Reliability and Risk Assessment of Post-Contingency Demand Response in Smart Distribution Networks

Abstract This paper presents a comprehensive framework for the assessment of reliability and risk implications of post-fault Demand Response (DR) to provide capacity release in smart distribution networks. A direct load control (DLC) scheme is presented to efficiently disconnect DR customers with differentiated reliability levels. The cost of interrupted load is used as a proxy for the value of the differentiated reliability contracts for different customers to prioritize the disconnections. The framework tackles current distribution system operator (DSO)’s corrective actions such as network reconfiguration, emergency ratings and load shedding, also considering the physical payback effects from the DR customers’ reconnection. Sequential Monte Carlo simulation (SMCS) is used to quantify the risk borne by the DSO if contracting fewer DR customers than required by deterministic security standards. Numerical results demonstrate the benefits of the proposed DR scheme, when compared to the current DLC scheme applied from the local DSO. In addition, as a key point to boost the commercial implementation of such DR schemes, the results show how the required DR volume could be much lower than initially estimated when properly accounting for the actual risk of interruptions and for the possibility of deploying the asset emergency ratings. The findings of this work support the rationale of moving from the current prescriptive deterministic security standards to a probabilistic reliability assessment and planning approach applied to smart distribution networks, which also involves distributed energy resources such as post-contingency DR for network support.

[1]  Yann-Chang Huang,et al.  Integrating direct load control with interruptible load management to provide instantaneous reserves for ancillary services , 2004 .

[2]  Michael J. Sullivan,et al.  Estimated Value of Service Reliability for Electric Utility Customers in the United States , 2009 .

[3]  Akbar Ebrahimi,et al.  Short-Term Impacts of DR Programs on Reliability of Wind Integrated Power Systems Considering Demand-Side Uncertainties , 2016, IEEE Transactions on Power Systems.

[4]  Emilio Ghiani,et al.  Reliability assessment in smart distribution networks , 2013 .

[5]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[6]  Matti Lehtonen,et al.  Distribution network reliability improvements in presence of demand response , 2014 .

[7]  E.F. El-Saadany,et al.  Reliability Evaluation for Distribution System With Renewable Distributed Generation During Islanded Mode of Operation , 2009, IEEE Transactions on Power Systems.

[8]  Roy Billinton,et al.  Impacts of demand side management on bulk system reliability evaluation considering load forecast uncertainty , 2011, 2011 IEEE Electrical Power and Energy Conference.

[9]  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).

[10]  Goran Strbac,et al.  Application of demand side response and energy storage to enhance the utilization of the existing distribution network capacity , 2013 .

[11]  J. Oyarzabal,et al.  A Direct Load Control Model for Virtual Power Plant Management , 2009, IEEE Transactions on Power Systems.

[12]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[13]  Amir Safdarian,et al.  A Distributed Algorithm for Managing Residential Demand Response in Smart Grids , 2014, IEEE Transactions on Industrial Informatics.

[14]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[15]  Pierluigi Mancarella,et al.  Modelling of household electro-thermal technologies for demand response applications , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[16]  Daniel S. Kirschen,et al.  Assessing the Impact of Wind Power Generation on Operating Costs , 2010, IEEE Transactions on Smart Grid.

[17]  Pierluigi Mancarella,et al.  Electrical network capacity support from demand side response: Techno-economic assessment of potential business cases for small commercial and residential end-users , 2015 .

[18]  E.F. El-Saadany,et al.  Supply Adequacy Assessment of Distribution System Including Wind-Based DG During Different Modes of Operation , 2010, IEEE Transactions on Power Systems.

[19]  Roy Billinton,et al.  Reliability evaluation of power systems , 1984 .

[20]  Qiuwei Wu,et al.  Direct Load Control (DLC) Considering Nodal Interrupted Energy Assessment Rate (NIEAR) in Restructured Power Systems , 2010, IEEE Transactions on Power Systems.

[21]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[22]  David Infield,et al.  Domestic electricity use: A high-resolution energy demand model , 2010 .

[23]  Hsiao-Dong Chiang,et al.  Improving Service Restoration of Power Distribution Systems Through Load Curtailment of In-Service Customers , 2011, IEEE Transactions on Power Systems.

[24]  Siripha Junlakarn,et al.  Distribution System Reliability Options and Utility Liability , 2014, IEEE Transactions on Smart Grid.

[25]  Pierluigi Mancarella,et al.  Modelling and assessment of the contribution of demand response and electrical energy storage to adequacy of supply , 2015 .

[26]  Gerard Ledwich,et al.  Demand Response for Residential Appliances via Customer Reward Scheme , 2014, IEEE Transactions on Smart Grid.

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

[28]  K. K. Kariuki,et al.  Evaluation of reliability worth and value of lost load , 1996 .

[29]  Rana Mukerji Demand response in the NYISO markets , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.