Zhang, X. H., Zhao, J. Q., & Chen, X. Y. (2011). Multi-objective unit commitment modeling and optimization for energy-saving and emission reduction in wind power integrated system. *Power System Protection and Control, 39*(17), 33–39.

Google Scholar

Wang, M., Mu, Y., Jia, H., Wu, J., Yu, X., & Qi, Y. (2017). Active power regulation for large-scale wind farms through an efficient power plant model of electric vehicles. *Applied Energy, 185*, 1673–1683.

Article
Google Scholar

Liao, Z., Chen, S., & Lin, C. (2018). Distribution network voltage state assessment with distributed generation based on improved probabilistic power flow method. In *DEStech Transactions on Environment, Energy and Earth Sciences, (appeec)*.

Google Scholar

China Reform Daily. The accumulative installed scale of hydropower, wind power and photovoltaic power in China ranks first in the world in 2018 [EB/OL]. http://www.cspplaza.com/article-15572-1.html*,* 2019-07-04.

Sina Southern Energy. Construction of renewable energy power generation exceeding 1/4 peak shaving power supply in 2018 is accelerated [EB/OL]. http://gd.sina.cn/energy/2019-06-28/detail-ihytcitk8214889.d.html?vt=4&wm=2256_3664&cid=183609*,* 2019-06-28.

Ren, S., Yang, X., Zhang, Y., Zhao, B., Xie, L., & Weng, G. (2017). A real time optimization strategy for microgrid integrated with schedulable ability and uncertainties. In *In Proceedings of the CSEE*.

Google Scholar

Huang, W., Zhang, N., Kang, C., Li, M., & Huo, M. (2019). From demand response to integrated demand response: Review and prospect of research and application. *Protection and Control of Modern Power Systems, 4*(1), 12.

Article
Google Scholar

Cui, W., Ding, Y., Hui, H., Lin, Z., Du, P., Song, Y., & Shao, C. (2018). Evaluation and sequential dispatch of operating reserve provided by air conditioners considering Lead–lag rebound effect. *IEEE Transactions on Power Systems, 33*(6), 6935–6950.

Article
Google Scholar

Wang, Y., Chen, Q., Kang, C., Zhang, M., Wang, K., & Zhao, Y. (2015). Load profiling and its application to demand response: A review. *Tsinghua Science and Technology, 20*(2), 117–129.

Article
Google Scholar

Siano, P. (2014). Demand response and smart grids—A survey. *Renewable and Sustainable Energy Reviews, 30*, 461–478.

Article
Google Scholar

Bie, Z., Xie, H., Hu, G., & Li, G. (2015). Optimal scheduling of power systems considering demand response. *Journal of Modern Power Systems and Clean Energy, 4*(2), 180.

Article
Google Scholar

Xie, K., Zhang, K., Luan, K., et al. (2019). Exploration of demand response score scheme under the background of electric power system reform. *Power Demand Side Management, 21*(03), 7–10.

Google Scholar

Qi, Y., Wang, D., Jia, H., Chen, N., Wei, W., Liu, K., & Fan, M. (2017). Research on under frequency load shedding strategy using aggregated thermostatically controlled loads based on demand response. Proceedings of the CSEE, 751-760.

Li, N., & Wang, X. (2015). Research of air conditioners providing frequency controlled reserve for microgrid. *Power Syst Prot Control, 43*, 101–105.

MathSciNet
Google Scholar

Wang, Y., Tong, Y., & Huang, M. (2017). Research on virtual energy storage model of air conditioning loads based on demand response. *Power System Technology, 41*(2), 394–401.

Google Scholar

Hui, H., Ding, Y., Lin, Z. Z., Siano, P., & Song, Y. (2019). Capacity allocation and optimal control of inverter air conditioners considering area control error in multi-area power systems. *IEEE Transactions on Power Systems*.

Song, M., Gao, C., & Su, W. (2016). Modeling and controlling of air-conditioning load for demand response applications. *Autom Electr Power Syst, 40*(14), 158–167.

Google Scholar

Ding, Y., Song, Y. H., Hui, H., & Shao, C. (2019). *Integration of air conditioning and heating into modern power systems: enabling*. Springer.

Qingze, W., Xingying, C., Qingguo, Y., Shiming, X., & Yingchen, L. (2017). Two-layer flexible optimal strategy for air-conditioning of office building under TOU price [J]. *Power System Protection and Control, 45*(21), 43–50.

