49 research outputs found
Characterisation and Flexibility Assessment of Aggregate Electrical Demand
No full textThe renewable energy sources (RES) are intermittent in their nature and their integration in electric power grid has introduced the mismatch between supply and demand. This mismatch can be leveled by using the flexibilities from the supply and the demand side. The demand side in a power system has key importance in the evolving context of the energy systems. Electrical load patterns that represent the consumption level are affected by different types of uncertainties associated with customer's behavior and with keeping acceptable comfort level. The resulting aggregated load pattern indicates the system response that may be more or less flexible in different periods of time. The distribution system operator in a microgrid is responsible for its secure and economic operation. Enhancing the knowledge on the aggregated behavior of these customers is particularly important for the distribution system operator, also with the aim of determining the potential flexibility of the demand and setting up the economic terms of the electricity provision to the customers. Extra charges due to high energy demand and contract violation penalties can be avoided using demand side flexibility. Demand side flexibility has many benefits in normal as well as emergency conditions like less cost and quick response. The study of aggregate residential demand for flexibility measures is important due to the diverse energy usage behavior of individual residents and conceptually, its availability all around the year for load management. Exploitation of possible flexibilities of the group of residential customer's behavior is considered as an important option to promote demand response programs and to achieve greater energy savings. As far as the residential sector is concerned, a reasonable work can be found in the literature to assess the flexibility for the individual appliances, the aggregation of selected appliances. However, little work is found on the aggregation of residential units. Also, despite of many discussions about the concept of flexibility, the few mathematical definitions of flexibility available do not address the variation in time of the overall demand aggregation. There is a need to develop a methodology to extract flexibility information from aggregate electricity consumption behavior of the residents and develop useful flexibility indices for the aggregate residential loads. For this purpose, the first action required is to augment availability of information about the characteristics of aggregate electricity demand. The analysis of aggregate demand patterns is carried out by considering the demand pattern data representing the average power determined from the energy referring to a given time step duration. This thesis contains a comprehensive statistical analysis to investigate the effect of time step duration and aggregation level on load variation profile. Then the customer behavior about the change is demand is modeled using the binomial probability distribution. This model has led towards some novel definitions of flexibility indices. A new method based on the Beta probability distribution has been developed to generate the time coupled aggregate residential demand patterns, whose evolution depends on the uncertainties associated with the customer's behavior. The outcome of this research work has also led towards defining the role of customers in microgrid application. For this purpose, a structure of the business model for a smart (mini) grid is proposed. The data sets used for all kind of analysis are generated for the different aggregations of the extra-urban residential customers using a bottom-up approach. The tools presented in this research work can be helpful for a system operator or an aggregator to assess demand side flexibilities, manage resources and efficiently use demand response programs. The findings of this work are also supportive to determine the metering s
A Probabilistic Approach to Study the Load Variations in Aggregated Residential Load Patterns
No full textThe demand side in a power system has key importance in the evolving context of the energy systems. Exploitation of possible flexibilities of the customer's behavior is considered as an important option to promote demand response programmes and to achieve greater energy savings. For this purpose, the first action required is to augment availability of information about consumption patterns. The electricity consumption in a residential system is highly dependent on various types of uncertainties due to the diverse lifestyle of customers. Knowledge about the aggregated behavior of residential customers is very important for the system operator or aggregator to manage load and supply side flexibilities for economic operation of the system. In this paper, the effect of sampling time is evaluated for different residential load aggregations using probabilistic approach. A binomial probability distribution model is used to extract trends in increase or decrease in demand with respect to time evolution of a typical day. For each case study scenario, confidence intervals are calculated to assess the uncertainty and randomness in load variation trends. The findings of this study will lead towards better management of demand and supply side resources in a smart grid and especially for microgrid
Future Business Model For Cellular Microgrids
No full textDifferent studies which were carried out in the past revealed that the environment for microgrids is very complex and uncertain due to regulatory and legal barriers. Across and within the developed countries the suggestions and views of regulatory authorities and legal bindings about the infrastructure and operation of microgrids are quite different. According to the present scenario, the viability of microgrids mainly depends upon how microgrids are framed, who owns them, which are the customers served from them and how much revenue is generated from them. This paper investigates the potential barriers in current business models to deploy microgrids and proposes a business model, centric to users, with the concept of consumers owned microgrid
Demand Flexibility Time Intervals for Aggregate Residential Load Patterns
No full textThis paper deals with the definition of demand flexibility time intervals. These intervals are extracted from the binomial probability model of load variation patterns with the two possible categories of increase and non-increase in demand. These intervals along with the information on the coefficient of variation of the aggregate demand are used to assess the potential of demand flexibility exhibited by the aggregate residential demand in different periods of the day. The results of the proposed approach are useful for the distribution system operator or an aggregator to effectively set up demand response programmes in suitable time slots of the day
