A multi-agent based optimization of residential and industrial demand response aggregators

Golmohamadi, Hessam; Keypour, Reza; Bak‐Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna

doi:10.1016/j.ijepes.2018.12.020

Cited by 101 publications

(52 citation statements)

References 27 publications

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“…Ant Colony Optimization [24] Approximate Q-Learning [35] Artificial Bee Colony [32] Artificial Neural Network [38] Differential Evolution [37] Expert System [28] Fuzzy Sets [17,27,44,47] Genetic Algorithm [16,22,25,34,36,39,43] Geometric Clustering with Unsupervised Learning [42] Heuristic Optimization [21] Imperialist Competitive Algorithm [18] Multi Agent Systems [10,14,23,41,48,49] Particle Swarm Optimization [11][12][13]15,26,29,30,33,37,40,46] Reinforcement Learning [50] Scatter Search [31] Simulated Annealing [19] Support Vector Machines [45] 4.…”

Section: Computational Intelligence Methods Referencementioning

confidence: 99%

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Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Georgilakis

2020

Energies

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The massive integration of distributed energy resources in power distribution systems in combination with the active network management that is implemented thanks to innovative information and communication technologies has created the smart distribution systems of the new era. This new environment introduces challenges for the optimal operation of the smart distribution network. Local energy markets at power distribution level are highly investigated in recent years. The aim of local energy markets is to optimize the objectives of market participants, e.g., to minimize the network operation cost for the distribution network operator, to maximize the profit of the private distributed energy resources, and to minimize the electricity cost for the consumers. Several models and methods have been suggested for the design and optimal operation of local energy markets. This paper introduces an overview of the state-of-the-art computational intelligence methods applied to the optimal operation of local energy markets, classifying and analyzing current and future research directions in this area.

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Section: Computational Intelligence Methods Referencementioning

confidence: 99%

“…In [48], a local energy market is designed to facilitate demand response of residential and industrial consumers and to provide flexibility to deal with the intermittency and volatility of renewable energy sources. Participating residential customers are represented by residential DR aggregator.…”

Section: Multi Agent Systemsmentioning

confidence: 99%

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Georgilakis

2020

Energies

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“…In the case of finite uncertainty realisations, the expected value can be determined by computing the objective function value for all possible realisations and taking its expectation. For other cases, an approximation of the set of possible realisations can be computed via Monte Carlo sampling, assuming a stochastic model, or based on field observations [44], [47]- [49]. Finally, in some particular problems, decisions need to be obtained based on a sequential methodology, commonly via two-stage or multi-stage-based stochastic optimisation approaches [8], [50].…”

Section: Residential Load Uncertaintiesmentioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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Energy is a vital resource for human activities and lifestyle, powering important everyday infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil fuels, remain the main source of worldwide consumed energy. The environmental impact of their exploitation has boosted research and investments in alternative, clean and renewable sources, including photovoltaic and windbased systems. As a whole, buildings are one of the major energy consumption sectors. Hence, improving energy efficiency in buildings will result in economical and environmental gains. In the case of households, home energy management systems are mainly used for monitoring real-time consumption and to schedule appliance operations so that the energy bill could be minimised, or according to another specific criterion. This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings. In addition, an updated literature list regarding commonly managed household appliances and scheduling objectives are included. Physical and operational constraints, and how they are addressed by home energy management systems along with security issues are also discussed. INDEX TERMS Energy efficiency, home energy management systems, household appliance models, load management, optimal scheduling, smart homes, security.

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“…Therefore, it cannot be an economic decision to make such a huge investment. To overcome the challenge, there are some flexibility potentials among the heavy industries that can provide demand flexibility to the electricity/gas network [2]. Since Iran is now beginning to emerge as the major refiner in the Middle East in terms of refining capacity, the electricity and heat generation of Iran's Oil Refinery Industries (ORI) can provide a golden opportunity to hedge against the energy shortage at peak hours.…”

Section: Motivation and Problem Descriptionmentioning

confidence: 99%

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Golmohamadi

Asadi

2020

Energies

Self Cite

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This paper proposes a novel approach to optimize the main energy consumptions of heavy oil refining industries (ORI) in response to electricity price uncertainties. The whole industrial sub-processes of the ORI are modeled mathematically to investigate the joint power-heat flexibility potentials of the industry. To model the refinery processes, an input/output flow-based model is proposed for five main refining units. Moreover, the role of storage tanks capacity in the power system flexibility is investigated. To hedge against the electricity price uncertainty, an uncertain bound for the wholesale electricity price is addressed. To optimize the industrial processes, a dual robust mixed-integer quadratic program (R-MIQP) is adopted; therefore, the ORI’s operational strategies are determined under the worst-case realization of the electricity price uncertainty. Finally, the suggested approach is implemented in the south-west sector of the Iran Energy Market that suffers from a lack of electricity in hot days of summer. The simulation results confirm that the proposed framework ensures industrial demand flexibility to the external grids when a power shortage occurs. The approach not only provides demand flexibility to the power system, but also minimizes the operation cost of the industries.

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A multi-agent based optimization of residential and industrial demand response aggregators

Cited by 101 publications

References 27 publications

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

A Survey on Home Energy Management

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

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