the empirical evidence that term premia have been on average positive and countercyclical, with numerous studies pointing to demand shocks as being an important driver of business cycles over the last few decades.

The ongoing climate change, the depletion of resources and the visible environmental changes led in the last decades to a revolution in our thinking about resources. Several technologies have been and continue to be developed in order to avoid depletion of fossil fuels by shifting the use from non-renewable to renewable resources. One of the most important related fields of study is the energy sector: the need to pass from a fossil-fuels based energy production to the so called Green Energy has brought many opportunities of development and growth but also raised many issues to solve. On a local scale, the main issues of this ongoing process are to meet the needs of the consumer by an optimized use of the local available resources and the economical sustainability of green energy production. In the RegiOpt software the principles of Process Network Synthesis (PNS) are used to advice the stakeholders in the planning of a profitable and sustainable regional energy supply system. The software compares the possible energy supply technologies and gives as output one or more optimal sets of technologies that optimize the use of resources with regard to the economical potential and the sustainability of the single processes. While the economical and the environmental aspects are considered in the RegiOpt, the further development of the software requires the consideration of time-dependency of energy supply and energy demand on annual basis. The capacity of covering energy demand over time is a central issue of energy production industry, under this point of view the current study focused on the time-dependency of supply and demand. Yearly response curves of strong time-dependent supply technologies have been found and have been combined with the yearly demand response curves, in order to evaluate the gap between the proposed energy system and the regional demand. The resulting curve has then been divided into a reasonable number of intervals representing periods of higher and lower difference between production and consumption. To fit the results in the RegiOpt software, we proceeded to an approximation of every period by its mean: the mean value of the curve in every period fairly represents the curve itself. Under this conditions the RegiOpt can be run multi periodically optimizing the technologies network for all periods.

Electricity is the most preferred source of energy, because of its quality and convenience of usage. It is probably one of the most vital infrastructural inputs for economic development of a country. Indeed it is the fulcrum which can leverage the future pace of growth and development. These reasons have made the electric power industry one of the fastest growing sectors in most developing countries and particularly in India. Therefore it is not surprising to observe the economic growth of a country being related to the increase in electricity consumption. In India, the growth rate of demand for power is generally higher than that of Gross Domestic Product (GDP). However, to achieve this kind of growth in electricity supply, the capital investments required are very huge. Even though the electricity sector generally gels a major share in the budgetary allocations in India, this is inadequate to add the required quantum of new generation capacity to keep pace with the increase in demand for electricity. Additional constraints like capital scarcity in the public sector, lack of enthusiasm among the private and foreign investors, and strong opposition from the environmentalists have further contributed to this slow pace of new generating capacity addition. This has resulted in severely constrained systems in India. The main focus of the present research work is on the development of an integrated approach for electricity planning using a mathematical modeling framework in (he context of resource constrained systems. There are very few attempts in the literature to integrate short, medium and long term issues in electricity planning. This is understandable from the point of view of unconstrained electricity systems where this type of integration is unnecessary since such systems have a luxury of surplus capacity to meet the current demand and capacity additions are required only for meeting predicted future increase in demand. However, in the case of constrained electricity systems, which are characterized by shortages, this kind of integration is very essential. These systems have to manage with inadequate capacity in the present, plan capacity additions to bridge the existing gap and to meet future increase in demand, and always explore the possibility of adding capacity with short gestation period. The integrated approach is expected to achieve effective supply-demand matching on a continuous basis encompassing both the short term and long term horizons. To achieve this, we have considered three alternatives- existing supply, new supply and non-supply (rationing) of electricity. The electricity system of the state of Karnataka, which is severely constrained by both limited capital and energy resources, has been selected for this purpose. As a first step, the supply and demand situation has been studied in the context of resource constraints. In terms of supply, both existing and future additions are studied in detail with respect to the potential created, generation types, import potential, technical constraints, energy and power shortages, planned and proposed capacity additions by both public and private sectors, etc. The demand patterns have been studied by introducing a new concept of "Representative Load Curves (RLCs)". These RLCs are used to model the temporal and structural variations in demand for electricity. Also, appropriate non-supply options (rationing measures) for effective management of shortages are identified. Incorporating this information, an integrated mathematical model, which is expected to generate a target plan for a detailed generation scheduling exercises and a requirement plan for a regular generation expansion planning, has been developed. The other important alternative "Demand-Side-Management (DSM)", which could be considered as an effective option to achieve efficient supply-demand matching has not been included in the present research work. The major reason for not including the DSM alternatives is due to the difficulty in integrating these in the modelling approach adopted here. In the present approach we have used typical daily load curves (RLCs) to represent the demand for electricity. These are aggregate load curves and do not contain any sector-wise or end-use-wisc details. On the other hand, DSM alternatives are end-use focused. To incorporate DSM alternatives, we should have information on end-usc-wisc power demand (kW or MW), savings potential, time-of-use, etc. For this purpose it may be required to have end-use-wisc daily load curves. This information is not available and a separate detailed survey may be required to generate these load curves. This, we felt, is out of the scope of this present research work and a separate study may be required to do this. Therefore, we restricted our focus to supply planning alone. A detailed literature review is conducted to understand different types of modeling approaches to electricity planning. For the present study, however, the review of literature has been restricted to the methods of generation expansion planning and scheduling. In doing so, we attempted to bring out the differences in various approaches in terms of solution methods adopted, alternatives included and modifications suggested. Also, we briefly reviewed the literature on models for power and energy rationing, because management of shortages is an important aspect of the present study. Subsequently, a separate section is devoted to present an overview of the non-supply of electricity and its economic impacts on the consumers. We found that the low reliability of the electrical system is an indicator of the existence of severe shortages of power and energy, which cause non-supply of electricity to the consumers. The overview also presented a discussion on reasons for non-supply of electricity, and the types of non-supply options the utilities adopt to over come these shortages. We also attempted to explain what we mean by non-supply of electricity, what are its cost implications, and the methods available in the literature to estimate these costs. The first objective of the research pertains to the development of a new approach to model the varying demand for electricity. Using the concept of Representative Load Curves (RLCs) we model the hourly demand for a period of four years, 1993-94, 1994-95, 1995-96 and 1996-97, to understand the demand patterns of both unconstrained and constrained years. Multiple discriminant analysis has been used to cluster the 365 load curves into nine RLCs for each of the four years. The results show that these RLCs adequately model the variations in demand and bring out the distinctions in the demand patterns existed during the unconstrained and constrained years. The demand analysis using RLCs helped to study the differences in demand patterns with and without constraints, impacts of constraints on preferred pattern of electricity consumption, success of non-supply options in both reducing the demand levels and greatly disturbing the electricity usage patterns. Multifactor ANOVA analyses are performed to quantify the statistical significance of the ability of the logically obtained factors in explaining the overall variations in demand. The results of the ANOVA analysis clearly showed that the considered factors accounted for maximum variations in demand at very high significance levels. It also brought out the significant influence of rationing measures in explaining the variations in demand during the constrained years. Concerning the second objective, we explained in detail, the development of an integrated mixed integer-programming model, which we felt is appropriate for planning in the case of resource constrained electricity systems. Two types of integrations are attempted (i) existing supply, non-supply and new supply options for dynamically matching supply and demand, (ii) operational and strategic planning in terms of providing target plans for the former and requirement plans for the latter. Broadly, the approach addresses the effective management of existing capacity, optimal rationing plan to effectively manage shortages and rationally decide on the new capacity additions both to bridge the existing gap between supply and demand, and to meet the future increases in demand. There is also an attempt to arrive at an optimal mix of public and private capacity additions for a given situation. Finally, it has been attempted to verify the possibility of integration of captive generation capacity with the grid. Further, we discussed in detail about the data required for the model implementation. The model is validated through the development of a number of scenarios for the state of Karnataka. The base case scenario analyses are carried out for both the unconstrained and constrained years to compare the optimal allocations with actual allocations that were observed, and to find out how sensitive are the results for any change in the values of various parameters. For the constrained years, a few more scenarios are used to compare the optimal practice of managing shortages with to what has been actually followed by the utility. The optimal allocations of the predicted demand to various existing supply and non-supply options clearly showed that the actual practice, reflected by the actual RLCs, are highly ad hoc and sub-optimal. The unit cost comparisons among different scenarios show that the least cost choice of options by the utility does not necessarily lead to good choices from the consumers’ perspective. Further, a number of future scenarios are developed to verify the ability of the model to achieve the overall objective of supply-demand matching both in the short and long term. For this purpose both the short horizon annual scenarios (1997-98 to 2000-01) and long horizon terminal year scenarios (2005-06 and 2010-11) are developed assuming capacity additions from only public sector. Overall, the results indicated that with marginal contributions from non-supply options and if the public sector generates enough resources to add the required capacity, optimal matching of supply and demand could be achieved. The scenario analyses also showed that it is more economical to have some level of planned rationing compared to having a more reliable system. The quantum of new capacity additions required and the level of investments associated with it clearly indicated the urgent need of private sector participation in capacity additions. Finally, we made an attempt to verify the applicability of the integrated model to analyse the implications of private sector participation in capacity additions. First, a number of scenarios are developed to study the optimal allocations of predicted hourly demand to private capacity under different situations. Secondly, the impacts of privatisation on the public utility and consumers are analysed. Both short term and long term scenarios are developed for this purpose. The results showed the advantage of marginal non-supply of electricity both in terms of achieving overall effective supply-demand matching and economic benefits that could be generated through cost savings. The results also showed the negative impacts of high guarantees offered to the private sector in terms of the opportunity costs of reduced utilization of both the existing and new public capacity. The estimates of unit cost of supply and effective cost of supply facilitated the relative comparison among various scenarios as well as finding out the merits and demerits of guarantees to private sector and non-supply of electricity. The unit cost estimates are also found to be useful in studying the relative increase in electricity prices for consumers on account of privatization, guarantees and reliable supply of electricity. Using the results of scenario analyses, likely generation expansion plans till the year 2010-11 are generated. The analyses have been useful in providing insights into fixing the availability and plant load factors for the private sector capacity. Based on the analysis, the recommended range for plant utilization factor is 72.88 - 80.57%. The estimated generation losses and the associated economic impacts of backing