Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

Dynamic load management in a smart home to participate in demand response events

In future power systems, in the smart grid and microgrids operation paradigms, consumers can be seen as an energy resource with decentralized and autonomous decisions in the energy management. It is expected that each consumer will manage not only the loads, but also small generation units, heating systems, storage systems, and electric vehicles. Each consumer can participate in different demand response events promoted by system operators or aggregation entities. This paper proposes an innovative method to manage the appliances on a house during a demand response event. The main contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources’ management in a demand response event, mainly the ones of long duration, by changing the priorities of loads during the event. A case study with two scenarios is presented considering a demand response with 30 min duration, and another with 240 min (4 h). In both simulations, the demand response event proposes the power consumption reduction during the event. A total of 18 loads are used, including real and virtual ones, controlled by the presented house management system.

Contextual Intelligent Load Management Considering Real-Time Pricing in a Smart Grid Environment

The use of demand response programs enables the adequate use of resources of small and medium players, bringing high benefits to the smart grid, and increasing its efficiency. One of the difficulties to proceed with this paradigm is the lack of intelligence in the management of small and medium size players. In order to make demand response programs a feasible solution, it is essential that small and medium players have an efficient energy management and a fair optimization mechanism to decrease the consumption without heavy loss of comfort, making it acceptable for the users. This paper addresses the application of real-time pricing in a house that uses an intelligent optimization module involving artificial neural networks.

Long-term nutrient load management and lake restoration: Case of Säkylän Pyhäjärvi (SW Finland)

Eutrophication caused by anthropogenic nutrient pollution has become one of the most severe threats to water bodies. Nutrients enter water bodies from atmospheric precipitation, industrial and domestic wastewaters and surface runoff from agricultural and forest areas. As point pollution has been significantly reduced in developed countries in recent decades, agricultural non-point sources have been increasingly identified as the largest source of nutrient loading in water bodies. In this study, Lake Säkylän Pyhäjärvi and its catchment are studied as an example of a long-term, voluntary-based, co-operative model of lake and catchment management. Lake Pyhäjärvi is located in the centre of an intensive agricultural area in southwestern Finland. More than 20 professional fishermen operate in the lake area, and the lake is used as a drinking water source and for various recreational activities. Lake Pyhäjärvi is a good example of a large and shallow lake that suffers from eutrophication and is subject to measures to improve this undesired state under changing conditions. Climate change is one of the most important challenges faced by Lake Pyhäjärvi and other water bodies. The results show that climatic variation affects the amounts of runoff and nutrient loading and their timing during the year. The findings from the study area concerning warm winters and their influences on nutrient loading are in accordance with the IPCC scenarios of future climate change. In addition to nutrient reduction measures, the restoration of food chains (biomanipulation) is a key method in water quality management. The food-web structure in Lake Pyhäjärvi has, however, become disturbed due to mild winters, short ice cover and low fish catch. Ice cover that enables winter seining is extremely important to the water quality and ecosystem of Lake Pyhäjärvi, as the vendace stock is one of the key factors affecting the food web and the state of the lake. New methods for the reduction of nutrient loading and the treatment of runoff waters from agriculture, such as sand filters, were tested in field conditions. The results confirm that the filter technique is an applicable method for nutrient reduction, but further development is needed. The ability of sand filters to absorb nutrients can be improved with nutrient binding compounds, such as lime. Long-term hydrological, chemical and biological research and monitoring data on Lake Pyhäjärvi and its catchment provide a basis for water protection measures and improve our understanding of the complicated physical, chemical and biological interactions between the terrestrial and aquatic realms. In addition to measurements carried out in field conditions, Lake Pyhäjärvi and its catchment were studied using various modelling methods. In the calibration and validation of models, long-term and wide-ranging time series data proved to be valuable. Collaboration between researchers, modellers and local water managers further improves the reliability and usefulness of models. Lake Pyhäjärvi and its catchment can also be regarded as a good research laboratory from the point of view of the Baltic Sea. The main problem in both of them is eutrophication caused by excess nutrients, and nutrient loading has to be reduced – especially from agriculture. Mitigation measures are also similar in both cases.

Increasing Utilization of US Electric Grids via Smart Technologies: Integration of Load Management, Real Time Pricing, Renewables, EVs and Smart Grid Sensors

