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Energy, Economic and Environmental Performance Simulation of a Hybrid Renewable Microgeneration System with Neural Network Predictive Control

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Energy, Economic and Environmental Performance Simulation of a Hybrid Renewable Microgeneration System with Neural Network Predictive Control Alexandria Engineering Journal (2016) xxx, xxx–xxx HOSTED BY Alexandria University Alexandria Engineering Journal www.elsevier.com/locate/aej www.sciencedirect.com ORIGINAL ARTICLE Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control Evgueniy Entchev a, Libing Yang a, Mohamed Ghorab a, Antonio Rosato b,*, Sergio Sibilio b a Natural Resources Canada, CanmetENERGY, 1 Haanel Drive, Ottawa, ON K1A 1M1, Canada b Second University of Naples, Department of Architecture and Industrial Design ‘‘Luigi Vanvitelli”, via San Lorenzo, 81031 Aversa, CE, Italy Received 12 July 2016; revised 13 August 2016; accepted 4 September 2016 KEYWORDS Abstract The use of artificial neural networks (ANNs) in various applications has grown signifi- Hybrid microgeneration cantly over the years. This paper compares an ANN based approach with a conventional on-off system; control applied to the operation of a ground source heat pump/photovoltaic thermal system serving Ground source heat pump; a single house located in Ottawa (Canada) for heating and cooling purposes. The hybrid renewable Photovoltaic thermal; microgeneration system was investigated using the dynamic simulation software TRNSYS. A con- Artificial neural network; troller for predicting the future room temperature was developed in the MATLAB environment and Predictive control; six ANN control logics were analyzed. Energy saving The comparison was performed in terms of ability to maintain the desired indoor comfort levels, primary energy consumption, operating costs and carbon dioxide equivalent emissions during a week of the heating period and a week of the cooling period. The results showed that the ANN approach is potentially able to alleviate the intensity of thermal discomfort associated with over- heating/overcooling phenomena, but it could cause an increase in unmet comfort hours. The anal- ysis also highlighted that the ANNs based strategies could reduce the primary energy consumption (up to around 36%), the operating costs (up to around 81%) as well as the carbon dioxide equiv- alent emissions (up to around 36%). Ó 2016 Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 1. Introduction The depletion of resources as well as an environmental con- science regarding global warming have urged the need for a * Corresponding author. Fax: +39 081 5010845. complete change in energy production, supply and consump- E-mail address: [email protected] (A. Rosato). tion patterns in order to reduce the energy demand as well Peer review under responsibility of Faculty of Engineering, Alexandria as to improve the efficiency of energy production systems University. http://dx.doi.org/10.1016/j.aej.2016.09.001 1110-0168 Ó 2016 Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: E. Entchev et al., Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control, Alexandria Eng. J. (2016), http://dx.doi.org/10.1016/j.aej.2016.09.001 2 E. Entchev et al. Nomenclature Latin letters UH time percentage under ‘‘underheating” conditions AC alternating current (%) ANN artificial neural network V 3-way valve CAD Canadian dollar CCHT Canadian centre for housing technology Greeks CO operational cost (CAD) D difference CO2 carbon dioxide COP coefficient of performance Superscripts CWT cold water tank ANN operation with artificial neural network based con- DC direct current trol strategy E energy (kWh) on-off operation with on-on-off control strategy EER energy efficiency ratio FC fuel cell Subscripts GHX ground heat exchanger CWT cold water tank GSHP ground source heat pump HWT hot water tank HVAC heating ventilation and air-conditioning in inlet HWT hot water tank max maximum MCHP micro combined heat and power mean average ° 2 MSE mean squared error ( C ) OC overcooling ” OC time percentage under ‘‘overcooling conditions OH overheating (%) out outlet ” OH time percentage under ‘‘overheating conditions p primary (%) pred predicted by the ANN based strategies ORC organic Rankine cycles PMV predicted mean vote PEMFC proton-exchange membrane fuel cell PVT photovoltaic thermal PED primary energy difference room associated with the indoor air PID proportional-integral-derivative SPT solar preheat tank PMV predicted mean vote TRNSYS obtained with the software TRNSYS17 PVT photovoltaic thermal UC undercooling 2 R coefficient of determination (-) UH underheating ° RMSE root mean square error ( C) (+3 h) 