Dr Babak Mahdavi

Job: Research Fellow

Faculty: Technology

School/department: School of Engineering and Sustainable Development

Research group(s): Institute of Energy and Sustainable Development

Address: IESD, Queens Building,De Montfort University,Leicester,LE1 9BHUK

T: +44 (0)116 207 8516

E: bmahdavi@dmu.ac.uk

W: www.iesd.dmu.ac.uk/~cascade

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Personal profile

Complex Adaptive Systems, Cognitive Agents & Distributed Energy (CASCADE): a Complexity Science-Based Investigation into the Smart Grid Concept.

Research group affiliations

CASCADE Project.

Publications and outputs

Exploring Smart Grid Possibilities: A Complex Systems Modelling Approach
Exploring Smart Grid Possibilities: A Complex Systems Modelling Approach Rylatt, R. Mark; Snape, J. Richard; Allen, P.; Ardestani, B. M.; Boait, Peter John; Boggasch, E.; Fan, Denis; Fletcher, G.; Gammon, Rupert; Lemon, Mark; Pakka, V. H.; Savill, M.; Smith, Stefan; Strathern, M.; Varga, Liz Smart grid research has tended to be compartmentalised, with notable contributions from economics, electrical engineering and science and technology studies. However, there is an acknowledged and growing need for an integrated systems approach to the evaluation of smart grid initiatives. The capacity to simulate and explore smart grid possibilities on various scales is key to such an integrated approach but existing models – even if multidisciplinary – tend to have a limited focus. This paper describes an innovative and flexible framework that has been developed to facilitate the simulation of various smart grid scenarios and the interconnected social, technical and economic networks from a complex systems perspective. The architecture is described and related to realised examples of its use, both to model the electricity system as it is today and to model futures that have been envisioned in the literature. Potential future applications of the framework are explored, along with its utility as an analytic and decision support tool for smart grid stakeholders. Complex Systems Research Centre, Cranfield University & Ostfalia, Fakultät Versorgungstechnik, EOS – Institut für energieoptimierte Systeme Open Access article
An Agent Based Model for Optimal Generation Mix based on Price Elasticity of Aggregated Consumer Demand
An Agent Based Model for Optimal Generation Mix based on Price Elasticity of Aggregated Consumer Demand Pakka, V. H.; Ardestani, B. M.; Rylatt, R. M. This work has its interests in the relation between the generation mix within a power system and the elasticity of demand based on the prices emerging from the short term electricity market. The paper starts by describing a new agent-based modelling framework that involves electricity producers, consumers and suppliers as agents participating in a market environment. The framework allows for investigating the effect of demand elasticities on bidding of generators in the short term market and its influence on their revenue in the long term. We focus on the increasingly important issue of renewable technology such as wind generation and the volatility it brings into the electricity market. Specifically we investigate three scenarios with varying mix of generating technologies such as coal, gas and wind turbines and measure the aggregate demand response to signals such as the System Buy Prices (SBP) emerging out of the balancing market. This work was conducted as part of the AMEN (Agentbased Modelling of Electricity Networks) project, funded by the Engineering and Physical Sciences Research Council in the UK under Grant Reference EP/K033492/1.
Levelling of heating and vehicle demand in distribution networks using randomised device control
Levelling of heating and vehicle demand in distribution networks using randomised device control Boait, Peter John; Ardestani, B. M.; Snape, J. Richard Rising demand from electrical heating and vehicles will drive major distribution network reinforcement costs unless 24-hour demand profiles can be levelled. We propose a demand response scheme in which the electricity supplier provides a signal to a “smart home” control unit that manages the consumer’s appliances using a novel approach for reconciliation of the consumer’s needs and desires with the incentives supplied by the signal. The control unit allocates demand randomly in timeslots that are acceptable to the consumer but with a probability biased in accordance with the signal provided by the supplier. This behaviour ensures that demand response is predictable and stable and allows demand to be shaped in a way that can satisfy distribution network constraints.
Accommodating renewable generation through an aggregator-focused method for inducing demand side response from electricity consumers