Electric power grids throughout the world suffer from serious inefficiencies associated with under-utilization due to demand patterns, engineering design and load following approaches in use today. These grids consume much of the world’s energy and represent a large carbon footprint. From material utilization perspectives significant hardware is manufactured and installed for this infrastructure often to be used at less than 20-40% of its operational capacity for most of its lifetime. These inefficiencies lead engineers to require additional grid support and conventional generation capacity additions when renewable technologies (such as solar and wind) and electric vehicles are to be added to the utility demand/supply mix. Using actual data from the PJM [PJM 2009] the work shows that consumer load management, real time price signals, sensors and intelligent demand/supply control offer a compelling path forward to increase the efficient utilization and carbon footprint reduction of the world’s grids. Underutilization factors from many distribution companies indicate that distribution feeders are often operated at only 70-80% of their peak capacity for a few hours per year, and on average are loaded to less than 30-40% of their capability. By creating strong societal connections between consumers and energy providers technology can radically change this situation. Intelligent deployment of smart sensors, smart electric vehicles, consumer-based load management technology very high saturations of intermittent renewable energy supplies can be effectively controlled and dispatched to increase the levels of utilization of existing utility distribution, substation, transmission, and generation equipment. The strengthening of these technology, society and consumer relationships requires rapid dissemination of knowledge (real time prices, costs & benefit sharing, demand response requirements) in order to incentivize behaviors that can increase the effective use of technological equipment that represents one of the largest capital assets modern society has created.

Proceedings of the Conference on the Challenge of Load Management : a convergence of diverse interests, June 11-12, 1975 /

Cover title: Load management

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Contextual and Environmental Awareness Laboratory for Energy Consumption Management

The recent changes on power systems paradigm requires the active participation of small and medium players in energy management. With an electricity price fluctuation these players must manage the consumption. Lowering costs and ensuring adequate user comfort levels. Demand response can improve the power system management and bring benefits for the small and medium players. The work presented in this paper, which is developed aiming the smart grid context, can also be used in the current power system paradigm. The proposed system is the combination of several fields of research, namely multi-agent systems and artificial neural networks. This system is physically implemented in our laboratories and it is used daily by researchers. The physical implementation gives the system an improvement in the proof of concept, distancing itself from the conventional systems. This paper presents a case study illustrating the simulation of real-time pricing in a laboratory.

Load Profile Analysis Tool for Electrical Appliances in Households

This paper presents a methodology to forecast the hourly and daily consumption in households. The methodology was validated for households in Lisbon region, Portugal. The paper shows that the forecast tool allows obtaining satisfactory results for forecasting. Models of demand response allow the support of consumer’s decision in exchange for an economic benefit by the redefinition of load profile or changing the appliance consumption period. It is also in the interest of electric utilities to take advantage of these changes, particularly when consumers have an action on the demand-side management or production. Producers need to understand the load profile of households that are connected to a smart grid, to promote a better use of energy, as well as optimize the use of micro-generation from renewable sources, not only to delivering to the network but also in self-consumption.

Load Profile Analysis Tool for Electrical Appliances in Households Assisted by CPS

This paper presents a methodology to forecast the hourly and daily consumption in households assisted by cyber physical systems. The methodology was validated using a database of consumption of a set of 93 domestic consumers. Forecast tools used were based on Fast Fourier Series and Generalized Reduced Gradient. Both tools were tested and their forecast results were compared. The paper shows that both tools allow obtaining satisfactory results for energy consumption forecasting.

Evaluation of a 2D multileader training system for improving sustainability and precision orchard management applications in italian apple orchards

The presented study aimed to evaluate the productive and physiological behavior of a 2D multileader apple training systems in the Italian environment both investigating the possibility to increase yield and precision crop load management resolution. Another objective was to find valuable thinning thresholds guaranteeing high yields and matching fruit market requirements. The thesis consists in three studies carried out in a Pink Lady®- Rosy Glow apple orchard trained as a planar multileader training system (double guyot). Fruiting leaders (uprights) dimension, crop load, fruit quality, flower and physiological (leaf gas exchanges and fruit growth rate) data were collected and analysed. The obtained results found that uprights present dependence among each other and as well as a mutual support during fruit development. However, individual upright fruit load and upright’s fruit load distribution on the tree (~ plant crop load) seems to define both upright independence from the other, and single upright crop load effects on the final fruit quality production. Correlations between fruit load and harvest fruit size were found and thanks to that valuable thinning thresholds, based on different vegetative parameters, were obtained. Moreover, it comes out that an upright’s fruit load random distribution presents a widening of those thinning thresholds, keeping un-altered fruit quality. For this reason, uprights resulted a partially physiologically-dependent plant unit. Therefore, if considered and managed as independent, then no major problems on final fruit quality and production occurred. This partly confirmed the possibility to shift crop load management to single upright. The finding of the presented studies together with the benefits coming from multileader planar training systems suggest a high potentiality of the 2D multileader training systems to increase apple production sustainability and profitability for Italian apple orchard, while easing the advent of automation in fruit production.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Demand response in electrical energy supply: an optimal real time pricing approach

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. This paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. The use of DemSi by a retailer in a situation of energy shortage, is presented. Load reduction is obtained using a consumer based price elasticity approach supported by real time pricing. Non-linear programming is used to maximize the retailer’s profit, determining the optimal solution for each envisaged load reduction. The solution determines the price variations considering two different approaches, price variations determined for each individual consumer or for each consumer type, allowing to prove that the approach used does not significantly influence the retailer’s profit. The paper presents a case study in a 33 bus distribution network with 5 distinct consumer types. The obtained results and conclusions show the adequacy of the used methodology and its importance for supporting retailers’ decision making.