down of existing and new public capacity on account of guarantees offered to private sector are found to be significantly high. The analyses also showed that the backing down might take place mainly during nights and low demand periods of monsoon and winter seasons. Other impacts of privatization that studied are in terms of increased number of alternatives for the utility to buy electricity for distribution and the associated increase in its cost of purchase. Regarding the consumers, the major impact could be in terms of significant increase in expected tariffs. The major contributions of this thesis are summarized as follows: i. An integrated approach to electricity planning that is reported here, is unique in the sense that it considers options available under various alternatives, namely, existing supply, non-supply and new supply. This approach is most suited for severely constrained systems having to manage with both energy and capital resource shortages. ii. The integration of operational and strategic planning with coherent target plans for the former and requirement plans for the latter bridges the prevailing gap in electricity planning approaches. iii. The concept of Representative Load Curves (RLCs), which is introduced here, captures the hourly, daily and seasonal variations in demand. Together, all the RLCs developed for a given year are expected to model the hourly demand patterns of that year. These RLCs are useful for planning in resource constrained electricity systems and in situations where it is required to know the time variations in demand (e.g. supply-demand matching, seasonal scheduling of hydro plants and maintenance scheduling). RLCs are also useful in identifying the factors influencing variations in demand. This approach will overcome the limitations of current method of representation in the form of static and aggregate annual load duration curves. iv. A new term, "non-supply of electricity" has been introduced in this thesis. A brief overview of non-supply presented here includes reasons for non-supply, type of non-supply, methods to estimate cost of non-supply and factors influencing these estimates. v. The integrated mixed integer programming model developed in the study has been demonstrated as a planning tool for- • Optimal hourly and seasonal scheduling of various existing supply, non-supply and new supply options • Estimation of supply shortages on a representative hourly basis using the information on resource constraints • Effectively planning non-supply of electricity through appropriate power/energy rationing methods • Estimation of the need for the new capacity additions both to bridge the existing gap and to take care of increase in future demand levels • Optimal filling of gaps between demand and supply on a representative hourly basis through new supply of electricity • Optimally arriving at the judicious mix of public and private capacity additions • Studying the impacts of private capacity on the existing and new public sector capacity, and on the consumers • Optimally verifying the feasibility of integrating the captive generation with the total system vi. The demand analysis using RLCs helped to bring out the differences in demand patterns with and without constraints, impacts of constraints on preferred pattern of electricity consumption, success of non-supply options in both reducing the demand levels and greatly disturbing the electricity usage patterns. Multifactor ANOVA analyses results showed that the logically obtained factors accounted for maximum variations in demand at very high significance levels. vii. A comparison of optimal (represented by optimal predicted RLCs) and actual (reflected by actual RLCs) practices facilitated by the model showed that the actual practice during constrained years is highly ad hoc and sub-optimal. viii. The results of the scenario analyses showed that it is more economical to have some amount of planned rationing compared to having a more reliable system, which does not allow non-supply of electricity. ix. The scenarios, which analysed the impacts of high guarantees offered to the private sector, showed the negative impacts of these in terms of reduced utilization of both the existing and new public capacity. x. Generation expansion plans till the year 2010-11 are developed using the results of various kinds of scenario analyses. Two groups of year-wise generation expansion plans are generated, one with only public sector capacity additions and the other with private sector participation. xi. The impacts of privatization of capacity additions are studied from the point of view of the utility and consumers in terms of expected increase in cost of purchase of electricity and tariffs. xii. The analyses are also made for developing some insights into fixing the availability and plant load factors for the private capacity. Based on the analysis, the recommended range for plant utilization factor is 72.88 - 80.57%. We believe that the integrated approach presented and the results obtained in this thesis would help utilities (both suppliers and distributors of electricity) and governments in making rational choices in the context of resource constrained systems. The results reported here may also be used towards rationalization of Government policies vis-a-vis tariff structures in the supply of electricity, planning new generation capacity additions and effective rationing of electricity. It is also hoped that the fresh approach adopted in this thesis would attract further investigations in future research on resource constrained systems.

For the appropriate operation and correct short term planning, daily demand curve (DDC) of municipal water distribution networks should be forecasted beforehand. For that purpose, artificial neural networks (ANN) is used as a new method. The proposed approach employs already recorded DDCs extracted from the database of ASKI (Ankara Water Authority) SCADA center and related independent parameters such as temperature and relative humidity obtained from DMI (State Meteorological Institute). In this study, a computer model was developed in order to forecast hourly DDCs using Matlab and related modules. Parameters that affect the consumption of the water were determined as temperature, relative humidity, human behavior (weekend or workday) and season. Randomly selected days were taken into account for performance of the ANN model. Forecasted DDC values were compared with recorded data and consequently the model gives relatively satisfactory results, an average of 75% match according to R2 values for Ankara N8-3 network. Same architecture was applied for Antalya network give better results, average of 85%. For planning purposes
total volume and peak water consumption values for the selected recorded days, the day before recorded days, ANN forecasted days and seasonal average was compared and seasonal average gave relatively better results. Using the forecasted DDC, (i) performance analysis of the pressure zone and (ii) optimum valve setting evaluation for pressure management were realized. The results of the study may help water utilities for short term planning of a water distribution network, rehabilitation of elements, taking counter measures and setting the valve openings for minimizing leakage and optimizing customer conformity of the distribution network.

