Integrated Planning of Renewable-Dominated Energy Systems and Demand Side Resources
In the present paper, the effect of an integrated planning of renewable energy resources, electrical and thermal storage, electric vehicles charger strategy, and various side demand response programs on electrical and thermal load supplying cost of a residential area was investigated. In this regard, a mathematical model was presented for an optimal integrated planning of renewable energy resources, heating equipment of buildings, thermal and electrical storages, electrical vehicles with respect to demand side management. The proposed model for optimal planning has been formulated as a mixed integer linear programing model. In this model, operational constraints of distribution network were considered. In order to evaluate the performance of the proposed model, its efficiency on electrical and thermal resources of a residential area with certain number of buildings was analyzed. The simulation obtained results revealed that using thermal and electrical storages and side demand response programs as well as the presence of a high number of electric vehicles can be followed by many merits when it is controlled by an energy management system. The outmost advantage is power absorption in low-load hours and releasing it in peak hours. This advantage causes decreasing load peak for electricity network and subsequently, decreasing the cost of supplying a residential area’s electricity. It is useful for both residents and operator. As found, it can be generally stated that an optimal and integrated planning of energy resources with respect to side demand management significantly decrease energy supply costs. According to simulation results, an integrated planning of all energy resources of a residential area can decrease consumption power in peak hours, energy supply cost as well as the need of buying it from a distribution network. This is in favor of both consumers and distributor company.
 N. Good and P. Mancarella, “Flexibility in Multi-Energy Communities with Electrical and Thermal Storage: A Stochastic, Robust Approach for Multi-Service Demand Response,” IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 503–513, Jan. 2019.
 A. O’Connell, D. Flynn, and A. Keane, “Rolling multi-period optimization to control electric vehicle charging in distribution networks,” IEEE Trans. Power Syst., vol. 29, no. 1, pp. 340–348, 2014.
 M. F. Shaaban, M. Ismail, E. F. El-Saadany, and W. Zhuang, “Real-time PEV charging/discharging coordination in smart distribution systems,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1797–1807, 2014.
 C. Ma, F. Marten, J.-C. Töbermann, and M. Braun, “Evaluation of modeling and simulation complexity on studying the impacts of electrical vehicles fleets in distribution systems,” in Power Systems Computation Conference (PSCC), 2014, 2014, pp. 1–7.
 U. C. Chukwu and S. M. Mahajan, “Real-time management of power systems with V2G facility for smart-grid applications,” IEEE Trans Sustain Energy, vol. 5, no. 2, pp. 558–566, 2014.
 T. Ma and O. Mohammed, “Economic analysis of real-time large scale PEVs network power flow control algorithm with the consideration of V2G services,” in Industry Applications Society Annual Meeting, 2013 IEEE, 2013, pp. 1–8.
 J. Rivera, P. Wolfrum, S. Hirche, C. Goebel, and H.-A. Jacobsen, “Alternating direction method of multipliers for decentralized electric vehicle charging control,” in Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on, 2013, pp. 6960–6965.
 E. Sortomme, M. M. Hindi, S. J. MacPherson, and S. S. Venkata, “Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses,” IEEE Trans. Smart Grid, vol. 2, no. 1, pp. 198–205, 2011.
 K. Clement-Nyns, E. Haesen, and J. Driesen, “The impact of charging plug-in hybrid electric vehicles on a residential distribution grid,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 371–380, 2010.
 W. J. Smith, “Can EV (electric vehicles) address Ireland’s CO2 emissions from transport?,” Energy, vol. 35, no. 12, pp. 4514–4521, 2010.
 C. O. Adika and L. Wang, “Demand-side bidding strategy for residential energy management in a smart grid environment,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1724–1733, 2014.
D. Steen, O. Carlson, and L. Bertling, “Assessment of electric vehicle charging scenarios based on demographical data,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1457–1468, 2012.
 L. Jian, H. Xue, G. Xu, X. Zhu, D. Zhao, and Z. Y. Shao, “Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid.,” IEEE Trans Ind. Electron., vol. 60, no. 8, pp. 3218–3226, 2013.
 Y. He, B. Venkatesh, and L. Guan, “Optimal scheduling for charging and discharging of electric vehicles,” IEEE Trans. Smart Grid, vol. 3, no. 3, pp. 1095–1105, 2012.
