This research presents optimum operation strategies for multi-energy systems as combined cooling, heat, and power systems with the approach of electricity supply priority regime. A grid with energy hub structure including electrical, thermal, and cooling hubs is explored. Accordingly, an energy hub structure integrated with storages and renewable resources is designed. The mathematical model of the operation system for the presented triple generation micro-grid is considered for energy flow to the energy grid. Considering the limitations of the storage system and the performance of the equipment as well as electricity and gas line, a dynamic optimum operation model is prepared on the basis of mixed integer linear programming and is solved in general algebraic modeling system optimization software so as to minimize energy supply costs. For the model verification, different scenarios are developed in a residential building for a typical summer day so that the renewable resources and storages are fed into the system gradually. According to the findings, as each element is included in the micro-energy grid, its operational parameters, viz. the cost of electricity, gas, and pollutant emissions, are improved remarkably. Scenario I includes a combined cooling, heat, and power system, Scenario II is supplemented with renewable solar and wind energy, and Scenario III includes electrical, heat, and cold storages in addition to renewable sources and combined cooling, heat, and power system. Results reveal a decrease in total productivity cost by 12.2% in Scenario II versus Scenario I and 10.9% in Scenario III versus Scenario II.
Di SommaMGraditiGSianoP.Optimal bidding strategy for a DER aggregator in the day-ahead market in the presence of demand flexibility. IEEE Trans Ind Electron2019;
66: 1509–1519.
2.
DuićNRosenMA.Sustainable development of energy systems. Energy Convers Manag2014;
87: 1057–1062.
3.
ChenXGongGWanZ, et al.
Performance study of a dual power source residential CCHP system based on PEMFC and PTSC. Energy Convers Manag2016;
119: 163–176.
4.
LiCZShiYMHuangXH.Sensitivity analysis of energy demands on performance of CCHP system. Energy Convers Manag2008;
49: 3491–3497.
5.
KrauseTAnderssonGFrohlichK, et al.
Multiple-energy carriers: modeling of production, delivery, and consumption. Proc IEEE2011;
99: 15–27.
6.
MancarellaP.MES (multi-energy systems): an overview of concepts and evaluation models. Energy2014;
65: 1–17.
7.
QuelhasAGilEMcCalleyJD, et al.
A multiperiod generalized network flow model of the US integrated energy system: part I – model description. IEEE Trans Power Syst2007;
22: 829.
8.
GeidlMKoeppelGFavre-PerrodP, et al.
Energy hubs for the future. IEEE Power Energy Mag2007;
5: 24–30.
9.
GeidlMAnderssonG. Optimal coupling of energy infrastructures. In: Power Tech, 2007 IEEE, Lausanne, pp.1398–1403. New York: IEEE.
10.
Daryani N and Tohidi S. Economic dispatch of multi-carrier energy systems considering intermittent resources. Energy & Environment 2019; 30(2): 341–362. https://doi.org/10.1177/0958305X18790959
11.
BahramiSSafeF.A financial approach to evaluate an optimized combined cooling, heat and power system. Energy Power Eng2013;
5: 352.
12.
RastegarMFotuhi-FiruzabadM.Load management in a residential energy hub with renewable distributed energy resources. Energy Build2015;
107: 234–242.
13.
BozchaluiMCHashmiSAHassenH, et al.
Optimal operation of residential energy hubs in smart grids. IEEE Trans Smart Grid2012;
3: 1755–1766.
14.
RastegarMFotuhi-FiruzabadMLehtonenM.Home load management in a residential energy hub. Electr Power Syst Res2015;
119: 322–328.
15.
El-ZonkolyAMAbdelazizAYEladlAM.Double-layer firefly algorithm for simultaneous optimal sizing and operation of energy hubs. Electr Power Compon Syst2017;
45: 1846–1857.
16.
WangSFanXHanL, et al.
Improved interval optimization method based on differential evolution for microgrid economic dispatch. Electr Power Compon Syst2015;
43: 1882–1890.
17.
IppolitoMTelarettiEZizzoG, et al. A new device for the control and the connection to the grid of combined RES-based generators and electric storage systems. In: 2013 International Conference on Clean Electrical Power (ICCEP), 2013, pp.262–267. New York: IEEE.
18.
FuQNasiriASolankiA, et al.
Microgrids: architectures, controls, protection, and demonstration. Electr Power Compon Syst2015;
43: 1453–1465.
19.
Emodi NV, Chaiechi T and Alam Beg AR. The impact of climate change on electricity demand in Australia. Energy & Environment 2018; 29(7): 1263–1297.
20.
Vatankhah Barenji R, Ghadiri Nejad M, and Asghari I. Optimally sized design of a wind/photovoltaic/fuel cell off-grid hybrid energy system by modified-gray wolf optimization algorithm. Energy & Environment 2018; 29(6): 1053–1070.
