Multi‐objective optimisation operation of thermostatically controllable appliances for voltage management in low‐voltage distribution networks

The development of residential demand response (RDR) programs opens up another perspective for mitigating the problems of voltage magnitude and unbalance limit violation (VMULV) in the low-voltage distribution network (LVDN). Under this background, this study introduces thermostatically controllable appliances (TCAs) including electric water heater (EWH) and air conditioner (AC) to tackle the VMULV problems in LVDN based on the incentive RDR. Firstly, the response characteristics of TCA in LVDN voltage management are analysed. Then, a voltage management model incorporating EWHs and ACs in residents is established as a multi-objective optimisation model that jointly minimises the RDR cost, network loss, and customer dissatisfaction. In this model, the discomfort index associated with hot water and indoor temperature is employed to reflect users' discomfort. The proposed modelling method is examined on a 6-resident network and a 28-resident network in Sweden with considerable unbalanced and distributed generations. Moreover, the impacts on the optimisation result from different objective functions, photovoltaic (PV) penetration levels, and the tolerable temperature ranges set by users are also investigated. The results indicate that the proposed method effectively alleviates voltage amplitude and imbalance limit violation, and improves the capability of LVDN to integrate PV.

[1]  Ali Arefi,et al.  A new approach to voltage management in unbalanced low voltage networks using demand response and OLTC considering consumer preference , 2018, International Journal of Electrical Power & Energy Systems.

[2]  Daniel Vanderpooten,et al.  Weighted sum model with partial preference information: Application to multi-objective optimization , 2017, Eur. J. Oper. Res..

[3]  Jun Zhuang,et al.  Comparison of AHP and Monte Carlo AHP Under Different Levels of Uncertainty , 2015, IEEE Transactions on Engineering Management.

[4]  Peter Wolfs,et al.  Comprehensive optimal photovoltaic inverter control strategy in unbalanced three-phase four-wire low voltage distribution networks , 2014 .

[5]  Wenchuan Wu,et al.  Distributed optimal residential demand response considering operational constraints of unbalanced distribution networks , 2018 .

[6]  Fangxing Li,et al.  A Framework of Residential Demand Aggregation With Financial Incentives , 2018, IEEE Transactions on Smart Grid.

[7]  Jan Meyer,et al.  Stochastic Assessment of Voltage Unbalance Due to Single-Phase-Connected Solar Power , 2017, IEEE Transactions on Power Delivery.

[8]  Yun Seng Lim,et al.  Energy Storage System for Mitigating Voltage Unbalance on Low-Voltage Networks With Photovoltaic Systems , 2012, IEEE Transactions on Power Delivery.

[9]  Ehab F. El-Saadany,et al.  Managing Demand for Plug-in Electric Vehicles in Unbalanced LV Systems With Photovoltaics , 2017, IEEE Transactions on Industrial Informatics.

[10]  Kashem M. Muttaqi,et al.  Community Energy Storage for Neutral Voltage Rise Mitigation in Four-Wire Multigrounded LV Feeders With Unbalanced Solar PV Allocation , 2015, IEEE Transactions on Smart Grid.

[11]  Arindam Ghosh,et al.  Voltage Unbalance Reduction in Low Voltage Feeders by Dynamic Switching of Residential Customers Among Three Phases , 2014, IEEE Transactions on Smart Grid.

[12]  Magdy M. A. Salama,et al.  A Comprehensive Study of the Impacts of PHEVs on Residential Distribution Networks , 2014, IEEE Transactions on Sustainable Energy.

[13]  Iqbal Husain,et al.  Reactive Power Management for Overvoltage Prevention at High PV Penetration in a Low-Voltage Distribution System , 2017 .

[14]  Gerard Ledwich,et al.  Probabilistic Voltage Management Using OLTC and dSTATCOM in Distribution Networks , 2018, IEEE Transactions on Power Delivery.

[15]  João P. S. Catalão,et al.  A Decentralized Electricity Market Scheme Enabling Demand Response Deployment , 2018, IEEE Transactions on Power Systems.

[16]  Ong Hang See,et al.  A review of residential demand response of smart grid , 2016 .

[17]  Johan Driesen,et al.  Optimization control scheme utilizing small-scale distributed generators and OLTC distribution transformers , 2016 .

[18]  J. Faiz,et al.  Differences between conventional and electronic tap-changers and modifications of controller , 2006, IEEE Transactions on Power Delivery.

[19]  Hui Li,et al.  Coordinated Control of Distributed Energy Storage System With Tap Changer Transformers for Voltage Rise Mitigation Under High Photovoltaic Penetration , 2012, IEEE Transactions on Smart Grid.

[20]  Johan Driesen,et al.  Load Balancing With EV Chargers and PV Inverters in Unbalanced Distribution Grids , 2015, IEEE Transactions on Sustainable Energy.

[21]  Canbing Li,et al.  Optimal Sizing of PV and BESS for a Smart Household Considering Different Price Mechanisms , 2018, IEEE Access.

[22]  Peter J. Wolfs,et al.  A Hybrid Model for Residential Loads in a Distribution System With High PV Penetration , 2013, IEEE Transactions on Power Systems.

[23]  S. Gopiya Naik,et al.  Programmable protective device for LV distribution system protection , 2018 .

[24]  Canbing Li,et al.  Energy Hub’s Structural and Operational Optimization for Minimal Energy Usage Costs in Energy Systems , 2018 .

[25]  Peter Wolfs,et al.  A review of high PV penetrations in LV distribution networks: Present status, impacts and mitigation measures , 2016 .

[26]  Omid Abrishambaf,et al.  Demand response implementation in smart households , 2017 .

[27]  P.A.N. Garcia,et al.  Three-Phase Power Flow Based on Four-Conductor Current Injection Method for Unbalanced Distribution Networks , 2008, IEEE Transactions on Power Systems.