Implementation of a price-driven demand response in a distributed energy system with multi-energy flexibility measures

Abstract Distributed energy systems are a promising integrated energy technology due to their energy efficiency and environment benefits. However, the increasing complexity of distributed energy systems, integrated with variable renewable energy, requires more intelligent operational methods to achieve economic efficiency, and reduce negative effects on the power grid. In this study, multi-flexibility measures are used to facilitate interaction between a distributed energy system and the power grid. First, a mixed-integer and linear programming model is proposed for optimizing the dispatch of a distributed energy system with minimum operational costs. Then, the price-driven demand response is performed by coordinating flexibility measures optimally in the operation of a distributed energy system in Guangdong province, China. A detailed case study is conducted in which three types of flexibility measures are modeled, and their effects on end-users and power grid are discussed. The optimal results show that each flexibility measure can well response to the time-of-use price. The distributed energy system’s operating costs were reduced by 1.7–12.9% when individual flexibility measures were applied and 19.6% when all the flexibility measures were implemented. However, the price-driven demand response program significantly decreases the tie-line’s (i.e. the power line connecting the distributed energy system to the main power grid) power stability. Three types of ancillary services based on multi-flexibility measures in a distributed energy system are proposed in this paper and optimized based on the e - constraint method to facilitate smooth interaction between the power grid and a distributed energy system. The Pareto frontiers indicate that all the operational costs decrease along with the ancillary service. The technical and economic boundaries of each ancillary service are further determined, which can help distributed energy system operators make more informed operational decisions.

[1]  Anke Weidlich,et al.  Optimal microgrid scheduling with peak load reduction involving an electrolyzer and flexible loads , 2016 .

[2]  Peter B. Luh,et al.  Multi-objective design optimization of distributed energy systems through cost and exergy assessments , 2017, Applied Energy.

[3]  Loi Lei Lai,et al.  Shifting Boundary for price-based residential demand response and applications , 2015 .

[4]  Changhui Yang,et al.  Residential electricity pricing in China: The context of price-based demand response , 2018 .

[5]  Behnam Mohammadi-Ivatloo,et al.  Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response , 2017 .

[6]  Jang-Won Lee,et al.  Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid , 2016, IEEE Transactions on Smart Grid.

[7]  Peter B. Luh,et al.  Operation optimization of a distributed energy system considering energy costs and exergy efficiency , 2015 .

[8]  Le Yang,et al.  Data and analytics to inform energy retrofit of high performance buildings , 2014 .

[9]  Sarah Busche,et al.  Power systems balancing with high penetration renewables: The potential of demand response in Hawaii , 2013 .

[10]  Dongmin Yu,et al.  Peak load management based on hybrid power generation and demand response , 2018, Energy.

[11]  I. Vassileva,et al.  Introducing a demand-based electricity distribution tariff in the residential sector: Demand response and customer perception , 2011 .

[12]  André Pina,et al.  Comparison of different demand response optimization goals on an isolated microgrid , 2018, Sustainable Energy Technologies and Assessments.

[13]  Yakai Lu,et al.  Flexible dispatch of a building energy system using building thermal storage and battery energy storage , 2019, Applied Energy.

[14]  Ning Lu,et al.  A Demand Response and Battery Storage Coordination Algorithm for Providing Microgrid Tie-Line Smoothing Services , 2014, IEEE Transactions on Sustainable Energy.

[15]  Pietro Elia Campana,et al.  Energy flexibility from the consumer: Integrating local electricity and heat supplies in a building , 2018, Applied Energy.

[16]  F. Galiana,et al.  Demand-side reserve offers in joint energy/reserve electricity markets , 2003 .

[17]  Fu Xiao,et al.  A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses , 2019, Applied Energy.

[18]  Anna Joanna Marszal,et al.  IEA EBC Annex 67 Energy Flexible Buildings , 2017 .

[19]  Kang Shushuo,et al.  Current status of distributed energy system in China , 2016 .

[20]  Furong Li,et al.  Demand response in the UK's domestic sector , 2009 .

[21]  Jiajia Yang,et al.  A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis , 2019, IEEE Transactions on Smart Grid.

[22]  Jianwei Huang,et al.  Incentivizing Energy Trading for Interconnected Microgrids , 2016, IEEE Transactions on Smart Grid.

[23]  Li Zhao,et al.  A literature research on feasible application of mixed working fluid in flexible distributed energy system , 2017 .

[24]  Damien Paire,et al.  A price decision approach for multiple multi-energy-supply microgrids considering demand response , 2018, 2018 IEEE International Energy Conference (ENERGYCON).

[25]  R. Ghorbani,et al.  Providing frequency regulation reserve services using demand response scheduling , 2016 .

[26]  Ala Hasan,et al.  Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller , 2019, Energy Conversion and Management.

[27]  Zhe Tian,et al.  The improvement of a simulation model for a distributed CCHP system and its influence on optimal operation cost and strategy , 2016 .

[28]  Jens Hesselbach,et al.  Economic Multiple Model Predictive Control for HVAC Systems - A Case Study for a Food Manufacturer in Germany , 2018 .

[29]  Zhaoxia Wang,et al.  Data center holistic demand response algorithm to smooth microgrid tie-line power fluctuation , 2018, Applied Energy.

[30]  Zechun Hu,et al.  A review on price-driven residential demand response , 2018, Renewable and Sustainable Energy Reviews.

[31]  Chengshan Wang,et al.  A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids , 2018, Applied Energy.

[32]  Andreas Sumper,et al.  Optimization problem for meeting distribution system operator requests in local flexibility markets with distributed energy resources , 2018 .

[33]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.

[34]  John Bagterp Jørgensen,et al.  Price-responsive model predictive control of floor heating systems for demand response using building thermal mass , 2019, Applied Thermal Engineering.

[35]  Hans Christian Gils,et al.  Assessment of the theoretical demand response potential in Europe , 2014 .

[36]  Kazem Zare,et al.  Optimal scheduling of heating and power hubs under economic and environment issues in the presence of peak load management , 2018 .

[37]  Sayyad Nojavan,et al.  Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program , 2017 .

[38]  Andong Wang,et al.  Development of a data driven approach to explore the energy flexibility potential of building clusters , 2018, Applied Energy.

[39]  Tao Jiang,et al.  Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system , 2017 .

[40]  Weiwei Miao,et al.  Hierarchical market integration of responsive loads as spinning reserve , 2013 .