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IEEE International Conference on Industrial Informatics (INDIN)

作者： Md Tofael Ahmed Pedro Faria Omid Abrishambaf Zita Vale GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (IPP) Porto Portugal

Home Energy Management System creates the scopes to small household electrical appliances users to participate in the demand response programs. Among several load controllable electrical household appliances water heater is more suitable. Integration of water heater is considered to manage the demand response events that can contribute to smart grid technology. This paper represents a thermodynamic load model for a water heater, which is considered as to be controlled through direct load control for demand response program. The daily electricity consumption and temperature profile of the heater is also considered, the direct load control method is activated to the heater as soon as the energy consumption reaches to 1 kW, with the effects the device is turned off for next one hour. In results, it gained a significant reduction in the electricity consumption for the users without making any discomfort as temperature does not reduce to disruption level. Real time electricity pricing is also compared which implied financial benefit to the consumers. The result exhibit that the method applied to this heater can contribute and participate in the demand response events.

关键词： Water heating Load modeling Resistance heating Home appliances Temperature measurement Load management Smart grids

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Optimizing Energy Costs in Energy Communities: A Monthly Tariff Clustering Approach

Optimizing Energy Costs in Energy Communities: A Monthly Tar...

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International Conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Fernando Lezama Diego Bairrao Filipe Doria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Polytechnic of Porto Porto Portugal

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k-means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments.

关键词： Costs Accuracy Heuristic algorithms Switching frequency Tariffs Clustering algorithms Switches Power systems intelligent systems Optimization

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Building Energy Consumption Forecast under Different Anticipations on a Green Computation Perspective

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IFAC-PapersOnLine 2023年第2期56卷 10923-10928页

作者： Daniel Ramos Pedro Faria Luis Gomes Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto (P.PORTO) Porto Portugal

Electrical buildings are composed by smart grids technologies intended on improving the energy efficiency. Nowadays, forecasting algorithms are crucial to formulate advance decisions resulting in lower energy costs. This paper uses two forecasting algorithms known as artificial neural networks and k-nearest neighbors to obtain accurate energy predictions in a target week with the support of an annual historic with energy and auxiliary sensors devices data. Green computing is also addressed in this paper to value the environmental sustainability of computing devices. This is possible by reducing the computational effort of the GPU device dedicated on forecasting activities. Therefore, the historic and predictions of this paper are contextualized in five minutes periods and hour schedules with energy activity behaviors.

关键词： building energy efficiency forecasting green computing

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Bidding in Local Electricity Markets Considering Community Storage Systems

Bidding in Local Electricity Markets Considering Community S...

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International Conference on Environment and Electrical eng.neering (EEEIC)

作者： Fernando Lezama Joao Soares Zahra Foroozandeh Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) LASI - Polytechnic of Porto Porto Portugal

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

Local energy markets are complex environments with interactions from different actors pursuing diverse objectives. This article introduces a model for bidding in local energy markets considering consumers, small producers, and prosumers with renewable generation. In addition, the model includes a community centralized energy storage system (ESS) that consumers as a community can freely use, paying an incentive to small producers for charging the storage device. Due to its complexity, the problem is solved using metaheuristic optimization. Through an experiment in a case study with nine agents and a 13.2 kWh ESS, we show that the community can benefit from the centralized storage unit, minimizing the consumers' cost and at the same time increasing the profits of the small generators. However, it is also shown that the way in which the community makes use of the central unit and splits the cost and profits is not trivial, and further discussion about the fair share of profits obtained from the battery requires scrutiny of the results.

关键词： Degradation Renewable energy sources Costs Metaheuristics Stochastic processes Europe Electricity supply industry

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Energy Community Consumption and Generation Dataset with Appliance Allocation

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IFAC-PapersOnLine 2022年第9期55卷 285-290页

作者： Calvin Goncalves Ruben Barreto Pedro Faria Luis Gomes Zita Vale GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (P.PORTO) P-4200-072 Porto Portugal

New energy approaches and models require an ample amount of data to be collected in order to be tested and validated, however ordinary on-site load data measuring from buildings or households can be expensive and time-consuming. This paper creates a dataset of a residential community by attributing sample consumption and photovoltaic production profiles and disaggregating the total power consumption of these households into the consumption of several individual appliances commonly used. Initially, a few consumption and photovoltaic production profiles and an extensive library of appliance profiles were collected, allowing the tool created to randomly assign the appliances’ load profile, if possible, into the available consumption areas of each end-user and in certain intervals previously defined by the user. In the case study, we generated a community with 49 households and a public building (municipal library) in 50 buildings and attributed 15 of these with renewable solar energy. The results show the creation of a flexible energy community where the appliances allocated can occupy approximately 40% of the total consumption profiles.

