Distributed Generation and Demand Response Dispatch for a Virtual Power Player Energy and Reserve Provision

Recent changes in the operation and planning of power systems have been motivated by the introduction of Distributed Generation (DG) and Demand Response (DR) in the competitive electricity markets' environment, with deep concerns at the efficiency level. In this context, grid operators, market operators, utilities and consumers must adopt strategies and methods to take full advantage of demand response and distributed generation. This requires that all the involved players consider all the market opportunities, as the case of energy and reserve components of electricity markets. The present paper proposes a methodology which considers the joint dispatch of demand response and distributed generation in the context of a distribution network operated by a virtual power player. The resources' participation can be performed in both energy and reserve contexts. This methodology contemplates the probability of actually using the reserve and the distribution network constraints. Its application is illustrated in this paper using a 32-bus distribution network with 66 DG units and 218 consumers classified into 6 types of consumers.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Demand Response Programs Definition Using Demand Price Elasticity to Define Consumers Aggregation for an Improved Remuneration Structure

The positioning of the consumers in the power systems operation has been changed in the recent years, namely due to the implementation of competitive electricity markets. Demand response is an opportunity for the consumers’ participation in electricity markets. Smart grids can give an important support for the integration of demand response. The methodology proposed in the present paper aims to create an improved demand response program definition and remuneration scheme for aggregated resources. The consumers are aggregated in a certain number of clusters, each one corresponding to a distinct demand response program, according to the economic impact of the resulting remuneration tariff. The knowledge about the consumers is obtained from its demand price elasticity values. The illustrative case study included in the paper is based on a 218 consumers’ scenario.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation Platform

Multi-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.

A consumer research project as the basis for the development of a marketing plan for a new spirit brand producing gin in Germany

This work project is about developing a marketing plan for a new gin brand in Germany. It is based on consumer and market research, including Portugal as a trend market for the qualitative research. For the undertaking it is seen as fundamental to understand the industry as well as the consumer needs, attitudes and preferences. Furthermore, it is important to consider the estimation of opinion leaders and trendsetters in the industry. In this context it turned out that barkeepers have a key-influencing role for the stimulation of demand. Based on the insights from this research as well as on the gained market knowledge, the marketing plan was developed. The goal is to convince other brand users to switch brands.

Utilisation de la coronarographie en Suisse: une étude de population. [The use of coronary vessel radiography in Switzerland: a population study].

All patients having undergone a coronarography during 1984 have been surveyed in Switzerland. This retrospective study has used existing data in the 13 centers practicing this diagnostic procedure. 4921 coronarographies were carried out in 1984, amongst 4359 patients. In terms of population-based rates, the national figures are 77 procedures/100,000 residents, and 68 patients/100,000 residents. Female rates are one fourth of the male rates (27/100,000 versus 112/100,000). For both sexes, the highest utilization rates are for the age groups 60-64. Swiss figures are relatively low when compared with other developed countries. However, patterns of utilization are very different within the country: according to the Canton of residence of the patient, the utilization rates (standardized for age and sex) vary from 8/100,000 to 160/100,000. There is a distinct gradient from south-west to north-east, which closely corresponds to the distribution of centers practicing the procedure. More intriguing is the fact that cardiovascular mortality shows an inverse geographical gradient, with the highest mortality in Cantons having the lowest rate of coronarography. Various reasons for the observed variations are discussed, in relation with differences in supply of diagnostic and therapeutic equipments, but also in relation with various patterns of demand related to differential morbidity rates and/or differential patterns of clinical decision.

Ambiguity on the insurer's side: the demand for insurance

Empirical evidence suggests that ambiguity is prevalent in insurance pricing and underwriting, and that often insurers tend to exhibit more ambiguity than the insured individuals (e.g., [23]). Motivated by these findings, we consider a problem of demand for insurance indemnity schedules, where the insurer has ambiguous beliefs about the realizations of the insurable loss, whereas the insured is an expected-utility maximizer. We show that if the ambiguous beliefs of the insurer satisfy a property of compatibility with the non-ambiguous beliefs of the insured, then there exist optimal monotonic indemnity schedules. By virtue of monotonicity, no ex-post moral hazard issues arise at our solutions (e.g., [25]). In addition, in the case where the insurer is either ambiguity-seeking or ambiguity-averse, we show that the problem of determining the optimal indemnity schedule reduces to that of solving an auxiliary problem that is simpler than the original one in that it does not involve ambiguity. Finally, under additional assumptions, we give an explicit characterization of the optimal indemnity schedule for the insured, and we show how our results naturally extend the classical result of Arrow [5] on the optimality of the deductible indemnity schedule.

