A Customized Evolutionary Algorithm for Multiobjective Management of Residential Energy Resources

Soares, Ana; Gomes, Álvaro; Antunes, Carlos Henggeler; Oliveira, Carlos

doi:10.1109/tii.2016.2628961

Cited by 58 publications

(19 citation statements)

References 23 publications

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“…Finally, a logical decision can be made to shed or curtail loads regarding decision makers' benefit and/or consumers' preferences [35]. Other applications, which can be applied in energy systems in respect of load controlling, is related to time-shiftable loads and thermostatically controllable loads [36][37][38]. In this regard, system operators control shiftable loads by using incentive-based offers.…”

Section: Energy Consumersmentioning

confidence: 99%

Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues

et al. 2018

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The Internet of Things (IoT) is beginning to shape the future of many industries and emerging markets. One of the target markets for IoT is the energy systems. IoT is a matter of producing, transferring, and processing information, therefore all parts of the system including software and hardware parts should be considered as a whole. In this paper, a state-of-the-art of the IoT-based energy systems is presented to review the recent activities on every component of IoT in energy systems. Challenges in this subject area are discussed, and some solutions are presented thereafter.

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Section: Energy Consumersmentioning

confidence: 99%

Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues

et al. 2018

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“…The scenarios proposed for the demand shifting are multiple and include electrical storage shifting (e.g., batteries, electrical vehicles) [17,19], thermal storage shifting [17,20], or electrical equipment shifting [18]. Soares et al [21] combined the different provided approaches. They present an evolutionary algorithm that optimizes the usage of heterogeneous energy resources (local generation and storage systems) and proposes an optimal strategy for the loads (shiftable loads and thermostatically controllable loads).…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Energy Usage in a Microgrid Using a Learning Algorithm

Essayeh

Fenni

Dahmouni

2018

Wireless Communications and Mobile Computing

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The microgrid is a new concept of integrating the distributed energy resources (DER) within the grid. The management of the heterogeneous sources of energy presents a challenge, especially as most of the DER are unpredictable. Besides, implementing microgrids should be economically beneficial to the customer; this will raise the challenge of decreasing the costs while ensuring the energy balance. In this paper, we used a stochastic approach based on a model-free Markov decision process (MDP) to derive the optimal strategy for the home energy management system. The approach aims to decrease the energy bill while taking into account the intermittency of the renewable energy resources (DER) and other constraints. While other proposals charge the battery from the utility energy, making the state of charge (SOC) of the battery a deterministic variable, our work adopts a scenario where the battery is charged from the excess of the generated energy, which makes the SOC a nondeterministic variable affected by the uncertain character of the renewable energy. Therefore, our model considers the randomness at two levels: renewable energy level and battery SOC level. We take into account the complexity of the solution, and we propose a simple strategy that can be implemented easily in microgrids.

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“…Several heuristic and evolutionary DR strategies have been developed to cope with the non-linearities involved in the problem (see e.g., [50,51]) . In advanced tariffs, the price/cost of the energy changes depending on the direction and the magnitude of the power flow between the node and the grid.…”

Section: Related Workmentioning

confidence: 99%

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Liberati¹,

Giorgio

2017

Energies

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This paper presents a two-level control scheme for the energy management of an electricity prosumer node equipped with controllable loads, local generation, and storage devices. The main control objective is to optimize the prosumer's energy bill by means of intelligent load shifting and storage control. A generalized tariff model including both volumetric and capacity components is considered, and user preferences as well as all technical constraints are respected. Simulations based on real household consumption data acquired with a sampling period of 1 s are discussed. The proposed control scheme bestows the prosumer node with the flexibility needed to support smart grid use cases such as bill optimization (i.e., local energy trading), control of the profile at the point of connection with the grid, demand response, and reaction to main supply faults (e.g., islanding operation), etc.

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A Customized Evolutionary Algorithm for Multiobjective Management of Residential Energy Resources

Cited by 58 publications

References 23 publications

Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues

Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues

Cost-Effective Energy Usage in a Microgrid Using a Learning Algorithm

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

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