Energy Management of Prosumer Communities

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably in the available electricity markets. Significant untapped potential remains in the exploitation and coordination of small and medium-sized distributed energy resources. However, such resources usually have a primary purpose, which imposes constraints on the exploitation of the resource; for example, the primary purpose of an electric vehicle battery is for driving, so the battery could be used as temporary storage for excess photovoltaic energy only if the vehicle is available for driving when the owner expects it to be. The aggregation of several distributed energy resources is a solution for coping with the unavailability of one resource. Solutions are needed for managing the electricity production and consumption characteristics of diverse distributed energy resources in order to obtain prosumers with more generic capabilities and services for electricity production, storage, and consumption. This collection of articles studies such prosumers and the emergence of prosumer communities. Demand response-capable smart loads, battery storages and photovoltaic generation resources are forecasted and optimized to ensure energy-efficient and, in some cases, profitable operation of the resources

Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation

Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques. A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor. A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs. This methodology exploits the limited training data more efficiently. In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions. So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings. Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%

Maintenance Optimization and Inspection Planning of Wind Energy Assets: Models, Methods and Strategies

Designing cost-effective inspection and maintenance programmes for wind energy farms is a complex task involving a high degree of uncertainty due to diversity of assets and their corresponding damage mechanisms and failure modes, weather-dependent transport conditions, unpredictable spare parts demand, insufficient space or poor accessibility for maintenance and repair, limited availability of resources in terms of equipment and skilled manpower, etc. In recent years, maintenance optimization has attracted the attention of many researchers and practitioners from various sectors of the wind energy industry, including manufacturers, component suppliers, maintenance contractors and others. In this paper, we propose a conceptual classification framework for the available literature on maintenance policy optimization and inspection planning of wind energy systems and structures (turbines, foundations, power cables and electrical substations). The developed framework addresses a wide range of theoretical and practical issues, including the models, methods, and the strategies employed to optimise maintenance decisions and inspection procedures in wind farms. The literature published to date on the subject of this article is critically reviewed and several research gaps are identified. Moreover, the available studies are systematically classified using different criteria and some research directions of potential interest to operational researchers are highlighted

Sizing and control of a Hybrid hydro-battery-flywheel storage system for frequency regulation services