In deregulated electricity markets, accurate modeling and forecasting of different variables, e.g. demand, prices, production etc. have obtained increasing importance in recent years. As in most electricity markets, the daily demand and prices are determined the day before the physical delivery by means of (semi-) hourly concurrent auctions, accurate forecasts are necessary for the efficient management of power systems. However, it is well known that electricity (demand/price) data exhibit some specific features, among which, daily, weekly and annual periodic patterns as well as non-constant mean and variance, jumps and dependency on calendar effects. Modeling and forecasting, thus, is a challenging task. This thesis tackles these two issues, and to do this, two approaches are followed. In the first case, we address the issue of modeling and out-of-sample forecasting electricity demand and price time series. For this purpose, an additive component model was considered that includes some deterministic and a stochastic residual components. The deterministic components include a long-term dynamics, annual and weekly periodicities and calendar effects. The first three components were estimated using splines while the calendar effects were modeled using dummy variables. The residual component is instead treated as stochastic and different univariate and multivariate models have been considered with increasing level of complexity. In both cases, linear parametric and nonlinear nonparametric models, as well as functional based models, have been estimated and compared in a one day-ahead out-of-sample forecast framework. The class of univariate models includes parametric autoregressive models (AR), nonparametric and nonlinear regression models based on splines (NPAR) and scalar-response functional models, that in turns can be formulated parametrically (FAR) or non parametrically (NPFAR). The multivariate models are vector autoregressive models (VAR) and functionalresponse, parametric (FFAR) and nonparametric (NPFFAR), models. For this issue, five different electricity markets, namely, British electricity market (APX Power UK), Nord Pool electricity market (NP), Italian electricity market (IPEX), Pennsylvania-New Jersey-Maryland electricity market (PJM) and Portuguese electricity market (OMIE(Po)) were considered for the period 2009 to 2014. The first five years were used for model estimation while the year 2014 was left for one-day-ahead forecasts. Predictive performances are first evaluated by means of descriptive indicators and then through a test to assess the significance of the differences. The analyses suggest that the multivariate approach leads to better results than the univariate one and that, within the multivariate framework, functional models are the most accurate, with VAR being a competitive model in some cases. The results also lead to another important finding concerning to the performance of parametric and nonparametric approach that showed strong linkage with underlying process. Finally the obtained results were compared with other works in the literature that suggest our forecasting errors are smaller compared with the state-of-art prediction techniques used in the literature. In the second part of this thesis the issue of electricity price forecasting is revisited following a completely different approach. The main idea of this approach is that of modeling the daily supply and demand curves, predicting them and finding the intersection of the predicted curves in order to find the predicted market clearing price and volume. In this approach, the raw bids/offers data for demand and supply, corresponding to each (half-) hour is first aggregated in a specific order. The functional approach converts the resulted piece wise curves into smooth functions. For this issue, parametric functional model (FFAR) and the nonlinear nonparametric counterpart (NPFFAR) were considered. As benchmark, an ARIMA model was fitted to the scalar time series corresponding to the market clearing prices obtained from the crossing points of supply and demand curves. Data from Italian electricity market were used for this issue and the results are summarized by different descriptive indicators. As in the first case, results show superior forecasting performance of our functional approach compare to ARIMA. Among different models, the nonparametric functional model produces better results compared to parametric models. Apart from the improvement in forecasting accuracy, it is important to stress that this approach can be used for optimizing bidding strategies. As forecasting the whole curves gives deep insight into the market, our analysis showed that this strategy can significantly improve bidding strategies and maximize traders profit.
Nell’ambito dei mercati elettrici liberalizzati, negli ultimi anni l’interesse verso una buona modellazione e un’accurata previsione di variabili da essi provenienti, ad es. domanda, prezzi, produzione etc., è andato via via crescendo. Ciànche perché in molti mercati elettrici, i prezzi e i volumi giornalieri vengono determinati mediante un sistema di aste (semi-)orarie che ha luogo il giorno precedente a quello della consegna fisica; una previsione accurata permette quindi un’efficiente gestione del sistema elettrico. La modellazione e la previsione di queste variabili, tuttavia, è resa difficile dal fatto che le serie storiche di domanda e prezzi, sono caratterizzate dalla presenza di vari tipi di periodicità, annuale, settimanale e giornaliera, da una media e una varianza che non sono costanti nel tempo, da picchi improvvisi e dalla dipendenza da diversi effetti di calendario. Questa tesi si occupa proprio di questo difficile compito e lo fa seguendo dua approcci principali. Nel primo approccio vengono modellate e previste, in un contesto out-of-sample, le serie storiche della domanda e dei prezzi ufficialmente riportati dal Gestore dei Mercati Energetici. A tal fine, viene considerato un modello a componenti additive che include una parte deterministica ed una componente residua stocastica. La parte deterministica, in particolare, contiene varie componenti che descrivono la dinamica di lungo periodo, quella periodica annuale e settimanale e gli effetti di calendario. Le prime tre componenti vengono stimate utilizzando delle splines del tempo mentre gli effetti di calendario vengono modellati mediante variabili dummy. La componente residuale, invece, viene trattata in maniera stocastica mediante vari modelli, univariati e multivariati, con diversi livelli di complessità. Sia nel caso univariato che in quello multivariato sono stati considerati modelli parametrici e non parametrici, nonché modelli basati sull’approccio funzionale. La classe dei modelli univariati comprende modelli lineari autoregressivi (AR), modelli (auto)regressivi non parametrici e non lineari basati su spline (NPAR) e modelli funzionali a risposta scalare. Questi ultimi, a loro volta, possono essere formulati secondo una specificazione parametrica (FAR) o non parametrica (NPFAR). Relativamente alla classe dei modelli multivariati, invece, sono stati considerati modelli vettoriali autoregressivi (VAR) e modelli funzionali a risposta funzionale, sia nella versione parametrica (FFAR) che in quella non parametrica (NPFFAR). Tutti questi modelli sono stati stimati e confrontati in termini di capacità previsiva nell’ambito della previsione a 1 giorno e out-of-sample. Per verificare le performance dei modelli sono stati considerati i dati provenienti da 5 tra i principali mercati elettrici: il mercato inglese (APX Power UK), il mercato del Nord Pool (NP), quello italiano (IPEX), quello di Pennsylvania-New Jersey-Maryland electricity market (PJM) ed, infine, quello portoghese (OMIE(Po)). Il periodo analizzato va dal 2009 al 2014. I primi cinque anni sono stati utilizzati per la stima dei modelli mentre l’intero 2014 è stato lasciato per le previsioni out-of-sample. La performance predittiva è stata valutata prima mediante indici descrittivi e poi mediante un test statistico per attestare la significatività delle differenze. I risultati suggeriscono che, in generale, l’approccio multivariato produce previsioni più accurate dell’approccio univariato e che, nell’ambito dei modelli multivariati, i modelli basati sull’approccio funzionale risultano i migliori, anche se il VAR è comunque competitivo in diverse situazioni. Questi risultati possono essere letti anche come un segnale della presenza o meno di non linearità nei vari processi generatori dei dati. Anche se il confronto con altri lavori non è mai del tutto omogeneo, gli errori di previsione ottenuti sono tendenzialmente più piccoli di quelli riportati in letteratura. Nella seconda parte della tesi il tema della previsione dei prezzi dell’elettrcità è stato riconsiderato seguendo un percorso completamente diverso. L’idea di fondo di questo nuovo approccio è quella di modellare non le serie dei prezzi di mercato, ma le curve di domanda e di offerta giornaliere mediante modelli funzionali, di prevederle un giorno in avanti, e di trovare l’intersezione tra le due curve previste. Questa intersezione fornisce la previsione della quantità e del prezzo di equilibrio (market clearing price and volume). Questa metodologia richiede di agregare, secondo uno specifico ordine, tutte le offerte di vendita e le richieste di acquisto presentate ogni (mezz’)ora. Ciò produce delle spezzate lineari a tratti che vengono trasformate dall’approccio funzionale in curve liscie (smooth functions). Per questo fine, sono state considerati modelli funzionali parametrici (FFAR) e nonparametrici (NPFFAR). Come benchmark è stato stimato un modello ARIMA scalare alle serie storiche dei prezzi di equilibrio (clearing prices) ottenuti dall’incrocio tra le curve di domanda e di offerta. L’applicazione di questo metodo è stata fatta limitatamente al caso del mercato italiano . Come precedentemente, i risultati suggeriscono una migliore abilità previsiva dell’approccio funzionale rispetto al modello ARIMA. Tra i vari modelli considerati, quello funzionale non parametrico ho fornito i risultati migliori. Va sottolineato poi che un aspetto rilevante, che va oltre il miglioramento nell’accuratezza previsiva, è che l’approccio basato sulla previsione delle curve di offerta e di domanda può essere utilizzato per ottimizzare le strategie di offerta/acquisto da parte degli operatori e, di conseguenza, per massimizzare il profitto dei traders.

På uppdrag av Sundsvall Energi AB har FVB Sverige AB påbörjat en förstudie kring etableringen av fjärrkyla i Sundsvall. Produktionsmedlen i det planerade nätet kommer att innefatta frikyla från akviferen och en kompressordriven kylmaskin. Det övergripande syftet med projektet har dels varit att ta fram en optimal framledningskurva, samt att ta reda på i vilken utsträckning frikylan kan nyttjas innan kylmaskinen måste användas som spetsproduktionsmedel. Projektet har inledningsvis fokuserat på att undersöka hur klimatet och kylbehovet ser uti Sundsvall. Kylbehovet granskades utifrån sex befintliga byggnader som nyttjar dricksvattenkyld fjärrkyla i Sundsvall. Därefter undersöktes olika typer av klimatsystem för att utröna vad de har för krav på framledningstemperaturen. Det konstaterades att kylbatterier var den komponent som kräver lägst framledningstemperatur, varför kyleffektberäkningar utfördes på dem. Resultatet ur kylbatteriberäkningarna fick motsvara den av fjärrkylenätet avgivna kyleffekten vid varierande utomhustemperatur. Genom att väga den avgivna kyleffekten vid varierande framledningstemperatur mot det erforderliga kyleffektbehovet vid varierande utomhustemperatur kunde framledningskurvan ta form. Akviferen antas hålla en temperatur på omkring 7°C till 9°C året runt, men utgångspunkten i detta projekt har varit att den konstant är 9°C. Under de förutsättningarna har framledningstemperaturen kunnat bestämmas till att vara 11°C under större delen av året, men att den sänks vid en utomhustemperatur på omkring 21°C i varierande grad ned till 6°C vid utomhustemperaturen 25°C. Med hjälp av framledningskurvan kunde därefter frikylans täckningsgrad bedömas. Resultatet visar att om framledningens temperatur höjs med 0,5–1,0°C i distributionsnätet kommer kylmaskinen att behöva vara i drift under 159 timmar per år. Om istället uppvärmningen blir 1,5° eller 2,0°C kommer kylmaskinen behöva vara i drift under 233 timmar respektive 325 timmar. Sammantaget har samtliga av projektets konkreta och verifierbara mål besvarats.