3 h later SOFC solid oxide fuel cells (+4 h) 4 h later SPT solar preheat tank (+5 h) 5 h later ° T temperature ( C) (+6 h) 6 h later TOU time of use UC time percentage under ‘‘undercooling” conditions (%) mainly in the residential sector (it has been estimated that of the PVT system result in a higher overall solar conversion buildings consume 40% of the world’s energy and generate rate than that of solely photovoltaic or solar collectors, and 33% of the carbon dioxide emissions [1]). thus enable a more effective use of solar energy. In addition Micro-cogeneration (MCHP) systems able to produce both to the micro-cogeneration technologies, several studies have heat and power at the point of use (with an electric output recognized that the ground source heat pump (GSHP) is an lower than 50 kWel according to [2]) are emerging as a suitable efficient and environment-friendly option for space heating/- approach to reduce energy consumption and pollutant emis- cooling of buildings [14–17] and a large number of GSHPs sions by offering high efficiency and good environmental foot- have been used in residential and commercial buildings print, offsetting the need for centrally-generated grid throughout the world [17] mainly thanks to the advantage of electricity, enhancing energy security and avoiding transmis- using the ground or groundwater as the heat source-sink with sion/distribution losses [3–6]. Today there are several technolo- respect to other thermal sources such as the outside air. There gies that are capable of providing cogeneration services [7,8] are several types of ground loop systems used in GSHPs, but and in the recent years numerous studies have been performed ground-coupled heat pump systems with vertical boreholes on development, design guidelines, experimental testing, are considered as the most suitable option for dwellings (where energy, cost and emission analyses, and optimization of limited space is available) [16,17]. Combination of MCHP MCHP systems [9,10]. Among them, the solar energy conver- devices to various thermally fed or electrically-driven cooling sion into electricity and heat in a single device called photo- units (such as GSHP systems) allows to set up a so-called voltaic thermal (PVT) collector is gaining an increasing micro combined cooling heat and power (MCCHP) system attention [11–13]. This is due to the fact that the dual functions [18,19], that represents the production in situ of a threefold Please cite this article in press as: E. Entchev et al., Energy, economic and environmental performance simulation of a hybrid renewable microgeneration system with neural network predictive control, Alexandria Eng. J. (2016), http://dx.doi.org/10.1016/j.aej.2016.09.001 A hybrid renewable microgeneration system 3 energy vector requested by the user from a unique source of as (b) conventional on-off controllers are used to manage the fuel. Although MCHP and GSHP are exciting technologies, systems at any case. they face several challenges, in gaining market share in mature It is well known that the control of microgeneration sys- and competitive markets, in further improving device’s effi- tems is very important for their optimal operation and plays ciency and reducing cost, in increasing the operational lifetime a key role in achieving better energy and economic perfor- to recover the initial investment, and also in understanding the mance under dynamic operating conditions. A simple on-off systems by both installers and potential end users. control strategy is still often used, but conventional controllers Some authors have studied hybrid micro-cogeneration sys- cannot deal with nonlinear phenomena, uncertainties of elec- tems with the integration of geothermal or renewable sources. trical and thermal demands, and time delays, and consequently Gusdorf [20] reported experimental results of a residential tri- they may reduce the energy efficiency and cause poor thermal generation system using internal combustion engine and comfort [35]. In fact, since building dynamic is a slow process, ground source heat pump. Guo et al. [21] analyzed a novel subject to the passively changing ambient environment, the air- cogeneration system driven by low-temperature geothermal conditioning systems may respond to an indoor temperature sources. Ribberink et al. [22] compared MCHP systems with change with a significant time delay using the on-off control a Stirling engine and solar collectors. Obara et al. [23] exam- method since the conventional controllers cannot forecast nei- ined a completely energy-independent micro-grid consisting ther the evolution of the weather conditions nor the reaction of of photovoltaic, water electrolyzers,
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