Google Scholar

Hui, H., Ding, Y., & Zheng, M. (2018). Equivalent modeling of inverter air conditioners for providing frequency regulation service. *IEEE Transactions on Industrial Electronics, 66*(2), 1413–1423.

Article
Google Scholar

Xiao, H. , Ming, Z. , & Gengyin, L. I. . (2018). Multi-objective optimal dispatching of active distribution networks considering energy storage systems and air-conditioning loads. Power System Protection and Control*.*

Jin, X., Mu, Y., Jia, H., Wu, J., Jiang, T., & Yu, X. (2017). Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system. *Applied Energy, 194*, 386–398.

Article
Google Scholar

Soroudi, A., Siano, P., & Keane, A. (2015). Optimal DR and ESS scheduling for distribution losses payments minimization under electricity price uncertainty. *IEEE Transactions on Smart Grid, 7*(1), 261–272.

Article
Google Scholar

Jianlin, L. I., Huimeng, M. A., Xiaodong, Y., Zhan, W., & Le, G. E. (2017). Overview on key applied technologies of large-scale distributed energy storage. *Power System Technology*.

Wei, L., Zhao, B., Wu, H., & Zhang, X. (2015). Optimal allocation model of energy storage system in virtual power plant environment with a high penetration of distributed photovoltaic generation. *Autom. Electr Power Syst, 23*, 66–74.

Google Scholar

Lei, F. , Yugang, N. , Siming, W. , & Tinggang, J. . (2018). Optimal capacity determination method based on day-ahead scheduling and real-time control. Power System Protection and Control.

Byrne, R. H., Nguyen, T. A., Copp, D. A., Chalamala, B. R., & Gyuk, I. (2017). Energy management and optimization methods for grid energy storage systems. *IEEE Access, 6*, 13231–13260.

Article
Google Scholar

Meng, L. I. U., LIANG, W., & ZHANG, Y. (2017). Cooperative generation-load optimal dispatching model considering airconditioning load group control. *Power System Technology, 41*(4), 1230–1236.

Google Scholar

Wang, D., Zeng, R., & Mu, Y. (2015). An optimization method for new energy utilization using thermostatically controlled appliances. *Power System Technology, 39*(12), 3457–3462.

Google Scholar

Chenxing, Y. A. N. G., Qingshan, X. U., & Xufang, W. A. N. G. (2017). Strategy of constructing virtual peaking unit by public buildings’ central air conditioning loads for day-ahead power dispatching. *Journal of Modern Power Systems and Clean Energy, 5*(2), 187–201.

Article
Google Scholar

Hui, H., Ding, Y., Liu, W., Lin, Y., & Song, Y. (2017). Operating reserve evaluation of aggregated air conditioners. *Applied Energy, 196*, 218–228.

Article
Google Scholar

Xie, D., Hui, H., Ding, Y., & Lin, Z. (2018). Operating reserve capacity evaluation of aggregated heterogeneous TCLs with price signals. *Applied Energy, 216*, 338–347.

Article
Google Scholar

Iacovella, S., Ruelens, F., Vingerhoets, P., Claessens, B., & Deconinck, G. (2015). Cluster control of heterogeneous thermostatically controlled loads using tracer devices. *IEEE Transactions on Smart Grid, 8*(2), 528–536.

Google Scholar

Zhao, H., Wu, Q., Huang, S., Zhang, H., Liu, Y., & Xue, Y. (2016). Hierarchical control of thermostatically controlled loads for primary frequency support. *IEEE Transactions on Smart Grid, 9*(4), 2986–2998.

Article
Google Scholar

Ning, L. (2012). An evaluation of the hvac load potential for providing load balancing service. *IEEE Transactions on Smart Grid, 3*(3), 1263–1270.

Article
Google Scholar

Ding, Y., Cui, W., Zhang, S., Hui, H., Qiu, Y., & Song, Y. (2019). Multi-state operating reserve model of aggregate thermostatically-controlled-loads for power system short-term reliability evaluation. *Applied Energy*, 241.

Hui, H., Ding, Y., & Yang, S. (2019). Modeling and analysis of inverter air conditioners for primary frequency control considering signal delays and detection errors. *Energy Procedia, 158*, 4003–4010.