Ambiguity on the insurer's side: the demand for insurance

Empirical evidence suggests that ambiguity is prevalent in insurance pricing and underwriting, and that often insurers tend to exhibit more ambiguity than the insured individuals (e.g., [23]). Motivated by these findings, we consider a problem of demand for insurance indemnity schedules, where the insurer has ambiguous beliefs about the realizations of the insurable loss, whereas the insured is an expected-utility maximizer. We show that if the ambiguous beliefs of the insurer satisfy a property of compatibility with the non-ambiguous beliefs of the insured, then there exist optimal monotonic indemnity schedules. By virtue of monotonicity, no ex-post moral hazard issues arise at our solutions (e.g., [25]). In addition, in the case where the insurer is either ambiguity-seeking or ambiguity-averse, we show that the problem of determining the optimal indemnity schedule reduces to that of solving an auxiliary problem that is simpler than the original one in that it does not involve ambiguity. Finally, under additional assumptions, we give an explicit characterization of the optimal indemnity schedule for the insured, and we show how our results naturally extend the classical result of Arrow [5] on the optimality of the deductible indemnity schedule.

Development of Best Practices for Ship Recycling Processes

Ship recycling has been considered as the best means to dispose off an obsolete ship. The current state of art of technology combined with the demands of sustainable developments from the global maritime industrial sector has modified the status of erstwhile ‘ship breaking’ involving ship scrap business to a modern industry undertaking dismantling of ships and recycling/reusing the dismantled products in a supply chain of pre owned product market by following the principles of recycling. Industries will have to formulate a set of best practices and blend them with the engineering activities for producing better quality products, improving the productivity and for achieving improved performances related to sustainable development. Improved performance by industries in a sustainable development perspective is accomplished only by implementing the 4E principles, ie.,. ecofriendliness, engineering efficiency, energy conservation and ergonomics in their core operations. The present study has done a comprehensive investigation into various ship recycling operations for formulating a set of best practices.Being the ultimate life cycle stage of a ship, ship recycling activities incorporate certain commercial procedures well in advance to facilitate the objectives of dismantling and recycling/reusing of various parts of the vessel. Thorough knowledge regarding these background procedures in ship recycling is essential for examining and understanding the industrial business operations associated with it. As a first step, the practices followed in merchant shipping operations regarding the decision on decommissioning have been and made available in the thesis. Brief description about the positioning methods and important preparations for the most feasible ship recycling method ie.,. beach method have been provided as a part of the outline of the background information. Available sources of guidelines, codes and rules & regulations for ship recycling have been compiled and included in the discussion.Very brief summary of practices in major ship recycling destinations has been prepared and listed for providing an overview of the global ship recycling activities. The present status of ship recycling by treating it as a full fledged engineering industry has been brought out to establish the need for looking into the development of the best practices. Major engineering attributes of ship as a unique engineering product and the significant influencing factors on her life cycle stage operations have been studied and added to the information base on ship recycling. Role of ship recycling industry as an important player in global sustainable development efforts has been reviewed by analysing the benefits of ship recycling. A brief synopsis on the state of art of ship recycling in major international ship recycling centres has also been incorporated in the backdrop knowledgebase generation on ship recycling processes.Publications available in this field have been reviewed and classified into five subject categories viz., Infrastructure for recycling yards and methods of dismantling, Rules regarding ship recycling activities, Environmental and safety aspects of ship recycling, Role of naval architects and ship classification societies, Application of information technology and Demand forecasting. The inference from the literature survey have been summarised and recorded. Noticeable observations in the inference include need of creation of a comprehensive knowledgebase on ship recycling and its effective implementation in the industry and the insignificant involvement of naval architects and shipbuilding engineers in ship recycling industry. These two important inferences and the message conveyed by them have been addressed with due importance in the subsequent part of the present study.As a part of the study the importance of demand forecasting in ship recycling has been introduced and presented. A sample input for ship recycling data for implementation of computer based methods of demand forecasting has been presented in this section of the thesis.The interdisciplinary nature of engineering processes involved in ship recycling has been identified as one of the important features of this industry. The present study has identified more than a dozen major stake holders in ship recycling having their own interests and roles. It has also been observed that most of the ship recycling activities is carried out in South East Asian countries where the beach based ship recycling is done in yards without proper infrastructure support. A model of beach based ship recycling has been developed and the roles, responsibilities and the mutual interactions of the elements of the system have been documented as a part of the study Subsequently the need of a generation of a wide knowledgebase on ship recycling activities as pointed out by the literature survey has been addressed. The information base and source of expertise required to build a broad knowledgebase on ship recycling operations have been identified and tabulated. Eleven important ship recycling processes have been identified and a brief sketch of steps involved in these processes have been examined and addressed in detail. Based on these findings, a detailed sequential disassembly process plan of ship recycling has been prepared and charted. After having established the need of best practices in ship recycling initially, the present study here identifies development of a user friendly expert system for ship recycling process as one of the constituents of the proposed best practises. A user friendly expert system has been developed for beach based ship recycling processes and is named as Ship Recycling Recommender (SRR). Two important functions of SRR, first one for the ‘Administrators’, the stake holders at the helm of the ship recycling affairs and second one for the ‘Users’, the stake holders who execute the actual dismantling have been presented by highlighting the steps involved in the execution of the software. The important output generated, ie.,. recommended practices for ship dismantling processes and safe handling information on materials present onboard have been presented with the help of ship recycling reports generated by the expert system. A brief account of necessity of having a ship recycling work content estimation as part of the best practices has been presented in the study. This is supported by a detailed work estimation schedule for the same as one of the appendices.As mentioned earlier, a definite lack of involvement of naval architect has been observed in development of methodologies for improving the status of ship recycling industry. Present study has put forward a holistic approach to review the status of ship recycling not simply as end of life activity of all ‘time expired’ vessels, but as a focal point of integrating all life cycle activities. A new engineering design philosophy targeting sustainable development of marine industrial domain, named design for ship recycling has been identified, formulated and presented. A new model of ship life cycle has been proposed by adding few stages to the traditional life cycle after analysing their critical role in accomplishing clean and safe end of life and partial dismantling of ships. Two applications of design for ship recycling viz, recyclability of ships and her products and allotment of Green Safety Index for ships have been presented as a part of implementation of the philosophy in actual practice.

