Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles.

A challenge that consistently arises when reviewing and justifying novel energy models and theorems is the accuracy of the electrical data used. Therefore, this paper presents a dataset representing a complete European residential community based on real-life data. In this case, a community of 250 residential households was constructed with actual energy consumption and photovoltaic generation profiles collected by smart meters in households in different European locations. Additionally, 200 members of the community were ascribed with their photovoltaic generation, while 150 were owners of a battery storage system. New profiles were generated from the sample collected and were randomly given to each end-user according to their previously defined characteristics. Furthermore, one regular and one premium vehicle were allocated to each household - a total of 500 electric vehicles - with information on their capacity, state of charge, and usage. Moreover, data on the location, type, and prices of public electric vehicle chargers were specified.

Demand Response Contextual Remuneration of Prosumers with Distributed Storage.

Prosumers are emerging in the power and energy market to provide load flexibility to smooth the use of distributed generation. The volatile behavior increases the production prediction complexity, and the demand side must take a step forward to participate in demand response events triggered by a community manager. If balance is achieved, the participants should be compensated for the discomfort caused. The authors in this paper propose a methodology to optimally manage a community, with a focus on the remuneration of community members for the provided flexibility. Four approaches were compared and evaluated, considering contextual tariffs. The obtained results show that it was possible to improve the fairness of the remuneration, which is an incentive and compensation for the loss of comfort. The single fair remuneration approach was more beneficial to the community manager, since the total remuneration was lower than the remaining approaches (163.81 m.u. in case study 3). From the prosumers' side, considering a clustering method was more advantageous, since higher remuneration was distributed for the flexibility provided (196.27 m.u. in case study 3).

Dataset of an energy community's generation and consumption with appliance allocation.

Energy data measured on-site from buildings can help describe the consumption behavior of end-users and can be used to examine and prove certain theorems and models, that require a large volume of data to be gathered. However, the direct extraction of this data can often be a lengthily and costly process. As a result, a dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. In addition, the overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles. First, several consumption and photovoltaic generation profiles, as well as a vast collection of appliance profiles, were gathered. These profiles were obtained from household readings in different locations, while the public building's profile was based on the consumption and photovoltaic production profiles of the research building GECAD. The profiles went through the process of normalization and new profiles were generated to complete the number of end-users needed. Moreover, these profiles were given a maximum consumption and production level at random before being accepted by one of the end-users. Therefore, fourteen of these households and the public building were randomly attributed with renewable solar energy. Finally, if possible, the tool created allocated, at random in previously determined intervals, the appliances' load profiles into each end-user's available consumption areas.

A research agenda for open energy science: Opportunities and perspectives of the F1000Research Energy Gateway.

Energy enables the functioning of modern society. However, humanity's reliance on fossil fuels since the industrial revolution has contributed to many societal problems including climate change, environmental degradation and pollution, and the transition to a renewable and carbon-free energy system is one of the grand challenges for the 21st century. The aim of this editorial is to outline the importance of a fast and transparent sharing of energy research and discuss key themes of the Energy Gateway of F1000Research.

Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System.

Recommender systems are able to suggest the most suitable items to a given user, taking into account the user's and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user's context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using ϵ -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed system.

Use of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approach.

The increase in sensors in buildings and home automation bring potential information to improve buildings' energy management. One promissory field is load forecasting, where the inclusion of other sensors' data in addition to load consumption may improve the forecasting results. However, an adequate selection of sensor parameters to use as input to the load forecasting should be done. In this paper, a methodology is proposed that includes a two-stage approach to improve the use of sensor data for a specific building. As an innovation, in the first stage, the relevant sensor data is selected for each specific building, while in the second stage, the load forecast is updated according to the actual forecast error. When a certain error is reached, the forecasting algorithm (Artificial Neural Network or K-Nearest Neighbors) is trained with the most recent data instead of training the algorithm every time. Data collection is provided by a prototype of agent-based sensors developed by the authors in order to support the proposed methodology. In this case study, data over a period of six months with five-minute time intervals regarding eight types of sensors are used. These data have been adapted from an office building to illustrate the advantages of the proposed methodology.

Consumption Optimization in an Office Building Considering Flexible Loads and User Comfort.

This 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.

An Intelligent Smart Plug with Shared Knowledge Capabilities.

The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource's context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.

Adaptive Portfolio Optimization for Multiple Electricity Markets Participation.

The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.