1,720,973 research outputs found
An Optimization Problem for Day-Ahead Planning of Electrical Energy Aggregators
Full text linkThe widespread diffusion of distributed energy resources, especially those based on renewable energy, and energy storage devices has deeply modified power systems. As a consequence, demand response, the ability of customers to respond to regulating signals, has moved from large high-voltage and medium-voltage end-users to small, low-voltage, customers. In order to be effective, the participation to demand response of such small players must be gathered by aggregators. The role and the business models of these new entities have been studied in literature from a variety of viewpoints. Demand response can be clearly applied by sending a dedicated price signal to customers, but this methodology cannot obtain a diverse, punctual, predictable, and reliable response. These characteristics can be achieved by directly controlling the loads units. This approach involves communication problems and technological readiness. This paper proposes a fully decentralized mixed integer linear programming approach for demand response. In this framework, each load unit performs an optimization, subject to technical and user-based constraints, and gives to the aggregator a desired profile along with a reserve, which is guaranteed to comply with the constraints. In this way, the aggregator can trade the reserve coming from several load units, being the only interface to the market. Upon request, then, the aggregator communicates to the load units the modifications to their desired profiles without either knowing or caring how this modification would be accomplished. The effectiveness is simulated on 200 realistic load units
Advanced operational functionalities for a low voltage Microgrid test site
No full textThis work aims to describe the implementation of a renewable energy based Microgrid test site facility at the University of Genova (Italy) and to depict the advanced functionalities there implemented within the national supported project Smartgen. The developed advanced Distribution
Management System (DMS) is based on a Supervisory Control And Data Acquisition (SCADA) system for the remote monitoring of a Low Voltage Microgrid composed by a photovoltaic (PV) plant, an ion-lithium energy storage system, electrical load and a weather station. The implemented DMS is embedded with advanced functionalities which provide 36 hours forecasting of the load consumption and renewable generation. The forecasted data are also used to optimally program and manage the storage device. The proposed algorithms are described and also implementation aspects are reported within the paper
Electrical consumption forecasting in hospital facilities: An application case
No full textThe topic of energy efficiency applied to buildings represents one of the key aspects in today’s interna- tional energy policies. Emissions reduction and the achievement of the targets set by the Kyoto Protocol are becoming a fundamental concern in the work of engineers and technicians operating in the energy management field. Optimal energy management practices need to deal with uncertainties in generation and demand, hence the development of reliable forecasting methods is an important priority area of research in electric energy systems. This paper presents a load forecasting model and the way it was applied to a real case study, to forecast the electrical consumption of the Cellini medical clinic of Turin. The model can be easily integrated into a Building Management System or into a real time monitoring system. The load forecasting is performed through the implementation of an artificial neural network (ANN). The proposed multi-layer perceptron ANN, based on a back propagation training algorithm, is able to take as inputs: loads, data concerning the type of day (e.g. weekday/holiday), time of the day and weather data. In particular, this work focuses on providing a detailed analysis and an innovative formal procedure for the selection of all the ANN parameters
Mixed-Integer Algorithm for Optimal Dispatch of Integrated PV-Storage Systems
No full textThe exploitation of combined PhotoVoltaic plants and storage systems is nowadays assuming growing importance, due to the technical, environmental and economical benefits which can derive from an optimal integration. In this paper, a mixed-integer algorithm for the optimal dispatch of a storage system, based on the day-ahead photovoltaic forecasting is developed. The optimization objective is the maximization of the total production of the integrated system, according to a requested active power profile, which can be defined by the operator. The study case of an existing Distribution Management System, which operates on the Low Voltage microgrid at University of Genova is analyzed. The procedure is validated by field results with particular attention to the storage round-trip efficiency
A Stochastic Optimization Method for Planning and Real-Time Control of Integrated PV-Storage Systems: Design and Experimental Validation
No full textThis paper proposes a solution for the day-ahead planning and the real-time control of an integrated system composed of a PhotoVoltaic plant (PV) and a Battery Energy Storage System (BESS). The day-ahead algorithm provides the optimal daily energy delivery profile based on a prediction of the PV production. The objective is to maximize the power delivery, according to the request of specific profile shape. The real- time operation algorithm dynamically regulates the BESS power exchange, in order to realize the scheduled power profile. Both the proposed algorithms exploit chance-constrained stochastic optimization, in order to take into account the uncertainties of the PV power predictions. Simulation results show the effectiveness of the proposed solutions, while their validation is achieved by experimental tests, executed on a LV microgrid controlled by a Distribution Management System (DMS)
Distributed Energy Resources Management in a Low Voltage Test Facility
No full textThe electric energy demand will increase in the future, and the will to exploit larger amounts of generation from renewable resources requires the development of new strategies to manage a more complex electrical system. Different techniques allow the smart management of distribution networks such as load shifting, peak shaving, and short-term optimization. This work aims to test, in a real low-voltage (LV) active network (LV test facility of Strathclyde University of Glasgow), a Microgrid Smart Energy Management System, which adopts a two-stage strategy. The two levels of the proposed energy control system are composed of: 1) midterm controller that, according to weather, load, and generation forecasts, computes the profile of the controllable resources (generation, load, and storage), the dispatch problem is then solved through an optimization process; and through 2) short-term controller, which controls the power absorption of the active network. This procedure is hierarchically designed to dispatch the resources/loads, according to priority signals with the objective to contain the energy consumption below predetermined thresholds. The scalability and effectiveness of the architecture, which is validated in a real test bed, demonstrates the feasibility of implementing such a type of controller directly connected to the LV breakers, delivering a part of a real smart grid
Mixed-integer algorithm for optimal dispatch of integrated PV-storage systems
No full textThe exploitation of combined photovoltaic plants and storage systems is nowadays assuming growing importance due to the technical, energetic, environmental and economical benefits which can derive from an optimal integration. In this paper a mixed-integer algorithm for optimal dispatch of a lithium-ions storage system, based on the day ahead PV forecasting, is developed within an existing Distribution Management System, which operates on the low voltage Microgrid at University of Genova. The optimization object is the maximization of the Microgrid production according to the requested active power profile, which can be defined by the operator. The procedure has been validated by field results with particular attention to the storage efficiency, that has been modeled with additional constraints
FORECASTING AND MONITORING OF ELECTRIC CONSUMPTION IN HOSPITAL FACILITIES. AN APPLICATION CASE: THE ''CELLINI'' CLINIC
No full textAn Architecture for Implementing State Estimation Application in Distribution Management System (DMS)
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