A multi-layer agent-based model for the analysis of energy distribution networks in urban areas

Fichera, Alberto; Pluchino, Alessandro; Volpe, Rosaria

doi:10.1016/j.physa.2018.05.124

Cited by 24 publications

(17 citation statements)

References 20 publications

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“…Energies 2019, 12, x FOR PEER REVIEW 3 of 34 management [18] and grid design strategies [19]. ABMs have also been used to predict price of energy trading in smart grids by the use of incomplete information by different agents to optimize their own utility [20].…”

Section: Electricity Markets and Grid Balancingmentioning

confidence: 99%

“…Previous work on the use of ABMs for electricity grids, markets, and the injection of renewables have focused on the effect of prosumption and peer to peer supply and its effect on the grid management [18] and grid design strategies [19]. ABMs have also been used to predict price of energy trading in smart grids by the use of incomplete information by different agents to optimize their own utility [20].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling

et al. 2019

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This article provides an agent-based model of a hypothetical standalone electricity network to identify how the feed-in tariffs and the installed capacity of wind power, calculated in percentage of total system demand, affect the electricity consumption from renewables. It includes the mechanism of electricity pricing on the Day Ahead Market (DAM) and the Imbalance Market (IM). The extra production volumes of Electricity from Renewable Energy Sources (RES-E) and the flexibility of electrical consumption of industries is provided as reserves on the IM. Five thousand simulations were run by using the agent-based model to gather data that were then fit in linear regression models. This helped to quantify the effect of feed-in tariffs and installed capacity of wind power on the consumption from renewable energy and market prices. The consumption from renewable sources, expressed as percentage of total system consumption, increased by 8.17% for every 10% increase in installed capacity of wind power. The sharpest increase in renewable energy consumption is observed when a feed-in tariff of 0.04 €/kWh is provided to the wind farm owners, resulting in an average increase of 9.1% and 5.1% in the consumption from renewable sources while the maximum installed capacity of wind power is 35% and 100%, respectively. The regression model for the annualized DAM prices showed an increase by 0.01 €cents/kWh in the DAM prices for every 10% increase in the installed wind power capacity. With every increase of 0.01 €/kWh in the value of feed-in tariffs, the mean DAM price is lowered as compared to the previous value of the feed-in tariff. DAM prices only decrease with increasing installed wind capacity when a feed-in tariff of 0.04 €/kWh is provided. This is observed because all wind power being traded on DAM at a very cheap price. Hence, no volume of electricity is being stored for availability on IM. The regression models for predicting IM prices show that, with every 10% increase in installed capacity of wind power, the annualized IM price decreases by 0.031 and 0.34 €cents/kWh, when installed capacity of wind power is between 0 and 25%, and between 25 and 100%, respectively. The models also showed that, until the maximum installed capacity of wind power is less than 25%, the IM prices increase when the value of feed-in tariff is 0.01 and 0.04 €/kWh, but decrease for a feed-in tariff of 0.02 and 0.03 €/kWh. When installed capacity of wind power is between 25 and 100%, increasing feed-in tariffs to the value of 0.03 €/kWh result in lowering the mean IM price. However, at 0.04 €/kWh, the mean IM price is higher, showing the effect of no storage reserves being available on IM and more expensive reserves being engaged on the IM. The study concludes that the effect of increasing installed capacity of wind power is more significant on increasing consumption of renewable energy and decreasing the DAM and IM prices than the effect of feed-in tariffs. However, the effect of increasing values of both factors on the profit of RES-E producers with storage facilities is not positive, pointing to the need for customized rules and incentives to encourage their market participation and investment in storage facilities.