On behalf of Sundsvall Energi AB, FVB Sverige AB has initiated a preliminary study on the establishment of a district cooling system in Sundsvall. The main source for the cooling will be cool water drawn from the aquifer and a compressor chiller. The main purpose of this project has both been to provide the optimal supply temperature of the cooling network at different outdoor temperatures, and to find out to what extent the cool water from the aquifer can be used by itself as the cooling source. The project was initially focused on examining the climate and cooling demand in Sundsvall. The cooling demand was examined on the basis of six existing buildings that uses freshwater district cooling, and different types of climatesystems were then examined to ascertain what their requirements for the supply temperature are. Cooling coil batteries were found to be the component that requires the lowest supply temperature; therefore, the cooling power calculations were relied on them. The outcome of the cooling coil battery calculations was presumed to correspond to the cooling power of the network itself. By comparing the cooling power of the coil batteries at different supply temperatures and the cooling demand at different outdoor temperatures the main supply temperature for the district cooling network took shape. The aquifer is expected to maintain a temperature of approximately 7°C to 9°C, but in this project the temperature is set to exactly 9°C. On those premises the supply temperature of the cooling network could be set to 11°C for most of the year, but with a reduction of the supply temperature at outdoor temperatures around 21°C. Subsequently the supply temperature is reduced to 6°C at the outdoor temperature 25°C. Via the supply temperature curve, the aquifer cooling coverage ratio could be assessed. The result shows that if the supply temperature is raised between 0,5°C and 1,0°C in the distribution network the compressor chiller will have to be in operation for 159 hours per year. If instead the supply temperature is raised 1,5°C or 2,0°C, the compressor chiller must be in operation for 233 hours and 325 hours, respectively. In summary, all the goals and targets of the project have been completed.

Purpose: This research aims to enhance the current understanding and knowledge of the demand-supply chain management (DSCM) concept by determining its elements, benefits, and requirements, as well as by analyzing key elements of the concept. Methodology: This research has utilized the case study strategy and the survey strategy, however, the case study strategy dominates. The case study research has involved five companies originating from Sweden and the collection of empirical data mainly from in-depth interviews with key persons representing senior and middle management. The survey research targeted the largest firms in Sweden and Finland and empirical data was collected through an online questionnaire. Findings: This research has established that the main elements of DSCM include market orientation, coordination of the demand and supply processes, viewing the demand and supply processes as being equally important, as well as value creation, differentiation, innovativeness, responsiveness, and cost-efficiency in the demand and supply processes. It has also been revealed that the main benefits of DSCM include enhanced competiveness, enhanced demand chain performance, as well as enhanced supply chain performance, while the main requirements of DSCM include organizational competences, company established principles, demand-supply chain collaboration, and information technology support. A key element of DSCM further investigated is differentiation focused supply chain design. It has been shown that these efforts can be organized into a process of five stages. In addition, it is important that this process is addressed in parallel with the new product development (NPD) process, that information is exchanged between them, and that they are directed on the basis of the same segmentation model. Another key element of DSCM further investigated is coordination between NPD and SCM. This research has identified several significant linkages between these management directions, which motivate the use of an integrative NPD process where the NPD functions are aligned with the main supply functions in the company and other sales-related functions supporting the commercialization. A final key element of DSCM further investigated is the significance of regarding the demand processes and the supply processes as being equally important. This research has revealed that logistics outsourcing can be risky, if it results in the supply processes being considered less important. Nevertheless, if senior management regards the outsourced processes as equally important as the in-house processes, the effect of logistics outsourcing on company strategies and direction in SCM could be reduced and logistics outsourcing could instead provide an opportunity to improve the design and differentiation of the supply chain. Research limitations/implications: This research has proposed, described, and further analyzed a demand-supply oriented management approach. Such a management approach stresses that the demand processes and the supply processes have to be coordinated and directed at an overlying level, in order to gain and sustain a competitive advantage in competitive and fragmented markets. This research is mainly explorative in nature, and more empirical data, from similar and other research settings, is needed to further validate the findings. Another limitation of the research is that it is essentially limited to Swedish companies (even if some Finnish companies are involved in the survey), however, many of the case companies have a large international presence and are among the top three in their industries, facts which provide some grounds for generalization. Practical implications: This research provides researchers and practitioners with insights into how to develop a demand-supply oriented business. It shows that companies should organize themselves around understanding how customer value is created and delivered, as well as how these processes and management directions can be coordinated. In order for this to occur, the demand and supply processes must be considered as being equally important and the firm needs to be managed jointly and in a coordinated manner by the demand- and supply-side of the company. It is also important that value creation is considered in both the demand and supply processes. Originality/value: Despite strong arguments from both researchers and practitioners for a demand-supply oriented management approach only a minority of companies appear to have effectively coordinated the demand and supply processes. This might be influenced by the lack of research examining how the demand and supply processes can be coordinated, what benefits can be gained by coordinating them, and what requirements are necessary to succeed. This research contributes by investigating these types of aspects further.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2003.
Includes bibliographical references (p. 140-141).
This thesis consists of three chapters that investigate the index premium and its underlying economics both theoretically and empirically. The first chapter presents our empirical findings about the index premium and its properties. First, we find that the index premia for both the S&P 500 and Russell 2000 have been growing over time, reaching levels of about 15% and 10%, respectively, in 2000. The premia arise somewhat gradually between the announcement and effective days and do not reverse at least in the next few weeks. Second, we find that the index premium is related to the idiosyncratic risk and market equity of a firm with economic and statistical significance. Third, we introduce a new concept that we label the index turnover cost, which represents a cost borne by index funds due to the index premium. We illustrate this cost and estimate it as 70-85 bp annually for the S&P 500 and 110-211 bp annually for the Russell 2000. The second chapter develops the first theoretical explanation in the literature for downward-sloping demand curves. In traditional multi-asset models such as the CAPM, demand curves for stocks are almost perfectly horizontal, because a representative investor who is sufficiently risk-tolerant to hold the entire market portfolio has to be almost indifferent to idiosyncratic risk. We start with the basic CAPM setting, but we further assume that there is a fixed cost to actively managing a stock portfolio and that individuals pay the cost through an institution as a proportional fee. In equilibrium, the proportional fee can entirely determine the cross-sectional pricing of stocks, while the risk aversion of individual investors still determines the aggregate market risk premium.