Article
Google Scholar

Kim, Y. J., Norford, L. K., & Kirtley, J. L. (2014). Modeling and analysis of a variable speed heat pump for frequency regulation through direct load control. *IEEE Transactions on Power Systems, 30*(1), 397–408.

Article
Google Scholar

Kim, Y. J., Fuentes, E., & Norford, L. K. (2015). Experimental study of grid frequency regulation ancillary service of a variable speed heat pump. *IEEE Transactions on Power Systems, 31*(4), 3090–3099.

Article
Google Scholar

Song, M., Gao, C., Yang, J., & Yan, H. (2018). Energy storage modeling of inverter air conditioning for output optimizing of wind generation in the electricity market. *CSEE Journal of Power and Energy Systems, 4*(3), 305–315.

Article
Google Scholar

Jin, X., Wu, J., Mu, Y., Wang, M., Xu, X., & Jia, H. (2017). Hierarchical microgrid energy management in an office building. *Applied Energy, 208*, 480–494.

Article
Google Scholar

Khan, S., Shahzad, M., Habib, U., Gawlik, W., & Palensky, P. (2016). Stochastic battery model for aggregation of thermostatically controlled loads. In 2016 IEEE International Conference on Industrial Technology (ICIT) (pp. 570-575). IEEE.

Ai, X., Zhao, Y., & Zhou, S. (2016). Study on virtual energy storage features of air conditioning load direct load control. *Proceedings of the CSEE, 36*(6), 1596–1603.

Google Scholar

Congwei, T. U. , Jun, C. , Dongli, Y. U. , & Xiangyang, M. . (2019). Control strategy of virtual energy storage system participating in frequency modulation based on air conditioning loads. Power Demand Side Management*.*

Wang, D., Ge, S., Jia, H., Wang, C., Zhou, Y., Lu, N., & Kong, X. (2014). A demand response and battery storage coordination algorithm for providing microgrid tie-line smoothing services. *IEEE Transactions on Sustainable Energy, 5*(2), 476–486.

Article
Google Scholar

Sanandaji, B. M., Hao, H., Poolla, K., & Vincent, T. L. (2014, June). Improved battery models of an aggregation of thermostatically controlled loads for frequency regulation. In 2014 American Control Conference (pp. 38-45). IEEE.

Zhaoyu, C. H. E. N., Dan, W. A. N. G., & Hongjie, J. (2017). Optimal smoothing control strategy of virtual energy storage system in microgrid based on continuous state constraints. *Power System Technology, 41*(1), 55–63.

Google Scholar

Hao, H., Sanandaji, B. M., Poolla, K., & Vincent, T. L. (2014). Aggregate flexibility of thermostatically controlled loads. *IEEE Transactions on Power Systems, 30*(1), 189–198.

Article
Google Scholar

Hughes, J. T., Domínguez-García, A. D., & Poolla, K. (2015). Virtual battery models for load flexibility from commercial buildings. In 2015 48th Hawaii International Conference on System Sciences (pp. 2627-2635). IEEE.

Zhao, L., Zhang, W. (2016). A geometric approach to virtual battery modeling of thermostatically controlled loads. In 2016 American Control Conference (ACC) (pp. 1452-1457). IEEE.

Hao, H., Sanandaji, B. M., Poolla, K., & Vincent, T. L. (2013). A generalized battery model of a collection of thermostatically controlled loads for providing ancillary service. In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 551-558). IEEE.

Ran, W., Dan, W., Hongjie, J., Zhanyong, Y., & Yebai, Q. I. (2015). A coordination control strategy of battery and virtual energy storage to smooth the micro-grid tie-line power fluctuations. *Proceedings of the CSEE, 35*(20), 5124–5134.

Google Scholar

Jin, X., Mu, Y., Jia, H., Yu, X., & Chen, N. (2017). Optimal scheduling method for a combined cooling, heating and power building microgrid considering virtual storage system at demand side. *In Proceedings of the CSEE* (Vol. 37, no. 2, pp. 581-590).

Zheng, Y., Hill, D., Liu, T., & Meng, K. (2018). Supplementary frequency regulation with multiple virtual energy storage system aggregators. *Electric Power Components and Systems, 46*(16–17), 1719–1730.

Article
Google Scholar

Hakimi, S. M., & Tafreshi, S. M. (2016). Smart virtual energy storage control strategy to cope with uncertainties and increase renewable energy penetration. *Journal of Energy Storage, 6*, 80–94.