 O. Sundstrom and C. Binding, “Flexible charging optimization for electric vehicles considering distribution grid constraints,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 26–37, 2012.
 P. J. Douglass, R. Garcia-Valle, P. Nyeng, J. Østergaard, and M. Togeby, “Smart demand for frequency regulation: Experimental results,” IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 1713–1720, 2013.
 M. Giuntoli and D. Poli, “Optimized thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storages,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 942–955, 2013.
 M. Loesch, D. Hufnagel, S. Steuer, T. Fabnacht, and H. Schmeck, “Demand side management in smart buildings by intelligent scheduling of heat pumps,” in Intelligent Energy and Power Systems (IEPS), 2014 IEEE International Conference on, 2014, pp. 1–6.
 X. Luo, C. K. Lee, W. M. Ng, S. Yan, B. Chaudhuri, and S. Y. R. Hui, “Use of adaptive thermal storage system as smart load for voltage control and demand response,” IEEE Trans. Smart Grid, vol. 8, no. 3, pp. 1231–1241, 2017.
 J. Heier, C. Bales, and V. Martin, “Combining thermal energy storage with buildings–a review,” Renew. Sustain. Energy Rev., vol. 42, pp. 1305–1325, 2015.
 I. Dincer, “On thermal energy storage systems and applications in buildings,” Energy Build., vol. 34, no. 4, pp. 377–388, 2002.
 B. He, “High-capacity cool thermal energy storage for peak shaving-A solution for energy challenges in the 21st century,” Kemiteknik, 2004.
 K. Hayashi, “Research and development on high-density cold latent-heat medium transportation technology,” in IEAAnnex-10-PCMs and Chemical Reactions for Thermal Energy Storage 4th Workshop, Tsu, Japan (2000), 2000.
 A. Heinz, “PCM storage to reduce cycling rates for boilers,” Graz Univ. Technol., 2007.
 L. S. Johansson, B. Leckner, L. Gustavsson, D. Cooper, C. Tullin, and A. Potter, “Emission characteristics of modern and old-type residential boilers fired with wood logs and wood pellets,” Atmos. Environ., vol. 38, no. 25, pp. 4183–4195, 2004.
 F. Boshell and O. P. Veloza, “Review of developed demand side management programs including different concepts and their results,” in Transmission and Distribution Conference and Exposition: Latin America, 2008 IEEE/PES, 2008, pp. 1–7.
 H. Mortaji, O. S. Hock, M. Moghavvemi, and H. A. Almurib, “Smart grid demand response management using internet of things for load shedding and smart-direct load control,” in Industry Applications Society Annual Meeting, 2016 IEEE, 2016, pp. 1–7.
 A. Y. Saber and G. K. Venayagamoorthy, “Plug-in vehicles and renewable energy sources for cost and emission reductions,” IEEE Trans. Ind. Electron., vol. 58, no. 4, pp. 1229–1238, 2011.
 N. Good and P. Mancarella, “Modelling and assessment of business cases for smart multi-energy districts,” in 2016 Power Systems Computation Conference (PSCC), 2016, pp. 1–7.
 R. Moreno, R. Moreira, and G. Strbac, “A MILP model for optimising multi-service portfolios of distributed energy storage,” Appl. Energy, vol. 137, pp. 554–566, 2015.
 L. Wang and C. Singh, “PSO-based multi-criteria optimum design of a grid-connected hybrid power system with multiple renewable sources of energy,” in Swarm Intelligence Symposium, 2007. SIS 2007. IEEE, 2007, pp. 250–257.
 A.-K. Daud and M. S. Ismail, “Design of isolated hybrid systems minimizing costs and pollutant emissions,” Renew. Energy, vol. 44, pp. 215–224, 2012.
 J. Soares, H. Morais, T. Sousa, Z. Vale, and P. Faria, “Day-Ahead Resource Scheduling Including Demand Response for Electric Vehicles,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 596–605, Mar. 2013.
 N. Good, E. Karangelos, A. Navarro-Espinosa, and P. Mancarella, “Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users’ discomfort,” IEEE Trans. Smart Grid, vol. 6, no. 5, pp. 2333–2342, 2015.