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With the increasing integration of decentral renewable energy systems in the residential sector, the opportunity to enhance the control via model predictive control is available. In this article, the main focus is to investigate the objective function of the model predictive controller (MPC) of an integrated thermal-electrical renewable energy system consisting of photovoltaics, solar thermal collectors, fuel cell along with auxiliary gas boiler and electricity grid using electrical and thermal storage in a single-family house. The mathematical definition of the objective function and the depth of detailing the objectives are the prime focus of this particular article. Four different objective functions are defined and are investigated on a day-to-day basis in the selected six representative days of the whole year for the single-family house in Ehingen, Germany with a white-box simulation model simulated using TRNSYS and MATLAB. Using the clustering technique then the six representative days are weighted extrapolated to a whole year and the outcomes of the whole year MPC implementation are estimated. The results show that the detailing of the mathematical model, even though is time and personnel consuming, does have its advantages. With the detailed objective function, 9% more solar thermal fraction; 32% less power-to-heat at an expense of 32% more gas boiler usage; 6% more thermal system effectiveness along with 10% increased total self-consumption fraction with 16% decrease in space heating demand, 492 kWh more battery usage and 66% reduced fuel cell production is achieved by the MPC in comparison to the status quo controller. Except for the effectiveness of the thermal system with increased gas boiler usage, which occurs due to less power-to-heat, the detailed objective function in comparison to the simple mathematical definition does evidently increase the smartness of the MPC.

Space cooling and heating always tends to be a major part of the primary energy usage. By using fossil fuel electricity for these purposes, the situation becomes even worse. One of the major electricity consumptions in India is air conditioning. There are a lot of different technologies and few researchers have come up with a debate between solar absorption cooling and PV electric cooling. In a previous paper, PV electric cooling was studied and now as a continuation, this paper focuses on solar thermal absorption cooling systems and their application in commercial/office buildings in India. A typical Indian commercial building is taken for the simulation in TRNSYS. Through this simulation, the feasibility and operational strategy of the system is analysed, after which parametric study and economic analysis of the system is done. When compared with the expenses for a traditional air conditioner unit, this solar absorption cooling will take 13.6 years to pay back and will take 15.5 years to payback the price of itself and there after all the extra money are savings or profit.  Although the place chosen for this study is one of the typical tropical place in India, this payback might vary with different places, climate and the cooling demand.Article History: Received May 12th 2017; Received in revised form August 15th 2017; Accepted 1st Sept 2017; Available onlineHow to Cite This Article: Narayanan, M. (2017). Techno-Economic Analysis of Solar Absorption Cooling for Commercial Buildings in India.  International Journal of Renewable Energy Development, 6(3), 253-262.https://doi.org/10.14710/ijred.6.3.253-262

An integrated electrical and thermal residential renewable energy system consisting of solar thermal collectors, gas boiler, fuel cell combined heat and power, a photovoltaic system with battery, inverter, and thermal storage for a single-family house of Sonnenhaus standard is investigated with a model predictive controller (MPC). The main focus of this article is to define a multi-objective mathematical function, develop a coupled simulation framework for the nonlinear time-varying deterministic discrete-time problem of the energy system using TRNSYS and MATLAB. With the developed methodology, a sensitivity analysis of maximum optimization time, swarm (or population or mesh) size of a typical spring day and a typical summer day assuming a 100% accurate weather and load forecast with three different algorithms: particle swarm optimization (PSO), genetic algorithm (GA) and global pattern search (GPS) are analyzed. Finally, the obtained results are compared with a status quo controller. Results show that the PSO algorithm optimizer performs the best in this MPC for such a complex and time-consuming MPC model in both the spring day and the summer day. The obtained results show that the PSO with swarm size 50 in the selected typical spring day and the PSO with swarm size 40 in the selected summer day reduces the objective function’s fitness value from 413 to −177 within 6 h optimization time and from 1396 to 1090 in 4 h optimization time respectively.