关键词： Appliance Allocation End-user Energy Profile Energy Community Renewable Energy

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Participation of a Smart Community of Consumers in Demand Response Programs

Participation of a Smart Community of Consumers in Demand Re...

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Clemson University Power Systems Conference (PSC)

作者： Omid Abrishambaf Pedro Faria Zita Vale GECAD— Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (IPP) Porto Portugal

ISBN: (纸本)9781728103174;9781728103167

The penetration of renewable energy resources and demand response programs causes several management issues, such as network instability. Several research projects are currently investigating and surveying several methods to enhance the network reliability. This paper represents a smart model of community grid that contains a central management unit and several consumers, producers and prosumers. In the proposed model, the community manager is able to control the consumption and generation of the resources by establishing contracts with its members. The community manager utilizes a single period optimization problem for minimizing its operation costs by applying different types of demand response programs and the use of renewable resources. In the case study, an internal low voltage distribution network of a real university campus is considered as the community grid, in order to test and validate the proposed model.

关键词： Contracts Optimization Load management Microgrids Pricing Electricity supply industry Smart cities

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Demand Response-based Model for an Energy Community Considering Members and Electric Vehicles Participation

Demand Response-based Model for an Energy Community Consider...

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International Conference on Environment and Electrical eng.neering (EEEIC)

作者： Ruben Barreto Luis Gomes Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI Polytechnic of Porto Porto Portugal

This paper proposes a demand response-based model to manage the energy resources of an energy community, considering the data privacy of the respective community members and electric vehicles. First, through a day-ahead forecasting algorithm, the implemented model identifies possible demand response events to be launched in the energy community. Then, the community members and electric vehicles are ranked to identify which will be selected to participate in the respective demand response event. This selection is based on several metrics created to assess different types of community members' data through clustering algorithms. Furthermore, this model also considers real-time monitoring and evaluation of the demand response event. Finally, the implemented model was tested with energy community data containing 750 possible candidates to participate in the demand response event with their flexibility, where these candidates are households and several different electric vehicles. The results show that the implemented model enables a saving of 13.66 EUR while reducing the CO 2 emissions by 32.34 kg in a single demand response event.

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Data mining for remuneration of consumers demand response participation 18th

Data mining for remuneration of consumers demand response pa...

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18th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2020

作者： Ribeiro, Catarina Pinto, Tiago Vale, Zita Baptista, José GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Vila Real Portugal Porto Portugal UTAD – Universidade de Trás-os-Montes e Alto-Douro Vila Real Portugal CPES – INESCTEC Porto Portugal

ISBN: (纸本)9783030519988

With the implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a player that allows aggregating a diversity of entities, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players’ benefits. The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. This paper proposes methodologies to develop strategic remuneration of aggregated consumers with demand response participation, this model uses a clustering algorithm, applied on values that were obtained from a scheduling methodology of a real Portuguese distribution network with 937 buses, 20310 consumers and 548 distributed generators. The normalization methods and clustering methodologies were applied to several variables of different consumers, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision-making process is found, according to players characteristics. © Springer Nature Switzerland AG 2020.

关键词： Distributed power generation

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Wang and Mendel's fuzzy rule learning method for energy consumption forecasting considering the influence of environmental temperature

Wang and Mendel's fuzzy rule learning method for energy cons...

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International Global Information Infrastructure Symposium (GIIS)

作者： Aria Jozi Tiago Pinto Isabel Praça Francisco Silva Brígida Teixeira Zita Vale GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP-Polytechnic of Porto Porto Portugal

Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.

关键词： Energy consumption Buildings Forecasting Databases Learning systems Weather forecasting

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Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods

Defining the Optimal Number of Demand Response Programs and ...

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International Conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Cátia Silva Pedro Faria Zita Vale GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development PP - Polytechnic of Porto Porto Portugal

ISBN: (数字)9781728131924

ISBN: (纸本)9781728131931

Nowadays, the data can be considered an asset when properly managed. An entity with the right tool to analyse the amount of data existent and withdraw crucial information will have the power to obliterate the competition. In the Energy sector, with Smart Grid introduction, small resources have more influence in the market through Demand Response and bidirectional communication. However, none of the actual business models is prepared to deal with the uncertainty related to these resources. The authors, in order to find a solution for this complex problem, proposed a methodology which the goal is to minimize operation costs and give fair compensation for resources who participate in the management of local markets. With this fair payment, it is expected continuous participation. Through clustering methods, remuneration groups are created. In the present paper, a study about the optimal number of clusters is performed. The information gives the Aggregator control in results of the following phases, understanding the impact in the remuneration of the resources.

关键词： Remuneration Elbow Distributed power generation Load management Clustering methods Optimal scheduling

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