Energy flexibility quantification of grid-responsive buildings: Energy flexibility index and assessment of their effectiveness for applications

Abstract The demand side is increasingly expected to provide energy flexibility for power grid economy and reliability. Buildings have various flexibility sources that can be effectively utilized for such purposes. According to different requirements of demand responses to power grid on response duration, response direction and response speed (within seconds, minutes, or even longer timescales), building energy flexibility is categorized as fast regulation, moderate regulation, load shedding, load shifting and load covering. In this paper, a comprehensive method is proposed to quantify building energy flexibility based on these categories. Two sets of flexibility indexes (flexibility capacities and flexibility ratios) for the above five energy flexibilities are proposed. An implementation case study is conducted to illustrate the use of these indexes and to validate the effectiveness of using them in flexibility performance assessment of buildings in particular. The impacts of different system design and control parameters on flexibility performance are also investigated quantitatively. The potential economic benefits of utilizing those energy flexibilities are analyzed in a real electricity market with an optimized use of different flexibility sources. Results show that electricity costs can be reduced by up to 21% if the market is available for such grid-responsive buildings.

[1]  Geert Deconinck,et al.  Demand response flexibility and flexibility potential of residential smart appliances: Experiences from large pilot test in Belgium , 2015 .

[2]  Chongqing Kang,et al.  Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life , 2016, IEEE Transactions on Smart Grid.

[3]  Ross Baldick,et al.  Dynamic Demand Response Controller Based on Real-Time Retail Price for Residential Buildings , 2014, IEEE Transactions on Smart Grid.

[4]  Shengwei Wang,et al.  Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective , 2020 .

[5]  Shengwei Wang,et al.  Development of grid-responsive buildings: Opportunities, challenges, capabilities and applications of HVAC systems in non-residential buildings in providing ancillary services by fast demand responses to smart grids , 2019, Applied Energy.

[6]  Ankit Jain,et al.  2015 California Demand Response Potential Study - Charting California’s Demand Response Future: Interim Report on Phase 1 Results , 2016 .

[7]  Xue Gao,et al.  PV-Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer With Distributed PV System , 2020, IEEE Transactions on Industry Applications.

[8]  Jean-Jacques Roux,et al.  Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass , 2015 .

[9]  P. A. Østergaard,et al.  Assessment and evaluation of flexible demand in a Danish future energy scenario , 2014 .

[10]  Hanchen Xu,et al.  Fundamentals and business model for resource aggregator of demand response in electricity markets , 2020 .

[11]  Peng Xu,et al.  Quantification of electricity flexibility in demand response: Office building case study , 2019, Energy.

[12]  Peter Cappers,et al.  Demand Response for Ancillary Services , 2013, IEEE Transactions on Smart Grid.

[13]  Cesar A. Silva-Monroy,et al.  A comparison of policies on the participation of storage in U.S. frequency regulation markets , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[14]  Zhao Zhen,et al.  A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework , 2020 .

[15]  Meng Wu,et al.  Optimal Participation of Price-Maker Battery Energy Storage Systems in Energy, Reserve and Pay as Performance Regulation Markets , 2019, 2019 North American Power Symposium (NAPS).

[16]  Victor M. Zavala,et al.  A multi-scale optimization framework for electricity market participation , 2017 .

[17]  Christos S. Ioakimidis,et al.  Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot , 2018 .

[18]  Xiaodong Cao,et al.  Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade , 2016 .

[19]  Sean P. Meyn,et al.  Ancillary Service to the Grid Through Control of Fans in Commercial Building HVAC Systems , 2014, IEEE Transactions on Smart Grid.

[20]  William Chung,et al.  Benchmarking the energy efficiency of commercial buildings , 2006 .

[21]  David Fischer,et al.  Model-based flexibility assessment of a residential heat pump pool , 2017 .

[22]  Pertti Järventausta,et al.  Using electrical energy storage in residential buildings – Sizing of battery and photovoltaic panels based on electricity cost optimization , 2019, Applied Energy.

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

[24]  William Chung,et al.  Review of building energy-use performance benchmarking methodologies , 2011 .

[25]  Fei Wang,et al.  Smart Households’ Aggregated Capacity Forecasting for Load Aggregators Under Incentive-Based Demand Response Programs , 2020, IEEE Transactions on Industry Applications.

[26]  Sila Kiliccote,et al.  Field Experience with and Potential for Multi-time Scale Grid Transactions from Responsive Commercial Buildings: , 2014 .

[27]  Alice Mugnini,et al.  Energy flexible buildings: A methodology for rating the flexibility performance of buildings with electric heating and cooling systems , 2019, Applied Energy.

[28]  Fu Xiao,et al.  Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids , 2019, Applied Energy.

[29]  Kangping Li,et al.  Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation , 2019, Applied Energy.

[30]  M. Behrangrad A review of demand side management business models in the electricity market , 2015 .

[31]  Hamidreza Zareipour,et al.  Operation Scheduling of Battery Storage Systems in Joint Energy and Ancillary Services Markets , 2017, IEEE Transactions on Sustainable Energy.

[32]  Shengwei Wang,et al.  A direct load control strategy of centralized air-conditioning systems for building fast demand response to urgent requests of smart grids , 2018 .

[33]  Fei Wang,et al.  Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description , 2018, IEEE Transactions on Smart Grid.

[34]  Shengwei Wang,et al.  Experimental study on the dynamics, quality and impacts of using variable-speed pumps in buildings for frequency regulation of smart power grids , 2020 .

[35]  Li Xiaolei,et al.  Using Dimmable Lighting for Regulation Capacity and Non-Spinning Reserves in the Ancillary Services Market. A Feasibility Study. , 2011 .