Space cooling and heating always tends to be a major part of the primary energy usage. By using fossil fuel electricity for these purposes, the situation becomes even worse. One of the major electricity consumptions in India is air conditioning. There are a lot of different technologies and few researchers have come up with a debate between solar absorption cooling and PV electric cooling. In a previous paper, PV electric cooling was studied and now as a continuation, this paper focuses on solar thermal absorption cooling systems and their application in commercial/office buildings in India. A typical Indian commercial building is taken for the simulation in TRNSYS. Through this simulation, the feasibility and operational strategy of the system is analysed, after which parametric study and economic analysis of the system is done. When compared with the expenses for a traditional air conditioner unit, this solar absorption cooling will take 13.6 years to pay back and will take 15.5 years to payback the price of itself and there after all the extra money are savings or profit.  Although the place chosen for this study is one of the typical tropical place in India, this payback might vary with different places, climate and the cooling demand.Article History: Received May 12th 2017; Received in revised form August 15th 2017; Accepted 1st Sept 2017; Available onlineHow to Cite This Article: Narayanan, M. (2017). Techno-Economic Analysis of Solar Absorption Cooling for Commercial Buildings in India.  International Journal of Renewable Energy Development, 6(3), 253-262.https://doi.org/10.14710/ijred.6.3.253-262

Buildings play an important role in the energy consumption of a household. There are different types of buildings and different standards, which are for each of them. Hence, the decentralized energy system has different configurations for each building standards and buildings built up according to each standards and have necessity to be controlled in a different approach. Using a case study of four different standards—Sonnenhaus, KfW55, Passive house and WSchVO95 of single family houses (SFH) of same geometry and boundary conditions the control constraints are showcased. The houses are selected such that high renewable energy self-production, low energy demand house, low net energy house and an old 1995 constructed house are compared. The differences in the system design, their control strategy and how it affects the system sizing or renewable fraction is explained in this paper. The same SFH according to different standards is simulated with TRNSYS and the energy system (including solar thermal collectors, PV, gas boiler, fuel cell CHP, thermal storage and electrical storage) for each house is optimized and compared. Thus, the paper showcases the importance of the building, not only geometry but also building physics and energy efficiency. Finally, the necessity for intelligent control system for a complicated building system with multiple energy source is justified and the requirements of such control systems are enlisted.

Residential buildings play an important role in energy consumption in the world. Around 40% of the world’s end energy is being used in residential buildings. The energy demand of just single and two-family houses in Germany equates to 10% of the whole of Germany’s emission. Increasing renewable energy utilization in residential buildings, especially for heating, is essential, which would apparently result in reduced primary energy consumption. Model predictive control (MPC) is one of the intelligent control techniques which uses a dynamic model to forecast system behaviour and optimize the forecasted outcomes such that the desired behaviour is achieved by optimal control of the system by manipulating the control variables. The role of intelligent controllers in building heating or in renewable energy systems has already been identified. However, what still remains open is the combination of energy systems, storage, and building all together into a single system controller and the universalization of this controller. In most of the literature, the MPC is customized and designed especially for that particular case. The obligation to develop a controller hierarchy or controller management where all controllers will communicate with each other and work optimally using MPC via energy and demand forecast and learn from the system such that it can be universally adaptive is present. The aim is to investigate MPC in a decentrally integrated thermal and electrical energy system for increasing renewable energy fraction and self-consumption with different optimization algorithms for different single-family house energy standards. The selected building standards are Sonnenhaus (high renewable fraction house), KfW55 (modern building), Passive house (low primary energy house), and WSchVO95 (old building characteristics). The energy system consists of photovoltaics (PV), inverter, charge controller, battery storage and grid backup on the electricity side along with the solid oxide fuel cell combined heat and power (FC-CHP). In addition, on the thermal side, the system includes solar thermal collectors, gas boiler, thermal storage, radiator/floor heating, and freshwater station. A constrained time-varying deterministic discrete-time model is used to represent a Building-HVAC problem here. A simulation framework for MPC simulation using the whitebox model of TRNSYS and MPC simulation in MATLAB is developed. To carry out optimization in MATLAB, a direct optimization, Generalized Pattern Search (GPS), and two widely used population-based stochastic meta-heuristic methods, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are chosen. An extrapolated annual evaluation of the MPC is carried out via six representative days chosen using the clustering technique.