(cont.) In contrast to any representative agent models, this allows demand curves for stocks to be sufficiently steep to have economic significance, also implying that stocks will be priced only approximately around their fundamental values. Our explanation can account for several empirically observed puzzles such as the magnitude of the S&P 500 index premium. The third chapter focuses on index investors for whom the index premium creates a recurring cost: as the index is updated, they need to buy stocks with the premium and sell stocks without the premium. Different index rules can produce different index premia due to the different frequency and criteria of updating. We build a model to investigate the behavior of the index turnover cost and the portfolio performance of a mechanical index fund under a market-cap rule, an exogenous random rule, and a deterministic rule. We find that the rational anticipation of future index composition reflected in prices today eliminates any first-order differences in index fund performance across the three index rules. As the index investors become a large part of the market, the non-index investors become less diversified, and this induces hedging motives which hurt the index investors especially under a market-cap rule.
by Antti Petäjistö.
Ph.D.

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006.
Includes bibliographical references (leaves 73-75).
Globalization increases product variety and shortens product life cycles. These lead to an increase in demand uncertainty and variability. Outsourcing to low-cost countries increases supply lead-time and supply uncertainty and variability. Coupled with the increase of mergers and acquisitions, which increase supply chain complexity, and the unforgiving nature of having too little or too much inventory, these factors have accelerated the importance and adoption of the Sales and Operations Planning (S&OP) process. S&OP is driven by a cross functional team, with the purpose of balancing supply and demand with the objective of maximizing a company's goals. It manages the supply and demand uncertainties, balances the different internal and external stakeholders' interests, and aligns the operations towards its strategy and vision. In support of the Supply Chain 2020 Project at MIT, this thesis focuses on analyzing the S&OP function across industries. Using the Phase I SC 2020 theses, literature, white papers, and interviews with industry experts, this thesis compares and contrasts the S&OP practices across nine industries.
(cont.) It examines their best practices and underlying principles, as well as the macro factors that have shaped the practices for the last ten to fifteen years, as well as what is expected in the future. Companies with the "best" S&OP processes collaborate internally to balance sales and operations, and align all internal stakeholders' interests. Furthermore, they collaborate externally with suppliers and customers to reduce supply and demand uncertainties. They also understand and manage demand and supply uncertainties, and align their effort towards their goals. These companies synchronize operations and are agile to changing environments.
by Peng Kuan Tan.
M.Eng.in Logistics

Natural gas pipeline network system is a critical infrastructure connecting gas resource and market, which is composed with the transmission pipeline system, underground gas storage (UGS) and liquefied natural gas (LNG) terminal demand. A methodology to assess the gas supply capacity and gas supply reliability of a natural gas pipeline network system is developed in this paper. Due to random failure and maintenance action of the components in the pipeline network system, the system can be in a number of operating states. The methodology is able to simulate the state transition process and the duration of each operating state based on a Monte Carlo approach. After the system transits to other states, the actual flow rate will change accordingly. The hydraulic analysis, which includes thermal-hydraulic simulation and maximum flow algorithm, is applied to analyze the change law of the actual flow rate. By combining the hydraulic analysis into the simulation of the state transition process, gas supply capacity of the pipeline network system is quantified. Furthermore, considering the uncertainty of market demand, the load duration curve (LDC) method is employed to predict the amount of demand for each consumer node. The gas supply reliability is then calculated by comparing the gas supply capacity with market demand. Finally, a detailed procedure for gas supply capacity and gas supply reliability assessment of a natural gas pipeline network system is presented, and its feasibility is confirmed with a case study. In the case study, the impact of market demand uncertainty on gas supply reliability is investigated in detail.

Abstract At present, China has a developing natural gas market, and ensuring the security of gas supply is an issue of high concern. Gas supply reliability, the natural gas pipeline system’s ability to satisfy the market demand, is determined by both supply side and demand side, and is usually adopted by the researches to measure the security of gas supply. In the previous study, the demand side is usually simplified by using load duration curve (LDC) to describe the demand, which neglects the effect of demand side management. The simplification leads to the inaccurate and unreasonable assessment of the gas supply reliability, especially in high demand situation. To overcome this deficiency and achieve a more reasonable result of gas supply reliability, this paper extends the previous study on demand side by proposing a novel method of management on natural gas demand side, and the effects of demand side management on gas supply reliability is analyzed. The management includes natural gas prediction models for different types of users, the user classification rule, and the demand adjustment model based on user classification. Firstly, An autoregressive integrated moving average (ARIMA) model and a support vector machine (SVM) model are applied to predict the natural gas demand for different types of users, such as urban gas distributor (including residential customer, commercial customer, small industrial customer), power plant, large industrial customer, and Compressed Natural Gas (CNG) station. Then, the user classification rule is built based on users’ attribute and impact of supplied gas’s interruption or reduction. Natural gas users are classified into four levels. (1) Demand Fully Satisfied; (2) Demand Slightly Reduced; (3) Demand Reduced; (4) Demand Interrupted. The user classification rule also provides the demand reduction range of different users. Moreover, the optimization model of demand adjustment is built, and the objective of the model is to maximize the amount of gas supply for each user based on the classification rule. The constraints of the model are determined by the classification rule, including the demand reduction range of different users. Finally, the improved method of gas supply reliability assessment is developed, and is applied to the case study of our previous study derived from a realistic natural gas pipeline system operated by PetroChina to analyze the effects of demand side management on natural gas pipeline system’s gas supply reliability.

