Regulatory capital and social trade-offs in planning of smart distribution networks with application to demand response solutions

Abstract Under the current UK regulatory framework for electricity distribution networks, asset upgrades are planned with the objectives of minimising both capital costs (and thus customer fees) and social costs such as those associated with carbon emissions and customer interruptions. This approach naturally results in economic trade-offs as network solutions meant to reduce social costs typically increase (sometimes significantly) capital costs, and vice versa. This can become an issue in a smart grid context where new operational solutions such as Demand Response (DR) may emerge. More specifically, even though there is a general belief that smart solutions will only provide benefits due to their potential to displace investments in costly assets (e.g., lines and substations), they may also introduce trade-offs associated with increased operational expenditure, power losses and emissions compared with networks with upgraded assets. On the other hand, the flexibility inherent in smart solutions could be used to balance the different types of costs, leading to attractive cost trade-offs if properly modelled, quantified and regulated. However, given the fundamental “non-asset” nature of DR, properly quantifying the resulting trade-offs so as to perform a like-for-like comparison with traditional planning strategies is a grand challenge. In this light, this article proposes a methodology to explicitly model and quantify capital and social cost trade-offs in distribution network planning, which can be incorporated into the existing regulatory framework. The results, based on real UK distribution networks, show that our proposed methodology can be used to explicitly model and regulate cost trade-offs. By doing so, it is possible to encourage more efficient levels of capital expenditure and social benefits by deploying the right mix of traditional asset-based and smart DR-based solutions.

[1]  Pierluigi Mancarella,et al.  Reliability evaluation of demand response to increase distribution network utilisation , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[2]  Pierluigi Mancarella,et al.  Probabilistic modeling and assessment of the impact of electric heat pumps on low voltage distribution networks , 2014 .

[3]  Marko Aunedi,et al.  Smart control for minimizing distribution network reinforcement cost due to electrification , 2013 .

[4]  Maurizio Delfanti,et al.  Changing the regulation for regulating the change: Innovation-driven regulatory developments for smart grids, smart metering and e-mobility in Italy , 2013 .

[5]  Friedrich Schneider,et al.  The importance of incorporating reliability of supply criteria in a regulatory system of electricity distribution : An empirical analysis for Austria , 2008 .

[6]  Pierluigi Mancarella,et al.  Distribution network reinforcement planning considering demand response support , 2014, 2014 Power Systems Computation Conference.

[7]  Pierluigi Mancarella,et al.  Optimal design of low-voltage distribution networks for CO2 emission minimisation. Part II: Discrete optimisation of radial networks and comparison with alternative design strategies , 2011 .

[8]  Haozhong Cheng,et al.  Active distribution network expansion planning integrating dispersed energy storage systems , 2016 .

[9]  R. A. Ewers UK energy policy , 1986 .

[10]  Ignacio J. Pérez-Arriaga,et al.  From distribution networks to smart distribution systems: Rethinking the regulation of European electricity DSOs , 2014 .

[11]  J. Broderick Capacity to Customers (C2C) Carbon Impact Assessment Final Assessment Report , 2015 .

[12]  Tim Jackson,et al.  Developing electricity distribution networks and their regulation to support sustainable energy , 2010 .

[13]  E. A. Martinez Cesena,et al.  Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response , 2016 .

[14]  Peter J. G. Pearson,et al.  UK Energy Policy 1980-2010: A history and lessons to be learnt , 2012 .

[15]  Colleen Lueken,et al.  Distribution grid reconfiguration reduces power losses and helps integrate renewables , 2012 .

[16]  Panos M. Pardalos,et al.  Handbook of Multicriteria Analysis , 2010 .

[17]  Pierluigi Mancarella,et al.  Optimal design of low-voltage distribution networks for CO2 emission minimisation. Part I: model formulation and circuit continuous optimisation , 2011 .

[18]  Sajad Najafi Ravadanegh,et al.  Multi-Stage Planning of Distribution Networks with Application of Multi-Objective Algorithm Accompanied by DEA Considering Economical, Environmental and Technical Improvements , 2016, J. Circuits Syst. Comput..

[19]  E. A. Martinez Cesena,et al.  Economic assessment of distribution network reinforcement deferral through post-contingency demand response , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[20]  Tooraj Jamasb,et al.  Distributed Generation Storage, Demand Response, and Energy Efficiency as Alternatives to Grid Capacity Enhancement , 2014 .

[21]  Vahid Vahidinasab,et al.  An aggregated model for coordinated planning and reconfiguration of electric distribution networks , 2016 .

[22]  Pierluigi Mancarella,et al.  Reliability and Risk Assessment of Post-Contingency Demand Response in Smart Distribution Networks , 2016 .