Role and Benefits of Flexible Thermostatically Controlled Loads in Future Low-Carbon Systems

Thermostatically controlled loads (TCLs) represent a valuable source of flexibility for the system. Depending on network needs, these devices could alter their nominal energy consumption and provide multiple ancillary services, facilitating the cost-effective transition to a low-carbon power system. Previous work mainly focused on investigating single service provision from TCLs, while intersections among different services have not been considered. Furthermore, the intrinsic energy payback effect was not fully included within optimisation tools for TCL scheduling. This paper presents a novel demand side response model (DSRM), which enables the optimal scheduling of energy/power consumption of a heterogeneous population of TCLs and the simultaneous allocation of multiple ancillary services. The model explicitly considers the effect of the energy recovery after delivering the services so that the deliverability of scheduled services from TCLs is always guaranteed. The proposed DSRM is integrated into an advanced stochastic unit commitment model to investigate the system benefits of the flexibility from TCLs. Case studies demonstrate that: 1) time-varying provision of multiple services from TCLs significantly increases their benefits; 2) TCL operation which aims to minimise the amplitude of the energy recovery causes sub-optimal utilisation of the devices; and 3) ignoring the energy payback leads to overestimate the TCL value.

[1]  Marko Aunedi,et al.  Economic and Environmental Benefits of Dynamic Demand in Providing Frequency Regulation , 2013, IEEE Transactions on Smart Grid.

[2]  Goran Strbac,et al.  Nondisruptive decentralized control of thermal loads with second order thermal models , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[3]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads , 2011, Proceedings of the IEEE.

[4]  Kankar Bhattacharya,et al.  Optimal Operation of Residential Energy Hubs in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[5]  Tyrone L. Vincent,et al.  Potentials and Economics of Residential Thermal Loads Providing Regulation Reserve , 2014, ArXiv.

[6]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.

[7]  Nick Jenkins,et al.  Investigation of Domestic Load Control to Provide Primary Frequency Response Using Smart Meters , 2012, IEEE Transactions on Smart Grid.

[8]  G. Strbac,et al.  Efficient Stochastic Scheduling for Simulation of Wind-Integrated Power Systems , 2012, IEEE Transactions on Power Systems.

[9]  Goran Strbac,et al.  Decentralized Control of Thermostatic Loads for Flexible Demand Response , 2015, IEEE Transactions on Control Systems Technology.

[10]  P. Siano,et al.  Assessing the benefits of residential demand response in a real time distribution energy market , 2016 .

[11]  Anna Scaglione,et al.  Reduced-Order Load Models for Large Populations of Flexible Appliances , 2015, IEEE Transactions on Power Systems.

[12]  Yang Shi,et al.  Distributed MPC of Aggregated Heterogeneous Thermostatically Controlled Loads in Smart Grid , 2016, IEEE Transactions on Industrial Electronics.

[13]  Yang Shi,et al.  Model Predictive Control for Thermostatically Controlled Appliances Providing Balancing Service , 2016, IEEE Transactions on Control Systems Technology.

[14]  Goran Strbac,et al.  Stochastic Scheduling With Inertia-Dependent Fast Frequency Response Requirements , 2016, IEEE Transactions on Power Systems.

[15]  M. A. Zehir,et al.  Review and comparison of demand response options for more effective use of renewable energy at consumer level , 2016 .

[16]  D.G. Infield,et al.  Stabilization of Grid Frequency Through Dynamic Demand Control , 2007, IEEE Transactions on Power Systems.

[17]  A. Mullane,et al.  Frequency control and wind turbine technologies , 2005, IEEE Transactions on Power Systems.

[18]  Yongpei Guan,et al.  Two-stage robust optimization for N-k contingency-constrained unit commitment , 2012, IEEE Transactions on Power Systems.

[19]  Lang Tong,et al.  Dynamic Pricing and Distributed Energy Management for Demand Response , 2016, IEEE Transactions on Smart Grid.

[20]  Johanna L. Mathieu,et al.  State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance , 2013 .

[21]  Kameshwar Poolla,et al.  Fast Regulation Service Provision via Aggregation of Thermostatically Controlled Loads , 2014, 2014 47th Hawaii International Conference on System Sciences.

[22]  Francois Bouffard,et al.  Contingency-Type Reserve Leveraged Through Aggregated Thermostatically-Controlled Loads—Part I: Characterization and Control , 2014, IEEE Transactions on Power Systems.

[23]  E. Karangelos,et al.  Towards Full Integration of Demand-Side Resources in Joint Forward Energy/Reserve Electricity Markets , 2012, IEEE Transactions on Power Systems.

[24]  Goran Strbac,et al.  Leaky storage model for optimal multi-service allocation of thermostatic loads , 2016 .

[25]  Jianzhong Wu,et al.  Power System Frequency Response From the Control of Bitumen Tanks , 2016, IEEE Transactions on Power Systems.

[26]  Goran Strbac,et al.  A MILP model for optimising multi-service portfolios of distributed energy storage , 2015 .

[27]  David Angeli,et al.  A Stochastic Approach to “Dynamic-Demand” Refrigerator Control , 2012, IEEE Transactions on Control Systems Technology.

[28]  William D'haeseleer,et al.  Active demand response with electric heating systems: Impact of market penetration , 2016 .

[29]  G. Strbac,et al.  Decentralized Participation of Flexible Demand in Electricity Markets—Part I: Market Mechanism , 2013, IEEE Transactions on Power Systems.

[30]  Duncan S. Callaway,et al.  Arbitraging Intraday Wholesale Energy Market Prices With Aggregations of Thermostatic Loads , 2015, IEEE Transactions on Power Systems.

[31]  Ning Lu,et al.  Improving the Centralized Control of Thermostatically Controlled Appliances by Obtaining the Right Information , 2015, IEEE Transactions on Smart Grid.

[32]  Tyrone L. Vincent,et al.  Aggregate Flexibility of Thermostatically Controlled Loads , 2015, IEEE Transactions on Power Systems.

[33]  J. Driesen,et al.  Primary and Secondary Frequency Support by a Multi-Agent Demand Control System , 2015, IEEE Transactions on Power Systems.

[34]  Wei Zhang,et al.  Aggregated Modeling and Control of Air Conditioning Loads for Demand Response , 2013, IEEE Transactions on Power Systems.