Abstract Gas supply security plays a vital role in ensuring the continuity of the power generation and distribution for one of Malaysian State. Unplanned deferments at offshore facilities creates enormous impacts on gas quantity leading to loss/lowering of power generation. Such occurrences lead to value leakage and hinders the expansion strategies by non-firming of investment decisions. It therefore becomes imperative as prudent operator to sustain upstream gas supply by ensuring the security by appropriate strategies in occurrence of such events. Also, for complex facilities it is decisive to have comprehensive understanding of network characteristic, offshore supplies distributions and topology in terms of hydraulics & flow regimes from multiple fields to ensure security of gas supply to customers. This paper proposes an approach to endorse the security of gas supply during normal and ad hoc situations by aligning the relevant feeders to respective demand centers thru comprehensible network modeling and ensure the optimized system response operating envelope for such events. An innovative process was commenced to design, develop, validate, and deploy the network simulation model to cater for the technical characteristics in terms of ullage, hydraulic first principles, blending aspects and safety features during aligning of the respective feeders. The landscape includes around 100+ feeders, multiple export pipelines, several gas highways, and many demand centers with each of its specific requirement. The inline equipment such as pressure boosters with performance curves (compressors/pumps), pressure manipulators (control valves) also formed the integral portion of the model for resilient outputs. Also, the equation of state (for thermodynamic behavior) and appropriate flow co-relation (for pressure drop estimations) were embedded in the model for representative results. The model was validated thoroughly with the plant data by identifying critical junction points to have realistic consequences. Input to the model were classified as engineering input (static such as design capacity, pressure limits, maximum allowable operating pressure (MAOP)) and operational inputs (flow allocations, priority of supply, precedence in operation of demand centers). The process was looped to reallocate the feeders till the required intent is met for the supply as well as on technical aspects. The simulation model could decipher the pain points across the various intensity of the networks such as pressure choking, unintended flow distribution, violations of the resultant specifications and potential breach in the safety limitations. Several iterations could be accomplished in terms of permutations and combinations to align appropriate feeders. The scenarios could be also optimized for the optimal value ranking of the fields to be evacuated for designated demand centers. The simulation model could suggest amendments in the operating strategy such as clustering of sweet/sour fields, integrated contaminant management system, and addition of loop lines to ensure the hydrocarbon molecule travels the intended path. Also, model assisted in generating the heat maps in terms of pressure concentration, flow dispersal and other aspects to have the big picture of the asset which can be probed as required. Network Modeling could recommend the relevant swing fields or alteration in the configuration in case of unforeseen circumstances if it occurs to ensure the security of supply of gas is intact to cater necessities. The approach could recommend that the upstream security of gas supply could be enhanced or endorsed via usage of Network Modeling by either by apposite changes in the operating philosophy and/or configuration. It also resulted into nurture trust of the stakeholder to empower the power generation using gas as fuel and business continuity is ensured for upstream.

Petrobras Transporte S.A. – TRANSPETRO’s Gas Pipeline System, composed by 7.3 thousand kilometers, 135 delivery stations and 21 compressor stations, has a very seasonally dependent operation. Highly linked with the Brazilian energy grid, during the dry season of the year a large part of the 77.3 million cubic meters of natural gas daily transportation are used to generate around 6.4 gigawatts to power the country. Additionally, the ever increasing number of power plants and distribution companies around the country demand more and more gas to be offered to supply the system. Among the different sources of natural gas available, the LNG is the most flexible for such seasonal operation. In order to support this current demand and to attend future demands, the regasification ability of Baía de Guanabara LNG Terminal was increased in December 2012, by changing the regasification vessel that supplies the southeast portion of the gas pipeline network, from 14 to 20 million cubic meters per day. To prepare to receive the new ship, some tests were performed to determine the operational limits on system survival time without LNG supply during vessel exchange. This assessment involved two different issues. The ship change operation occurred during a period of high consumption, when the LNG terminal was needed to sustain the network inventory. A long period without this supply, caused by the exchange of LNG vessel, would affect the deliveries. On the other hand, the new ship’s commissioning curve would introduce a large amount of natural gas into the system during a short period of time, demanding that the deliveries absorbed such volume. Four planning scenarios were assessed based on some expected pipeline supply and delivery conditions. The work was important as a reference for future changes on operating supply units of TRANSPETRO gas pipeline system, showing the importance of pipeline simulation both as a planning tool for pipeline logistic problems and as operational support.

The dynamic performance of the Fueling Machine (F/M) Heavy Water (D2O) supply system for Wolsong Nuclear Power Plant (NPP) was evaluated using Modular Modeling System (MMS) computer code. Parametric study has been carried out to investigate the effects of dual common set pressure and the position change rate of series valve on the dynamic behavior of common header pressure and common bleed valve position during the mode changes of supply pressure. The results show that the introduction of the series valve position demand curve and the dual common header set pressure is effective to attenuate the overshoot of common header pressure during mode changes. This does not lead any adverse effects on the system performance of supply pressure control and heavy water supply to F/M during the mode changes. The dynamic evaluation results of the F/M D2O supply system will be used for the new control system parameter settings and help to relieve system operators’ burdens during the system operation.

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.