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Section: Electricity Markets and Grid Balancingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling

et al. 2019

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“…The application of ABMs to the distribution network has already produced some interesting applications that focus on a much smaller scale than the transmission network and consider urban networks [39][40][41]. In some cases, complex network considerations are introduced [41].…”

Section: Agent-based Modeling In Power Systems and Power Network Designmentioning

confidence: 99%

A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature

et al. 2019

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Transmission expansion planning is a problem of considerable complexity where classical optimization techniques are unable to handle large case studies. Decomposition and divide-and-conquer strategies have been applied to this problem. We propose an alternative approach based on agent-based modeling (ABM) and inspired by the behavior of the Plasmodium mold, which builds efficient transportation networks as result of its search for food sources. Algorithms inspired by this mold have already been applied to road-network design. We modify an existing ABM for road-network design to include the idiosyncratic features of power systems and their related physics, and test it over an array of case studies. Our results show that the ABM can provide near-optimal designs in all the instances studied, possibly with some further interesting properties with respect to the robustness of the developed design. In addition, the model works in a decentralized manner, using mostly local information. This means that computational time will scale with size in a more benign way than global optimization approaches. Our work shows promise in applying ABMs to solve similarly complex global optimization problems in the energy landscape.Energies 2019, 12, 4427 2 of 17 local, because they refer to agents and those they encounter in their proximity [3]. However, their interactions might lead to globally optimal design, such as in the cases of ant-colony behavior and slime mold growth, which have been shown to develop optimal networks [2]. In these biological systems, network development involves two phases: exploration and consolidation. The exploratory phase is characterized by the over-production of links between the core organism and the possible food sources. The consolidation phase, in turn, leads to the selection and positive reinforcement of the links that are used the most, while destroying or recycling the rest. The final network structure is likely to represent a context-specific balance between the need for efficient transport, low cost, and robustness.The inspiration for our algorithm comes from reference [2]. In their article, which is based on biological experiments, the authors argue that the slime mold Physarum polycephalum creates a network that is efficient enough to compete with the real design of the railway system around Tokyo. The molds Plasmodium and Physarum have been shown to be able to solve many computational problems, such as finding the shortest path between two points [4], constructing hierarchies of planar proximity graphs [5], or executing basic logical computing schemes [5,6]. The molds are also capable of sketching a mathematical model of an adaptive network that solves a convoluted maze [2]. The key underlying mechanism supporting its success, in all these instances, is always this cycle of over-production of links and consolidation. This is precisely what we will imitate in our algorithm: the transmission lines that are used the most will be reinforced, while those that are not used will be discarded. Some s...

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“…The methodology here proposed adopts the SOC earthquake engine of the OFC model in the context of agent-based simulations. Agent-based (or multi-agent) simulations are a powerful computational tool that has been extensively applied in several fields to model complex phenomena [27][28][29][30][31][32][33][34][35][36]. This approach has been here implemented by means of a new software, called ABES (Agent-Based Earthquake Simulator) and realized within the programmable multi-agent environment NetLogo [37], with the purpose of assessing the seismic vulnerability of urban areas in the critical state.…”

Section: Introductionmentioning

confidence: 99%

A New Agent-Based Methodology for the Seismic Vulnerability Assessment of Urban Areas

Greco

Pluchino

Barbarossa

et al. 2019

IJGI

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In order to estimate the seismic vulnerability of a densely populated urban area; it would in principle be necessary to evaluate the dynamic behaviour of individual and aggregate buildings. These detailed seismic analyses, however, are extremely cost-intensive and require great processing time and expertise judgment. The aim of the present study is to propose a new methodology able to combine information and tools coming from different scientific fields in order to reproduce the effects of a seismic input in urban areas with known geological features and to estimate the entity of the damages caused on existing buildings. In particular, we present new software called ABES (Agent-Based Earthquake Simulator), based on a Self-Organized Criticality framework, which allows to evaluate the effects of a sequence of seismic events on a certain large urban area during a given interval of time. The integration of Geographic Information System (GIS) data sets, concerning both geological and urban information about the territory of Avola (Italy), allows performing a parametric study of these effects on a real context as a case study. The proposed new approach could be very useful in estimating the seismic vulnerability and defining planning strategies for seismic risk reduction in large urban areas

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A multi-layer agent-based model for the analysis of energy distribution networks in urban areas

Cited by 24 publications

References 20 publications

Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling

Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling

A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature

A New Agent-Based Methodology for the Seismic Vulnerability Assessment of Urban Areas

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