Residential micro-grid load management through artificial neural networks

Barelli, Linda; Bidini, Gianni; Bonucci, F.; Ottaviano, A.

doi:10.1016/j.est.2018.03.011

Cited by 32 publications

(22 citation statements)

References 41 publications

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“…Aien et al [61] developed a methodology for estimating the real-time state of energy prices by probabilistic optimum power flow studies using the hybrid artificial neural networks concept. Similarly, Barelli et al [62] propose an ANN model to predict the scheduling of programmable loads given the climatic conditions related to the current and the previous day, in addition to the weather forecast for the following day. Furthermore, Aien et al [61] show the use of ANN in different power system applications, such as load forecasting, electricity price forecasting, wind speed prediction, and state estimations in the distribution system.…”

Section: Artificial Neural Network Applied In Resmentioning

confidence: 99%

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

et al. 2020

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To reduce the risks of a new energy crisis and increase energy availability, the use of renewable energy sources (RES) is important and recommended. In Brazil, micro and small companies contribute about 25% of gross domestic product (GDP), and electric energy is employed intensively, so the importance of microgeneration is observable. This research aims to analyze the economic viability of the micro-generation wind energy project for micro and small businesses. Thus, three Brazilian states, Rio Grande do Norte, Rio Grande do Sul and Minas Gerais were considered, and different scenarios were proposed. A feasibility analysis is then performed, followed by a stochastic analysis using Monte Carlo simulation (MCS). Finally, models of artificial neural networks (ANN) are used to evaluate the relative importance (RI) of the variables. The results show that none of the states appears economically feasible under the conditions presented. In the stochastic analysis, the probability of viability is between 17% and 24% in all states, which shows the low probability of viability for microgeneration. Through ANN training, it was possible to calculate the RI, in which it is possible to identify the variables that have most impact on the net present value (NPV) in all states; it is considered the most important variable in the project's viability. In addition, the discussion explores the importance of public incentives for promoting investment in renewable energy, which can reduce investment costs and make it attractive to small and medium-sized businesses. INDEX TERMS Microgeneration, wind power, stochastic feasibility analysis, sensitivity analysis, artificial neural networks.

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Section: Artificial Neural Network Applied In Resmentioning

confidence: 99%

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

et al. 2020

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“…It is worth remarking how Equation (2) is valid for horizontal panels. The unavoidable losses [30] due to bad weather conditions (e.g., rainy and cloudy days) are here accounted for through the γ factor shown in the first Equation 2, which is evaluated on a monthly basis by comparing model outputs with experimental values acquired for the selected location from [31].…”

Section: Pv Yield Estimationmentioning

confidence: 99%

Self-Sufficient and Islanded-Oriented Design of a Reversible Solid Oxide Cell-Based Renewable Microgrid

2019

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This article presents the development of a constrained optimization algorithm, whose scope is to support the preliminary design of a renewable microgrid, integrating solar panels and wind turbines with reversible solid oxide cells. The motivations behind this research activity lie in the increasing interest in renewable-based production and on-site storage of hydrogen, and its aim is to help this energy vector spread worldwide and in as many industrial and residential sectors as possible within a reasonably short timeframe. To this end, suitable models were developed by referring to the most relevant literature and by introducing some specific simplifying assumptions. Such an approach allowed the setting-up of a multi-variable constrained optimization task, whose outcomes correspond to the most techno-economic effective plant configuration with respect to assigned design criteria. The optimum solution was particularly sought via the generalized reduced gradient method, aimed at determining renewable plants sizes under the constraint that the final stored hydrogen level is brought back to the initial value after one year. The results highlight that an interesting payback time of about 10 years can be attained, while guaranteeing that the optimal configuration holds promising resiliency and islanded-use capabilities (such as almost weekly self-sufficiency) via smart over-the-year charge-sustaining management of the designed hydrogen storage tank. In this way, it was possible to simultaneously address, via the specific optimization problem formulation, the interconnected needs of optimally designing system components in terms of installed power, and the proper management of the reversible solid oxide cell unit.

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“…Intelligent fuzzy logic is proposed to make decision intelligent. Maximization of PV source is proposed in [4], through which storage system is integrated in order to achieve high energy independence in SMG that is based on residential load management. Data driven based home energy management (HEM) is studied in [5], that is optimized by Bayesian algorithm including renewable energy resources (RER) and energy storage system.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Based Framework for Energy Management of Smart Micro-Grid

et al. 2020

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The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and ecofriendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work. INDEX TERMS Control agent, energy market management controller, multi objective grey wolf optimization, home energy management controller, local generation, market management, micro-grid, particle swarm optimization, renewable energy resources

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Residential micro-grid load management through artificial neural networks

Cited by 32 publications

References 41 publications

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

Microgeneration of Wind Energy for Micro and Small Businesses: Application of ANN in Sensitivity Analysis for Stochastic Economic Feasibility

Self-Sufficient and Islanded-Oriented Design of a Reversible Solid Oxide Cell-Based Renewable Microgrid

Multi Objective Based Framework for Energy Management of Smart Micro-Grid

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