Accommodating renewable generation through an aggregator-focused method for inducing demand side response from electricity consumers Boait, Peter John; Ardestani, B. M.; Snape, J. Richard The ability to influence electricity demand from domestic and small business consumers so that it can be matched to intermittent renewable generation and distribution network constraints is a key capability of a smart grid. This involves signalling to consumers to indicate when electricity use is desirable or undesirable. However simply signalling a time dependent price does not always achieve the required demand response and can result in unstable system behaviour. We propose a demand response scheme in which an aggregator mediates between the consumer and the market and provides a signal to a “smart home” control unit that manages the consumer’s appliances using a novel method for reconciliation of the consumer’s needs and preferences with the incentives supplied by the signal. This method involves random allocation of demand within timeslots acceptable to the consumer with a bias depending on the signal provided. By simulating a population of domestic consumers using heat pumps and electric vehicles with properties consistent with UK national statistics, we show the method allows total demand to be predicted and shaped in a way that can simultaneously match renewable generation and satisfy network constraints, leading to benefits from reduced use of peaking plant and avoided network reinforcement. Consortium project with Cranfield University The file attached is a postprint of a paper submitted to and accepted for publication in [journal] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library"
CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems
CASCADE: An Agent Based Framework For Modeling The Dynamics Of Smart Electricity Systems Rylatt, R. M.; Gammon, Rupert; Boait, Peter John; Varga, L.; Allen, P.; Savill, M.; Snape, J. Richard; Lemon, Mark; Ardestani, B. M.; Pakka, V. H.; Fletcher, G.; Smith, S.; Fan, D.; Strathern, M. The Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE) project is developing a framework based on Agent Based Modelling (ABM). The CASCADE Framework can be used both to gain policy and industry relevant insights into the smart grid concept itself and as a platform to design and test distributed ICT solutions for smart grid based business entities. ABM is used to capture the behaviors of diff erent social, economic and technical actors, which may be defi ned at various levels of abstraction. It is applied to understanding their interactions and can be adapted to include learning processes and emergent patterns. CASCADE models ‘prosumer’ agents (i.e., producers and/or consumers of energy) and ‘aggregator’ agents (e.g., traders of energy in both wholesale and retail markets) at various scales, from large generators and Energy Service Companies down to individual people and devices. The CASCADE Framework is formed of three main subdivisions that link models of electricity supply and demand, the electricity market and power fl ow. It can also model the variability of renewable energy generation caused by the weather, which is an important issue for grid balancing and the profi tability of energy suppliers. The development of CASCADE has already yielded some interesting early fi ndings, demonstrating that it is possible for a mediating agent (aggregator) to achieve stable demandfl attening across groups of domestic households fi tted with smart energy control and communication devices, where direct wholesale price signals had previously been found to produce characteristic complex system instability. In another example, it has demonstrated how large changes in supply mix can be caused even by small changes in demand profi le. Ongoing and planned refi nements to the Framework will support investigation of demand response at various scales, the integration of the power sector with transport and heat sectors, novel technology adoption and diffusion work, evolution of new smart grid business models, and complex power grid engineering and market interactions. Collaborative project with Cranfield University
Agent-based modelling of the UK short term electricity market: Effects of intermittent wind power.
Agent-based modelling of the UK short term electricity market: Effects of intermittent wind power. Pakka, V. H.; Ardestani, B. M.; Rylatt, R. M.

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Research interests/expertise

Agent-based modelling, Geosimulation, Microsimulation; Empirical validation, Spatial data analysis, Complex adaptive systems simulation, Agent-based computational economics, Geographically referenced social simulation, Emergence, Nonlinear dynamics.

Membership of professional associations and societies

Fellow of Royal Geographical Society – Institute of British Geographers
Member of British Computer Society
Member of the Association of American Geographers
Member of the Association for Computing Machinery, Special Interest Group on Simulation and Modeling
Member of Institute of Electrical and Electronics Engineers, Computer Society
Member of European Social Simulation Association
Member of Complex Systems Society.