ON (s,S) inventory policy with/without retrial and interruption of services/production

In this thesis we have developed a few inventory models in which items are served to the customers after a processing time. This leads to a queue of demand even when items are available. In chapter two we have discussed a problem involving search of orbital customers for providing inventory. Retrial of orbital customers was also considered in that chapter; in chapter 5 also we discussed retrial inventory model which is sans orbital search of customers. In the remaining chapters (3, 4 and 6) we did not consider retrial of customers, rather we assumed the waiting room capacity of the system to be arbitrarily large. Though the models in chapters 3 and 4 differ only in that in the former we consider positive lead time for replenishment of inventory and in the latter the same is assumed to be negligible, we arrived at sharper results in chapter 4. In chapter 6 we considered a production inventory model with production time distribution for a single item and that of service time of a customer following distinct Erlang distributions. We also introduced protection of production and service stages and investigated the optimal values of the number of stages to be protected.

Performance of Supply Chain when using selected Coordination Mechanisms

Coordination among supply chain members is essential for better supply chain performance. An effective method to improve supply chain coordination is to implement proper coordination mechanisms. The primary objective of this research is to study the performance of a multi-level supply chain while using selected coordination mechanisms separately, and in combination, under lost sale and back order cases. The coordination mechanisms used in this study are price discount, delay in payment and different types of information sharing. Mathematical modelling and simulation modelling are used in this study to analyse the performance of the supply chain using these mechanisms. Initially, a three level supply chain consisting of a supplier, a manufacturer and a retailer has been used to study the combined effect of price discount and delay in payment on the performance (profit) of supply chain using mathematical modelling. This study showed that implementation of individual mechanisms improves the performance of the supply chain compared to ‘no coordination’. When more than one mechanism is used in combination, performance in most cases further improved. The three level supply chain considered in mathematical modelling was then extended to a three level network supply chain consisting of a four retailers, two wholesalers, and a manufacturer with an infinite part supplier. The performance of this network supply chain was analysed under both lost sale and backorder cases using simulation modelling with the same mechanisms: ‘price discount and delay in payment’ used in mathematical modelling. This study also showed that the performance of the supply chain is significantly improved while using combination of mechanisms as obtained earlier. In this study, it is found that the effect (increase in profit) of ‘delay in payment’ and combination of ‘price discount’ & ‘delay in payment’ on SC profit is relatively high in the case of lost sale. Sensitivity analysis showed that order cost of the retailer plays a major role in the performance of the supply chain as it decides the order quantity of the other players in the supply chain in this study. Sensitivity analysis also showed that there is a proportional change in supply chain profit with change in rate of return of any player. In the case of price discount, elasticity of demand is an important factor to improve the performance of the supply chain. It is also found that the change in permissible delay in payment given by the seller to the buyer affects the SC profit more than the delay in payment availed by the buyer from the seller. In continuation of the above, a study on the performance of a four level supply chain consisting of a manufacturer, a wholesaler, a distributor and a retailer with ‘information sharing’ as coordination mechanism, under lost sale and backorder cases, using a simulation game with live players has been conducted. In this study, best performance is obtained in the case of sharing ‘demand and supply chain performance’ compared to other seven types of information sharing including traditional method. This study also revealed that effect of information sharing on supply chain performance is relatively high in the case of lost sale than backorder. The in depth analysis in this part of the study showed that lack of information sharing need not always be resulting in bullwhip effect. Instead of bullwhip effect, lack of information sharing produced a huge hike in lost sales cost or backorder cost in this study which is also not favorable for the supply chain. Overall analysis provided the extent of improvement in supply chain performance under different cases. Sensitivity analysis revealed useful insights about the decision variables of supply chain and it will be useful for the supply chain management practitioners to take appropriate decisions.

