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Browsing ISEP – GECAD – Artigos by Author "Abrishambaf, Omid"
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- Agricultural irrigation scheduling for a crop management system considering water and energy use optimizationPublication . Abrishambaf, Omid; Faria, Pedro; Gomes, Luis; Vale, ZitaCenter pivot systems are widely used to overcome the irrigation needs of agricultural fields. In this paper, an autonomous approach is proposed in order to improve the low efficiency of irrigation by developing a system based on the water requirement of the plantations, through field data. The data are local temperature, local wind, soil moisture, precipitation forecast, and soil evapotranspiration calculation. This information enables the system to calculate the real evapotranspiration for not being necessary to restrict to lysimetric measures. By this way, the system schedules the irrigation for the lower cost periods, considering the produced energy by the local resources, and the price of energy purchased from the utility grid. Also, it is considered that the irrigation must be carried out within the time interval in which the plantations do not reach the wilding point, so it will be carried out at the periods with the lowest cost. This will optimize the overall operational costs of the irrigation.
- Air conditioner consumption optimization in an office building considering user comfortPublication . Khorram Ghahfarrokhi, Mahsa; Faria, Pedro; Abrishambaf, Omid; Vale, ZitaThe rapid growth of energy consumption and its consequences in the last decades, made the world persuaded to energy optimization and energy management. Therefore, producers and prosumers should be equipped with the automation infrastructures to perform the management programs, such as demand response programs. Office buildings are considering as a proper case for implementing energy consumption minimization since they are responsible for a huge portion of total energy consumption in the world. This paper proposes a multi-period optimization algorithm implemented in Supervisory Control and Data Acquisition system of an office building. The developed optimization algorithm is an efficient solution considered for minimizing the power consumption of air conditioners by considering the user comfort constraints. Two determinative parameters are defined to prevent over-power reduction from certain devices. In order to respect to user preferences, priority numbers are dedicated to each air conditioner to present the importance of each device. A case study with several scenarios is implemented to verify the performance of the proposed algorithm in real life using real data of the building. The obtained results show the impacts of proposed parameters and different comfort constraints of algorithm while the main target of the optimization has been reached.
- An Aggregation Model for Energy Resources Management and Market NegotiationsPublication . Abrishambaf, Omid; Faria, Pedro; Spínola, João; Vale, ZitaCurrently the use of distributed energy resources, especially renewable generation, and demand response programs are widely discussed in scientific contexts, since they are a reality in nowadays electricity markets and distribution networks. In order to benefit from these concepts, an efficient energy management system is needed to prevent energy wasting and increase profits. In this paper, an optimization based aggregation model is presented for distributed energy resources and demand response program management. This aggregation model allows different types of customers to participate in electricity market through several tariffs based demand response programs. The optimization algorithm is a mixed-integer linear problem, which focuses on minimizing operational costs of the aggregator. Moreover, the aggregation process has been done via K-Means clustering algorithm, which obtains the aggregated costs and energy of resources for remuneration. By this way, the aggregator is aware of energy available and minimum selling price in order to participate in the market with profit. A realistic low voltage distribution network has been proposed as a case study in order to test and validate the proposed methodology. This distribution network consists of 25 distributed generation units, including photovoltaic, wind and biomass generation, and 20 consumers, including residential, commercial, and industrial buildings.
- An Optimization Based Community Model of Consumers and Prosumers: A Real-Time Simulation and Emulation ApproachPublication . Abrishambaf, Omid; Silva, Cátia; Faria, Pedro; Vale, ZitaThe electricity consumption pattern is being increased day by day. Currently, network operators are moving towards renewable energy resources and applying demand response programs. However, the small and medium scale consumers and producers are needed to be aggregated and participate in the electricity markets as a unique resource. This paper proposes an optimization-based community model for aggregating the small scales consumers and producers. The model includes a central controller, which is considered as an aggregator, and several local community managers to keep the network balanced locally. Furthermore. real-time simulation approach and several real devices as hardware-in-the-loop are used to validate the system under practical challenges. The results of the paper reveal a gap between the simulation and experimental results and prove the performance of system in real-time mode using actual devices.
- Application of an optimization-based curtailment service provider in real-time simulationPublication . Abrishambaf, Omid; Faria, Pedro; Vale, ZitaThe use of demand response programs and distributed renewable energy resources are intensively discussed. These concepts play a key role in the distribution network, especially smart grids and microgrids. Nowadays, most of the implemented demand response programs are considered for large-scale resources, which make small and medium resources unable to participate in electricity market negotiations. In order to overcome this barrier, a third-party entity, namely an aggregator, can be considered as an intermediate player between the demand side and grid side. For this purpose, curtailment service provider is considered as an aggregator, which aggregates small and medium-scale resources, who do not have adequate capacity of reduction or generation and allow them to participate in wholesale electricity markets as a unique resource. However, before massive implementation of business models, the performance of the curtailment service provider should be adequately surveyed and validated in order to prevent future problems. This paper proposes a real-time simulation model of a curtailment service provider, which employs several real and laboratory hardware equipment considered as hardware-in-the-loop in the real-time simulator. Furthermore, an optimization problem is developed for a curtailment service provider in order to optimally schedule the available resources including several demand response programs and distributed renewable resources, aiming at minimizing its operation costs. The implemented case study considers a distribution network with 20 consumers and prosumers, and 26 renewable-based producers including wind and photovoltaic generation, where the developed model is performed in real-time for 12 min and behaviors of small and medium prosumers and producers is surveyed.
- Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy managementPublication . Faia, R.; Pinto, Tiago; Abrishambaf, Omid; Fernandes, Filipe; Vale, Zita; Corchado, Juan ManuelThis paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.
- Consumption Optimization in an Office Building Considering Flexible Loads and User ComfortPublication . Khorram, Mahsa; Faria, Pedro; Abrishambaf, Omid; Vale, ZitaThis paper presents a multiperiod optimization algorithm that is implemented in a Supervisory Control and Data Acquisition system. The algorithm controls lights and air conditioners as flexible loads to reduce the consumption and controls a dishwasher as a deferrable load to implement the load shifting. Several parameters are considered to implement the algorithm for several successive periods in a real building operation. In the proposed methodology, optimization is done regarding user comfort, which is modeled in the objective function related to the indoor temperature in each room, and in the constraints in order to prevent excessive power reduction, according to users' preferences. Additionally, the operation cycle of a dishwasher is included, and the algorithm selects the best starting point based on the appliance weights and power availability in each period. With the proposed methodology, the building energy manager can specify the moments when the optimization is run with consideration of the operational constraints. Accordingly, the main contribution of the paper is to provide and integrate a methodology to minimize the difference between the actual and the desired temperature in each room, as a measure of comfort, respecting constraints that can be easily bounded by building users and manager. The case study considers the real consumption data of an office building which contains 20 lights, 10 ACs, and one dishwasher. Three scenarios have been designed to focus on different functionalities. The outcomes of the paper include proof of the performance of the optimization algorithm and how such a system can effectively minimize electricity consumption by implementing demand response programs and using them in smart grid contexts.
- Demand response implementation in smart householdsPublication . Fotouhi Ghazvini, Mohammad Ali; Soares, João; Abrishambaf, Omid; Castro, Rui; Vale, ZitaHome energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for end-users without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provide advantages of a dispatchable source. It is expected that the penetration of EVs will grow considerably in future. This algorithm is designed for a smart household with a rooftop photovoltaic (PV) system integrated with an energy storage system (ESS). Simulation results are presented under different pricing and DR programs to demonstrate the application of the HEMS and to verify its’ effectiveness. Case studies are conducted using real measurements. They consider the household load, the rooftop PV generation forecast and the built-in parameters of controllable appliances as inputs. The results exhibit that the daily household energy cost reduces 29.5%–31.5% by using the proposed optimization-based algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes.
- Energy Scheduling Using Decision Trees and Emulation: Agriculture Irrigation with Run-of-the-River Hydroelectricity and a PV Case StudyPublication . Abrishambaf, Omid; Faria, Pedro; Vale, Zita; Corchado, Juan M.Agriculture is the very backbone of every country. Unfortunately, agricultural sustainability is threatened by the lack of energy-efficient solutions. The threat becomes more evident with the constantly growing world population. The research community must, therefore, focus on resolving the problem of high energy consumption. This paper proposes a model of energy scheduling in agricultural contexts. Greater energy efficiency is achieved by means of PV (photovoltaics) and hydropower, as demonstrated in the conducted case study. The developed model is intended for contexts where the farm is located near a river, so the farmer can use the flowing water to produce energy. Moreover, the model has been emulated using a variety of state-of-the-art laboratory devices. Optimal energy scheduling is performed via a decision tree approach, optimizing the use of energy resources and reducing electricity costs. Finally, a realistic scenario is presented to show the technical features and the practical behaviors of each emulator when adapting the results of the decision tree. The research outcomes demonstrate the importance of the technical validation of each model. In addition, the results of the emulation reveal practical issues that had not been discovered during the theoretical study or during the simulation
- House Management System with Real and Virtual Resources: Energy Efficiency in Residential MicrogridPublication . Santos, Gabriel; Fernandes, Filipe; Pinto, Tiago; Silva, Marco; Abrishambaf, Omid; Morais, Hugo; Vale, ZitaThe reduction of the greenhouse gas emissions is a priority all around the globe. The investment on renewable energy sources is contributing for new opportunities in the context of the smart grids and microgrids. Recent advances are transforming the consumer into a prosumer, being able to adapt the consumption depending on its own generated power, and selling the surplus or buying the missing power. In this context, home management systems are emerging as an effective means to support the management of energy resources in the context of communication between functions/devices of a smart home. This paper presents a new agent-based home energy management approach, using ontologies to enable semantic communications between heterogeneous multi-agent entities. The main goal is to support an efficient energy management of end consumers in the context of microgrids, obtaining a scheduling for both real and virtual resources. A case study is presented, which simulates a 25-bus microgrid that includes a laboratorial controlled house (with real and simulated resources), which is managed by the proposed energy management system.