Energy security and environmental challenges are some of the drivers for increasing the electricity generation from non-programmable Renewable Energy Source (RES), adding pressure to the grid, especially if located in weakly connected (or isolated) islands, like Sardinia. Variable-speed Pumped Storage Hydro Power (PSHP) can offer a high degree of flex ibility in providing ancillary services (namely primary and secondary regulations), but due to the hydromechanical nature of the equipment, sudden variations in the power output cause wear and tear. Other energy storage devices can not compete with PSHP in terms of energy and power availability. This work aims to assess the potential benefits derived from the hybridization of a PSHP with Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS) in providing frequency regulation services to the grid of the Sardinia Island (Italy). The focus of the study tries to cross both the plant owner point of view, whose aim is to have a smooth PSHP operation and the economic incentive to hybridize the plant, and the Transmission System Operator’s, whose aim is to have a fast reacting plant that better stabilizes the grid frequency. This is done by simulations of a detailed dynamic model of the PSHP, whose hydraulic machine has been characterized from real experimental data, the BESS and the FESS. Moreover, two power management strategies are presented, based on different criteria, to effectively coordinate the devices making up the Hybrid Energy Storage System (HESS). First the simulations are performed open-loop, to assess the impact of various combinations of installed BESS and FESS powers over the wear and tear of the equipment. Later the model is used in an optimization procedure to find the combination of installed BESS and FESS powers and the respective controlparameters that would guarantee the maximum economic return at the end of the investment life. Last, the model is included into a Sardinian power system model and simulated in a future scenario with high RES penetration, assessing the plant capabilities to effectively contain and restore the frequency. Results show that there is not a catch-all solution in terms of hybridization and that a trade-off must be made between the plant owner’s urge to smoothly operate the plant in order to reduce the equipment degradation, and the TSO’s objective to have fast responsive plants providing high quality frequency regulation services. If on one hand open-loop simulations show that the hybridization reduce the main wear and tear indicators, on the other the optimal hybrid system limits the plant ability to contain the frequency excursions in closed-loop simulations, as the optimization problem was formulated over the plant owner’s interests. The results show that there much potential for frequency stabilization and wear and tear reduction, but more techno-economic data is required to fully investigate the benefits of this configuration.Energy security and environmental challenges are some of the drivers for increasing the electricity generation from non-programmable Renewable Energy Source (RES), adding pressure to the grid, especially if located in weakly connected (or isolated) islands, like Sardinia. Variable-speed Pumped Storage Hydro Power (PSHP) can offer a high degree of flex ibility in providing ancillary services (namely primary and secondary regulations), but due to the hydromechanical nature of the equipment, sudden variations in the power output cause wear and tear. Other energy storage devices can not compete with PSHP in terms of energy and power availability. This work aims to assess the potential benefits derived from the hybridization of a PSHP with Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS) in providing frequency regulation services to the grid of the Sardinia Island (Italy). The focus of the study tries to cross both the plant owner point of view, whose aim is to have a smooth PSHP operation and the economic incentive to hybridize the plant, and the Transmission System Operator’s, whose aim is to have a fast reacting plant that better stabilizes the grid frequency. This is done by simulations of a detailed dynamic model of the PSHP, whose hydraulic machine has been characterized from real experimental data, the BESS and the FESS. Moreover, two power management strategies are presented, based on different criteria, to effectively coordinate the devices making up the Hybrid Energy Storage System (HESS). First the simulations are performed open-loop, to assess the impact of various combinations of installed BESS and FESS powers over the wear and tear of the equipment. Later the model is used in an optimization procedure to find the combination of installed BESS and FESS powers and the respective controlparameters that would guarantee the maximum economic return at the end of the investment life. Last, the model is included into a Sardinian power system model and simulated in a future scenario with high RES penetration, assessing the plant capabilities to effectively contain and restore the frequency. Results show that there is not a catch-all solution in terms of hybridization and that a trade-off must be made between the plant owner’s urge to smoothly operate the plant in order to reduce the equipment degradation, and the TSO’s objective to have fast responsive plants providing high quality frequency regulation services. If on one hand open-loop simulations show that the hybridization reduce the main wear and tear indicators, on the other the optimal hybrid system limits the plant ability to contain the frequency excursions in closed-loop simulations, as the optimization problem was formulated over the plant owner’s interests. The results show that there much potential for frequency stabilization and wear and tear reduction, but more techno-economic data is required to fully investigate the benefits of this configuration

A Stacked GRU-RNN-based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This paper presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both uni-variate and multi-variate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation

Clusterwise regression and market segmentation : developments and applications

The present work consists of two major parts. In the first part the literature on market segmentation is reviewed, in the second part a set of new methods for market segmentation are developed and applied.Part 1 starts with a discussion of the segmentation concept, and proceeds with a discussion on marketing strategies for segmented markets. A number of criteria for effective segmentation are summarized. Next, two major streams of segmentation research are identified on the basis of their theoretical foundation, which is either of a microeconomic or of a behavioral science nature. These two streams differ according to both the bases and the methods used for segmenting markets.After a discussion of the segmentation bases that have been put forward as the normative ideal but have been applied in practice very little, different bases are classified into four categories, according to their being observable or unobservable, and general or product- specific. The bases in each of the four categories are reviewed and discussed in terms of the criteria for effective segmentation. Product benefits are identified as one of the most effective bases by these criteria.Subsequently, the statistical methods available for segmentation are discussed, according to a classification into four categories, being either a priori or post hoc, and either descriptive or predictive. Post hoc (clustering) methods are appealing because they deal adequately with the complexity of markets, while the predictive methods within this class (AID, clusterwise regression) combine this advantage with prediction of purchase (predisposition).Within the two major segmentation streams, segmentation methods have been developed that are specifically tailored to the segmentation problems at hand. These are discussed. For the microeconomic school focus is upon recently developed latent class approaches that simultaneously estimate consumer segments and market characteristics (market shares, switching, elasticities) within these segments. For the behavioral science school focus is on benefit segmentation. Disadvantages of the traditional two-stage approach, in which consumers are clustered into segments on the basis of benefit importances estimated at the individual level, are revealed and procedures that have been addressed to one or more of these problems are reviewed.In Part 2, three new methods for benefit segmentation are developed: clusterwise regression, fuzzy clusterwise regression and generalized fuzzy clusterwise regression.The first method is a clustering method that simultaneously groups consumers in a number of nonoverlapping segments, and estimates the benefit importances within segments. The performance of the algorithm on synthetic data is investigated in a Monte Carlo study. Empirically, the method is shown to outperform the two-stage procedure. Special attention is paid to significance testing with Monte Carlo test procedures, and convergence to local optima. An application to segmentation of the meat-market in the Netherlands on the basis of data on elderly peoples preferences for meat products is given. Three segments are identified. The first segment weights sensory quality against exclusiveness (price), in the second segment quality is traded off against fatness. This segment, comprising predominantly of females, had the best knowledge of nutrition. In the third segment preference is based on quality only. Regional differences were identified among segments.Fuzzy clusterwise regression extends clusterwise regression in that it allows consumers to be a member of more than one segment. It simultaneously estimates the preference functions within segments, as well as the degree of membership of consumers in those segments. Using synthetic data, the performance of the method is evaluated. Empirical comparisons with two other methods are provided, and the cross-validity of the method with respect to classification and prediction is assessed. Attention is given in particular to the selection of the appropriate number of segments, the setting of the user defined fuzzy weight parameter, and Monte Carlo significance test procedures. An application to data on preferences for meatproducts used on bread in the Netherlands revealed three segments. In the first segment, taste and fitness for common use are important. In the second segment, taste overridingly determines preference, but products that are considered more exclusive and natural and less fat and salt are also preferred. In segment three the health related product benefits are even more important. The importance of taste decreases from segment one to three, while the importance of health-related aspects increases in that direction. The health oriented segments comprised more females, older people and people who attributed causality of their behavior more to themselves.The method was also applied to data on consumers image for stores that sell meat. Again three segments were revealed. The value shoppers, trade off quality and price.They come from smaller families and spend less on meat. In the largest segment store image is based upon product quality. Females have higher membership in this segment, that is more involved with the store where they buy meat. For service shoppers, both service and atmosphere are important. This segment tends to be more store-loyal.Next, a generalization of fuzzy clusterwise regression is proposed, which incorporates both benefit segmentation and market structuring within the framework of preference analysis. The method simultaneously estimates the preference functions within each of a number of clusters, and the parameters indicating the degree of membership of both subjects and products in these clusters. The performance of this method is assessed in a Monte Carlo study on synthetic data. The method is compared empirically with clusterwise regression and fuzzy clusterwise regression. The significance testing with Monte Carlo test procedures, and the selection of the fuzzy weight parameters is treated in detail. Two segments were revealed in an analysis of consumer preferences of butter and margarine brands. The segments differed mainly in the importance attached to exclusiveness and fitness for multiple purposes. The brands competing within these segments were revealed. Females and consumers with a higher socioeconomic status had higher memberships in the segments in which exclusiveness was important.Finally, the clusterwise regression methods developed in this work are compared with other recently developed procedures in terms of the assumptions involved. The substantive results obtained in the empirical studies concerning foods are summarized and their implications for future research are given. The implications and the contribution of the methods to the development of marketing strategies for segmented markets are discussed

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice