An adaptive multi-agent-based approach to smart grids control and optimization

Carvalho, Marco; Pérez, Carlos; Granados, Adrian

doi:10.1007/s12667-012-0054-0

Cited by 16 publications

(5 citation statements)

References 19 publications

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“…Table 3 shows the implemented agent types according to [86][87][88][89][90]. Each agent has a different level of complexity, which is reflected in their element-specific tasks in consideration of the individual design goals [60,91].…”

Section: Multi-agent Simulationmentioning

confidence: 99%

Analysis of Dependencies between Gas and Electricity Distribution Grid Planning and Building Energy Retrofit Decisions

et al. 2020

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Most macroeconomic studies predict a decline in final energy demand and the use of natural gas in the heating sector in Europe. In the course of building retrofitting, gas-based heating systems are predominantly replaced by electricity-based solutions. This influences the business models of electricity and especially gas distribution network operators (DNOs), where grid charges tend to rise. The resulting feedback effect could accelerate the decrease of demand and finally lead to the defection of the gas grid—an effect that has been neglected in energy system analysis so far. We present a multi-agent simulation with a rule-based gas and electricity DNO model and a building retrofit optimization model to analyze these interdependencies during the transformation path, focusing on the role of different technical, economic, and regulatory triggers. Our case studies for a real grid area of a German city shows that an interplay of the gas and electricity DNO’s strategy, as well as the building-, heating system-, grid-, and trigger-configuration, determine the decision on the extension, continuation, or defection of the gas grid infrastructure. Finally, strategies for how to reduce the risk of a gas grid defection, which are relevant for DNOs, policy makers, and creators of macro-economic models, are discussed.

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Section: Multi-agent Simulationmentioning

confidence: 99%

Analysis of Dependencies between Gas and Electricity Distribution Grid Planning and Building Energy Retrofit Decisions

et al. 2020

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“…However, smart grids are important to integrate several forms of renewable distributed generation, diversifying the energy matrix, reducing transmission losses, and improving the power system reliability, security and efficiency [6,8]. Furthermore, smart grids result in socio-economic benefits to the society at large [5].…”

Section: Introductionmentioning

confidence: 99%

A review on smart grids and experiences in Brazil

Santo

Kanashiro

Santo

et al. 2015

Renewable and Sustainable Energy Reviews

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“…proposed an advanced battery energy storage system allocation method based on multiple agents' collaboration during an energy transaction process in a distribution system 11 . developed an ABM framework for dynamic simulation of local resource production, storage, and consumption 12 . developed an MAS for best practice of infrastructure, including the monitoring and coordinating a smart grid environment with renewable sources and configurable storage devices.…”

Section: Related Workmentioning

confidence: 99%

A multi agent‐based optimisation model for the distribution planning and control of energy‐based intermittent renewable sources

Mohammed

Al-Bazi

2021

Int J Energy Res

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Summary Recent work on the management of renewable energy sources focuses on developing innovative tools and techniques to control the different behaviours of energy generation systems (eg, the intermittent production of wind power). These tools contribute to achieving a proper balance between energy supply and consumer demand and guarantee a sustainable energy level in storage devices that can face potential generation shortages. In contrast, previous studies mainly focused on stimulating consumer response to market prices so as to rationalise consumption, especially during peak periods. This study investigates the impact of supplier decisions about using different renewable energy sources on the distribution planning and control of energy for the best consumer demand satisfaction and achieving a sustainable energy level in different located storage devices. A Multi Agent‐Based Heuristic Optimisation model is developed to deliver this aim. The heuristic optimisation part of this model is proposed to optimise the energy level across differently located storage devices. It also guarantees the best energy exchange between regions at the strategic planning level. A sensitivity analysis study is conducted to verify the behaviour of the proposed model towards various demand levels, followed by a comparison study to justify the proposed agent‐based heuristic model's superiority. The results highlight the impact of using intermittent renewable sources including solar, wind, hydro and storage devices on energy production, control and distribution. In addition, a sustainable level of storage in devices placed at different locations is achieved by optimising the energy storage operation. This sustainable storage aids any insufficient energy generated from intermittent sources to satisfy consumer requirements. The best energy exchange between regions is also presented.

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An adaptive multi-agent-based approach to smart grids control and optimization

Cited by 16 publications

References 19 publications

Analysis of Dependencies between Gas and Electricity Distribution Grid Planning and Building Energy Retrofit Decisions

Analysis of Dependencies between Gas and Electricity Distribution Grid Planning and Building Energy Retrofit Decisions

A review on smart grids and experiences in Brazil

A multi agent‐based optimisation model for the distribution planning and control of energy‐based intermittent renewable sources

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