Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy

Ionescu, Laurenţiu Mihai; Bizon, Nicu; Mazare, Alin-Gheorghita; Belu, Nadia

doi:10.3390/math8071144

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…Based on the proposed phase identification methodology, we have developed an automated methodology for the optimal redistribution of single-phase consumers in a low voltage grid. The developed methodology makes use of a genetic algorithm [16] specially developed to minimize the imbalances between bases.…”

Section: Resultsmentioning

confidence: 99%

Phase Identification in Smart Grids – Case Study

Crespí-Castañer,

Roca,

Rosselló

et al. 2023

RE&PQJ

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. A widespread problem of the electricity distribution system is determining to which phase a consumer is connected. Generally, utility companies did not record the distribution of the phases over time, which is essential to address the problem of unbalanced voltage distribution networks. In addition, manual phase identification is not feasible due to its high cost. The massive deployment of smart meters allowed periodic readings of energy consumption, voltage, current, etc. Out of this large amount of data, several techniques based on machine learning have emerged, addressing the problem of phase identification automatically while maintaining the existing infrastructure. Hence, phase identification is essential for developing smart-grid solutions. In the present work, we applied an unsupervised machine-learning technique that allows the classification of the time-voltage series recorded by the smart meters of a low-voltage three-phase radial distribution network located in the Balearic Islands (Spain). The results show that there is a correlation between the time series of the feeder voltage and the consumer meters. The proposed method reached a 100% success rate in the case study. In addition, the results obtained open the way to deploy a new grid configuration to minimize the load imbalance.

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Section: Resultsmentioning

confidence: 99%

Phase Identification in Smart Grids – Case Study

Crespí-Castañer,

Roca,

Rosselló

et al. 2023

RE&PQJ

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“…Metaheuristic algorithms such as the genetic algorithm, particle swarm optimization, differential evolution, whale algorithm, and fireworks algorithm are nature-inspired optimization techniques that have proven their performance in problems regarding the optimization of operation conditions in electrical networks. Recently, they have been applied for integrating electric vehicles and distributed generation into smart grids [36], optimal reconfiguration of distribution networks [37], optimal power flow analysis in DC distribution networks [38], reliability improvement [39], and optimal consumption planning [40]. In this paper, a genetic algorithm was used to determine the optimal buses and phases of connection for a fixed number of storage units (batteries) with the aim of reducing the energy losses over a time interval of 24 h. The GA was preferred because, as the following subsection of the paper will describe in detail, it allows for simple and efficient modeling of the mentioned storage use scenarios, which is the same as using the same basic approach in solving three different problems with minimal modifications to a base case.…”

Section: Related Literaturementioning

confidence: 99%

A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids

et al. 2021

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The global climate change mitigation efforts have increased the efforts of national governments to incentivize local households in adopting PV panels for local electricity generation. Since PV generation is available during the daytime, at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of local generation to a later time. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation conditions in the network. This paper proposes a metaheuristic approach based on a genetic algorithm that considers three different scenarios of using energy storage for reducing the energy losses in the network. Two cases considers the battery placement and operation under the direct control of the network operator, with single and multiple bus and phase placement locations. Here, the aim was to maximize the benefit for the whole network. The third case considers selfish prosumer battery management, where the storage owner uses the batteries only for their own benefit. The optimal design of the genetic algorithm and of the solution encoding allows for a comparative study of the results, highlighting the important strengths and weaknesses of each scenario. A case study is performed in a real distribution system.

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“…Metaheuristic algorithms such as the Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, Whale Algorithm, Fireworks Algorithm, are nature-inspired optimization techniques that proved their performance in problems regarding the optimization of operation conditions in electrical networks. In the latest years, they were applied for integrating electric vehicles and distributed generation into smart grids [36], optimal reconfiguration of distribution networks [37], optimal power flow analysis in DC distribution networks [38], reliability improvement [39], optimal consumption planning [40]. In the paper, a Genetic Algorithm was used to determine the optimal buses and phases of connection for a fixed number of storage units (batteries), with the aim of reducing the energy losses over a time interval of 24 hours, according to the assumptions and scenarios described in Section 1.…”

Section: Related Literaturementioning

confidence: 99%

A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids

Ivanov¹,

Neagu²,

Grigoraș³

et al. 2021

Preprint

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The global climate change mitigation efforts have increased the efforts of national government to incentivize local households in adopting individual renewable energy as a mean to help reduce the usage of electricity generated using fossil fuels and to gain independence from the grid. Since the majority of residential generation is made by PV panels that generate electricity at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of locally generated electricity at later times. On the other hand, the presence of distributed generation can affect negatively the operating conditions of low-voltage distribution networks. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation of the network. This paper proposes a metaheuristic approach based on a Genetic Algorithm that considers three different scenarios of using energy storage for reducing the losses in the network. Prosumer and network operator priorities can be considered in different scenarios inside the same algorithm, to provide a comparative study of different priorities in storage placement. A case study performed on a real distribution network provides insightful results.

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Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy

Cited by 13 publications

References 27 publications

Phase Identification in Smart Grids – Case Study

Phase Identification in Smart Grids – Case Study

A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids

A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids

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