21.
GraditiGDi SilvestreMLGalleaR, et al.
Heuristic-based shiftable loads optimal management in smart micro-grids. IEEE Trans Ind Inf2015;
11: 271–280.
22.
MahliaTSaktisahdanTJannifarA, et al.
A review of available methods and development on energy storage; technology update. Renew Sustain Energy Rev2014;
33: 532–545.
23.
EshraghiASalehiGHeibatiS, et al.
An enhanced operation model for energy storage system of a typical combined cool, heat and power based on demand response program: the application of mixed integer linear programming. Build Serv Eng Res Technol2019;
40: 47–74.
24.
PoullikkasA.A comparative overview of large-scale battery systems for electricity storage. Renew Sustain Energy Rev2013;
27: 778–788.
25.
GraditiGIppolitoMTelarettiE, et al.
Technical and economical assessment of distributed electrochemical storages for load shifting applications: an Italian case study. Renew Sustain Energy Rev2016;
57: 515–523.
26.
MarzbandMJavadiMPourmousaviSA, et al.
An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory. Electr Power Syst Res2018;
157: 187–199.
27.
El-ZonkolyA.Optimal scheduling of observable controlled islands in presence of energy hubs. Electr Power Syst Res2017;
142: 141–152.
28.
Warren P. Demand-side policy: Global evidence base and implementation patterns. Energy & Environment 2018; 29(5): 706–731.
29.
ChenYLuoYYangH.An optimization method for design and operation of combined cooling, heating, and power systems toward a smart grid. Sci Technol Built Environ2016;
22: 766–782.
30.
MilcarekRJAhnJZhangJ.Review and analysis of fuel cell-based, micro-cogeneration for residential applications: current state and future opportunities. Sci Technol Built Environ2017;
23: 1224–1243.
31.
ElsiedMOukaourAGualousH, et al.
Optimal economic and environment operation of micro-grid power systems. Energy Convers Manag2016;
122: 182–194.
32.
ChenH-JWangDWChenS-L.Optimization of an ice-storage air conditioning system using dynamic programming method. Appl Therm Eng2005;
25: 461–472.
33.
LeeW-SChenYTWuT-H.Optimization for ice-storage air-conditioning system using particle swarm algorithm. Appl Energy2009;
86: 1589–1595.
34.
GuoLLiuWCaiJ, et al.
A two-stage optimal planning and design method for combined cooling, heat and power microgrid system. Energy Convers Manag2013;
74: 433–445.
35.
Ghaem Sigarchian S, Malmquist A, Martin V. Optimal planning and design method for complex polygeneration systems : A case study for a residential building in Italy. Energy Conversion and Management 2017; pp.1–35. Available at: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228077
36.
HallLMBuckleyAR.A review of energy systems models in the UK: prevalent usage and categorisation. Appl Energy2016;
169: 607–628.
37.
LopesJPMoreiraCMadureiraA.Defining control strategies for microgrids islanded operation. IEEE Trans Power Syst2006;
21: 916–924.
38.
SechilariuMWangBCLocmentF.Supervision control for optimal energy cost management in DC microgrid: design and simulation. Int J Electr Power Energy Syst2014;
58: 140–149.
39.
Mesquita FGG. Design Optimization of Stand-Alone Hybrid Energy Systems: A Dissertation submitted under the scope of Mestrado Integrado em Engenharia Electrotécnica e de Computadores Major Energia. (Supervisor: Prof. Doutor Cláudio Domingos Martins Monteiro) Fevereiro de 2010.
40.
BrahmanFHonarmandMJadidS.Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system. Energy Build2015;
90: 65–75.
41.
JagdevHBrowneJJordanP.Verification and validation issues in manufacturing models. Comput Ind1995;
25: 331–353.
42.
SargentRG. Verification, validation, and accreditation: verification, validation, and accreditation of simulation models. In: Proceedings of the 32nd conference on winter simulation, 2000, pp.50–59. San Diego, CA: Society for Computer Simulation International.
43.
KleijnenJP.An overview of the design and analysis of simulation experiments for sensitivity analysis. Eur J Oper Res2005;
164: 287–300.
44.
Murray-SmithDJ.Testing and validation of computer simulation models.
Berlin:
Springer, 2015.
45.
SargentRG.Verification and validation of simulation models. J Simulat2013;
7: 12–24.
46.
BranderMSoodAWylieC, et al. Technical paper| electricity-specific emission factors for grid electricity. Ecometrica, Emissionfactors.com, 2011.
47.
EshraghiASalehiGHeibatiSLariK.An assessment of the effect of different energy storage technologies on solar power generators for different power sale scenarios: the case of iran, Sustainable. Energy, Technologies and Assessments 2019;
34: 62–67.