A Statistical Analysis of Sampling Time and Load Variations for Residential Load Aggregations
No full textThe electrical load in residential systems highly depends on various types of uncertainty due to the lifestyle of the residential customers. Enhancing the knowledge on the aggregated behavior of these customers is particularly important for the distribution system operator, also with the aim of determining the potential flexibility of the residential demand and setting up the economic terms of the electricity provision to the customers. This paper addresses the impact of the sampling time interval with which the customer data are gathered on the characteristics of the aggregated electricity demand. A dedicated statistical analysis has been carried out to highlight the load variations occurring for different numbers of aggregated extra urban residential customers. The results are represented in the form of normalized percentage load variations, using the number of samples and the maximum demand variation to construct the normalizing factor. The results indicate how the sampling time interval affects the load variations for different levels of customer aggregation
Weibull distribution model for the characterization of aggregate load patterns
No full textProbabilistic Modeling of electric load is a key aspect for the study of distribution system. Characteristics of electric load patterns are extracted by using appropriate probabilistic model. Characterization of aggregated load pattern is very helpful for the system operator or aggregator at microgrid level. Inter-temporal evaluation of electric load patterns is a challenging task. Intertemporal load patterns behavior of residential consumers are extracted by using Weibull distribution and generalized regression neural network. Weibull distribution based probabilistic model with neural network is used for the generation of load patterns from the characteristics extracted from the reference load patterns. Generated load patterns are useful for the scenario analysis, offline testing of power system, distributed generation studies, analysis of equipment before installation. Goodness of Fit (GOF) indicators are used for calculating the accuracy and validation of proposed probabilistic model
Effect of Aggregation Level and Sampling Time on Load Variation Profile – A Statistical Analysis
No full textElectrical load patterns that represent the consumption level are affected by different types of uncertainties associated with customer’s behavior and with keeping acceptable comfort level. The resulting aggregated load pattern indicates the system response that may be more or less flexible in different periods of time. Many research activities have been dedicated to explore the flexibility of load demand using load patterns and associated uncertainties but little work is found on investigating the effect of sampling time and aggregation level on the shape of the load patterns. Knowing the characteristics of the electrical load patterns is a key aspect to manage load and supply side flexibilities for most economic system operation. This paper addresses the effects of sampling interval as well as aggregation level on the characteristics of the aggregated load patterns. The study is carried out on the basis of comprehensive statistical computations on collected data using load variation profiles, because these profiles embed the information on the load variation trend. The findings of this study may be used for load forecasting and management, generation allocation and economic operation of smart grid system, especially for microgrids
Potential of Residential Demand Flexibility - Italian Scenario
No full textAggregator in a microgrid is responsible for its secure and economic operation. As far as system economics is concerned, there are many factors upon which energy cost is dependent, for example peak demand rates and penalties due to violations in energy purchase contracts. Extra charges due to high energy demand and contract violation penalties can be avoided using demand side flexibility. Demand side flexibility has many benefits in normal as well as emergency conditions like less cost and quick response. Residential loads are the major part to be supplied and have 7 days and 24 hour availability for flexible operation. This paper presents the potential for effective use of demand flexibility from residential customers for peak reduction. Demand flexibilities are calculated with Monte Carlo Simulation using probabilistic data of Italian households. Different scenarios are generated to demonstrate the effectiveness of flexibility in residential sector
Definitions of Demand Flexibility for Aggregate Residential Loads
No full textNowadays, enhanced knowledge on the nature of the electricity demand is achieved through the progressively increasing deployment of smart meters and advanced data analysis techniques. One of the major challenges is to exploit this knowledge to support the introduction of strategies to modify the demand according to relevant objectives to be achieved, like users' participation in demand response programmes. A key point for facing this challenge is to characterize the demand flexibility. In spite of many discussions about the concept of flexibility, the few mathematical definitions of flexibility available do not address the variation in time of the overall demand aggregation. This paper starts from the analysis of time-variable patterns of aggregate residential customers, ending up with suitable definitions of expected flexibility for aggregate demand. These definitions are based on assessing positive and negative pattern variations and are identified from the analysis of the collective behavior of the aggregate users. A set of results are shown for different numbers of aggregate customers, by considering different values of the averaging time step for load pattern representation
A Conceptual Framework for the Business Model of Smart Grids
No full textIn spite of the strong believes, the progress and maturity in the deployment of smart grids is very slow. The main reasons behind the slow pace are not due to the technical aspects but, perhaps, as a result of an inappropriate business model, not adequately centered to the customers. Nature, with ability to survive and resist, is a splendid example of competitive life. Mathematical models show that natural complex systems are the replication of simple structures. Exploring this approach, smart grids can be seen as an aggregation of mini and micro grids, with the smart user as fundamental stone. The technical aggregation can be paired with a new business model, based on correspondent enterprises, devoted to operation and maintenance. This framework can open new opportunities, favoring innovations, increasing resilience, pushing toward more responsible energy usage behavior while giving tangible economic and social benefits to the customers