Article
Google Scholar

Wang, D., Meng, K., Gao, X., Qiu, J., Lai, L. L., & Dong, Z. Y. (2017). Coordinated dispatch of virtual energy storage systems in LV grids for voltage regulation. *IEEE Transactions on Industrial Informatics, 14*(6), 2452–2462.

Article
Google Scholar

Yongjian, Y., Weixin, L., Wenqing, Y., Baosheng, W., Guangshan, L., Gangwei, Y., & Le, K. (2018). Research on dynamic adaptive droop control strategy for microgrid. In 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2) (pp. 1-4). IEEE.

Zhang, Y., Guo, L., Jia, H., Li, Z., & Lu, Z. (2012). An energy storage control method based on state of charge and variable filter time constant [J]. *Automation of Electric Power Systems, 6.*

Zhang, Z., Guo, X., Zhang, X., & Wu, D. (2017). Strategy of smoothing wind power fluctuation based on storage battery. *Power System Protection and Control, 3*, 62–68.

Google Scholar

Tong, Y., You, X., Wang, Y., & Huang, M. (2017). Research on virtual energy storage of air conditioning load. *Journal of Beijing Jiaotong University, 41*(5), 126–131.

Google Scholar

Chassin, D. P., Stoustrup, J., Agathoklis, P., & Djilali, N. (2015). A new thermostat for real-time price demand response: Cost, comfort and energy impacts of discrete-time control without deadband. *Appl Energy, 155*, 816–825.

Article
Google Scholar

Zhao, Y., Lu, Y., Yan, C., & Wang, S. (2015). MPC-based optimal scheduling of grid-connected low energy buildings with thermal energy storages. *Energy and Buildings, 86*, 415–426.

Article
Google Scholar

Shi, Q., Cui, H., Li, F., Liu, Y., Ju, W., & Sun, Y. (2017). A hybrid dynamic demand control strategy for power system frequency regulation. *CSEE Journal of Power and Energy Systems, 3*(2), 176–185.

Article
Google Scholar

Shi, Q., Li, F., Hu, Q., & Wang, Z. (2018). Dynamic demand control for system frequency regulation: Concept review, algorithm comparison, and future vision. *Electr Power Syst Res, 154*, 75–87.

Article
Google Scholar

Dong, D., Zongqi, L., & Shuili, Y. (2015). Battery energy storage aid automatic generation control for load frequency control based on fuzzy control. *Power System Protection and Control, 43*(8), 81–87.

Google Scholar

Cheng, M., Sami, S. S., & Wu, J. (2017). Benefits of using virtual energy storage system for power system frequency response. *Appl Energy, 194*, 376–385.

Article
Google Scholar

Shu, Y., Zhang, Z., Guo, J., & Zhang, Z. L. (2017). Study on key factors and solution of renewable energy accommodation. *Proceedings of the CSEE, 37*(1), 1–8.

MathSciNet
Google Scholar

Yuhang, X., Junyong, L., Chao, F., Xiaoyu, L., Chen, W. U., & Zhengwen, H. (2016). Optimal scheduling model of virtual power plant considering demand response. *Power System Technology.*

Li, S., Jiang, C., Zhao, Z., & Li, Z. (2017). Study of transient voltage stability for distributed photovoltaic power plant integration into low voltage distribution network. *Power System Protection and Control, 45*(8), 67–72.

Google Scholar

Bijun, L. I., & Yuqiang, H. (2016). Research of emergency load regulation for security and stability control [J]. *Power System Protection and Control, 44*(11), 104–110.

Google Scholar

Mehta, N., Sinitsyn, N. A., Backhaus, S., & Lesieutre, B. C. (2014). Safe control of thermostatically controlled loads with installed timers for demand side management. *Energy Conversion and Management, 86*, 784–791.

Article
Google Scholar

Liu, M., Shi, Y., & Liu, X. (2015). Distributed MPC of aggregated heterogeneous thermostatically controlled loads in smart grid. *IEEE Transactions on Industrial Electronics, 63*(2), 1120–1129.

Article
Google Scholar

Vrettos, E., Tang, Y., Xu, Y., & Xu, Y. (2019). A distributed control scheme of thermostatically controlled loads for the building-microgrid community. *IEEE Transactions on Sustainable Energy*.

Li, W., Lian, J., Engel, D., & Wang, H. (2018). Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads. *Journal of Control and Decision, 5*(2), 148–168.