From evolution to co-evolution – An empirical study of the role of innovation, policy and public research in industrial dynamics

In the past decades since Schumpeter’s influential writings economists have pursued research to examine the role of innovation in certain industries on firm as well as on industry level. Researchers describe innovations as the main trigger of industry dynamics, while policy makers argue that research and education are directly linked to economic growth and welfare. Thus, research and education are an important objective of public policy. Firms and public research are regarded as the main actors which are relevant for the creation of new knowledge. This knowledge is finally brought to the market through innovations. What is more, policy makers support innovations. Both actors, i.e. policy makers and researchers, agree that innovation plays a central role but researchers still neglect the role that public policy plays in the field of industrial dynamics. Therefore, the main objective of this work is to learn more about the interdependencies of innovation, policy and public research in industrial dynamics. The overarching research question of this dissertation asks whether it is possible to analyze patterns of industry evolution – from evolution to co-evolution – based on empirical studies of the role of innovation, policy and public research in industrial dynamics. This work starts with a hypothesis-based investigation of traditional approaches of industrial dynamics. Namely, the testing of a basic assumption of the core models of industrial dynamics and the analysis of the evolutionary patterns – though with an industry which is driven by public policy as example. Subsequently it moves to a more explorative approach, investigating co-evolutionary processes. The underlying questions of the research include the following: Do large firms have an advantage because of their size which is attributable to cost spreading? Do firms that plan to grow have more innovations? What role does public policy play for the evolutionary patterns of an industry? Are the same evolutionary patterns observable as those described in the ILC theories? And is it possible to observe regional co-evolutionary processes of science, innovation and industry evolution? Based on two different empirical contexts – namely the laser and the photovoltaic industry – this dissertation tries to answer these questions and combines an evolutionary approach with a co-evolutionary approach. The first chapter starts with an introduction of the topic and the fields this dissertation is based on. The second chapter provides a new test of the Cohen and Klepper (1996) model of cost spreading, which explains the relationship between innovation, firm size and R&D, at the example of the photovoltaic industry in Germany. First, it is analyzed whether the cost spreading mechanism serves as an explanation for size advantages in this industry. This is related to the assumption that the incentives to invest in R&D increase with the ex-ante output. Furthermore, it is investigated whether firms that plan to grow will have more innovative activities. The results indicate that cost spreading serves as an explanation for size advantages in this industry and, furthermore, growth plans lead to higher amount of innovative activities. What is more, the role public policy plays for industry evolution is not finally analyzed in the field of industrial dynamics. In the case of Germany, the introduction of demand inducing policy instruments stimulated market and industry growth. While this policy immediately accelerated market volume, the effect on industry evolution is more ambiguous. Thus, chapter three analyzes this relationship by considering a model of industry evolution, where demand-inducing policies will be discussed as a possible trigger of development. The findings suggest that these instruments can take the same effect as a technical advance to foster the growth of an industry and its shakeout. The fourth chapter explores the regional co-evolution of firm population size, private-sector patenting and public research in the empirical context of German laser research and manufacturing over more than 40 years from the emergence of the industry to the mid-2000s. The qualitative as well as quantitative evidence is suggestive of a co-evolutionary process of mutual interdependence rather than a unidirectional effect of public research on private-sector activities. Chapter five concludes with a summary, the contribution of this work as well as the implications and an outlook of further possible research.