Article
Google Scholar

Yang, J., Liu, T., Wang, H., Tian, Z., & Liu, S. (2019). Optimizing the regulation of aggregated thermostatically controlled loads by jointly considering consumer comfort and tracking error. *Energies, 12*(9), 1757.

Article
Google Scholar

Wang, B., Zhu, F., Ji, W., & Cao, Y. (2016). Load cutting potential modeling of central air-conditioning and analysis on influencing factors. *Automation of Electric Power Systems, 40*(19), 44–52.

Article
Google Scholar

Yin, R., Kara, E. C., Li, Y., DeForest, N., Wang, K., Yong, T., & Stadler, M. (2016). Quantifying flexibility of commercial and residential loads for demand response using setpoint changes. *Appl Energy, 177*, 149–164.

Article
Google Scholar

Yaping, L. I. , Ke, W. , Xiaorui, G. , Dan, Z. , & Wenbo, M. . (2015). Demand response potential based on multi-scenarios assessment in regional power system. Power System and Clean Energy.

Google Scholar

Wang Yuan; Zhou Ming; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources ( North China Electric Power University ); (2017). Demand response potential evaluation method of time-of-use price for residential community. Electric Power Construction.

Yuchao, Z., Jianxue, W., & Xiaoyu, C. (2018). Direct control strategy of central air-conditioning load and its schedulable potential evaluation. *Electric Power Automation Equipment, 38*(5), 227–234.

Google Scholar

Alimohammadisagvand, B., Jokisalo, J., Kilpeläinen, S., Ali, M., & Sirén, K. (2016). Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control. *Appl Energy, 174*, 275–287.

Article
Google Scholar

Schibuola, L., Scarpa, M., & Tambani, C. (2015). Demand response management by means of heat pumps controlled via real time pricing. *Energy and Buildings, 90*, 15–28.

Article
Google Scholar

Wang, D., Fan, M., & Jia, H. (2014). User comfort constraint demand response for residential thermostatically-controlled loads and efficient power plant modeling. *Proceedings of the CSEE, 34*(13), 2071–2077.

Google Scholar

Wang, J. X., Zhong, H. W., Xia, Q., & Yang, S. (2016). Model and method of demand response for thermostatically-controlled loads based on cost-benefit analysis. *Automation of Electric Power Systems, 40*(5), 45–53.

Google Scholar

Vanouni, M., & Lu, N. (2014). Improving the centralized control of thermostatically controlled appliances by obtaining the right information. *IEEE Transactions on Smart Grid, 6*(2), 946–948.

Article
Google Scholar

Lu, N., & Zhang, Y. (2012). Design considerations of a centralized load controller using thermostatically controlled appliances for continuous regulation reserves. *IEEE Transactions on Smart Grid, 4*(2), 914–921.

Article
Google Scholar

Molina-Garcia, A., Bouffard, F., & Kirschen, D. S. (2010). Decentralized demand-side contribution to primary frequency control. *IEEE Trans Power Syst, 26*(1), 411–419.

Article
Google Scholar

Tindemans, S. H., Trovato, V., & Strbac, G. (2015). Decentralized control of thermostatic loads for flexible demand response. *IEEE Transactions on Control Systems Technology, 23*(5), 1685–1700.

Article
Google Scholar

Vrettos, E., Ziras, C., & Andersson, G. (2016). Fast and reliable primary frequency reserves from refrigerators with decentralized stochastic control. *IEEE Transactions on Power Systems, 32*(4), 2924–2941.

Article
Google Scholar

Shilei, D., Mingyu, W. . (2018). Cooperative control strategy of dc microgrid power flow controller and distributed energy storage system. Power System Protection and Control*.*

Jinzhou, F. U., Ming, S. . (2018). Energy management strategy based on weather condition for photovoltaic-energy storage integrated power system. Power System Protection and Control.

Zilong, Y., Zhenhao, S., Jing, P., et al. (2015). Multi-mode coordinated control strategy of distributed PV and energy storage system. *Proceeding of the CSEE, 39*(8), 2213–2220.

Google Scholar

Jia, H., Ding, Y., Song, Y., Singh, C., & Li, M. (2018). Operating reliability evaluation of power systems considering flexible reserve provider in demand side. *IEEE Transactions on Smart Grid, 10*(3), 3452–3464.

Article
Google Scholar