In this paper, a mathematical model by which the thermal and physical behavior of a solar dish/Stirling system was investigated, then the system was designed, analysed and optimized. In this regard, all of heat losses in a dish/Stirling system were calculated, then, the output net-work of the Stirling engine was computed, and accordingly, the system efficiency was worked out. These heat losses include convection and conduction heat losses, radiation heat losses by emission in the cavity receiver, reflection heat losses of solar energy in the parabolic dish, internal and external conduction heat losses, energy dissipation by pressure drops, and energy losses by shuttle effect in displacer piston in the Stirling engine. All of these heat losses in the parabolic dish, cavity receiver and Stirling engine were calculated using mathematical modeling in MatlabTM software. For validation of the proposed model, a 10 kW solar dish/Stirling system was designed and the simulation results were compared with the Eurodish system data with a reasonable degree of agreement. This model is used to investigate the effect of geometric and thermodynamic parameters including the aperture diameter of the parabolic dish and the cavity receiver, and the pressure of the compression space of the Stirling engine, on the system performance. By using the PSO method, which is an intelligent optimization technique, the total design was optimized and the optimal values of decision-making parameters were determined. The optimization has been done in two scenarios. In the first scenario, the optimal value of each designed parameter has been changed when the other parameters are equal to the designed case study parameters. In the second scenario, all of parameters were assumed in their optimal values. By optimization of the modeled dish/Stirling system, the total efficiency of the system improved to 0.60% in the first scenario and it increased from 21.69% to 22.62% in the second scenario of the optimization, while the system variables changed slightly. Article History: Received Sept 28, 2015; Received in revised form January 08, 2016; Accepted February 15, 2016; Available online How to Cite This Article: Nazemi, S. D. and Boroushaki, M. (2016) Design, Analysis and Optimization of a Solar Dish/Stirling System. Int. Journal of Renewable Energy Development, 5(1), 33-42. http://dx.doi.org/10.14710/ijred.5.1.33-42
The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Consequently, a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration is proposed. In the first layer, DSM is employed to minimize the electricity demand (e.g. heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Rutgers University, Laboratory for Energy Smart Systems) team. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in total annual energy consumption and annual cost of microgrid.
The impact of load growth on electricity peak demand is becoming a vital concern for utilities. To prevent the need to build new power plants or upgrade transmission lines, power companies are trying to design new demand response programs. These programs can reduce the peak demand and be beneficial for both energy consumers and suppliers. One of the most popular demand response programs is the building load scheduling for energy-saving and peak-shaving. This paper presents an autonomous incentive-based multi-objective nonlinear optimization approach for load scheduling problems (LSP) in smart building communities. This model’s objectives are three-fold: minimizing total electricity costs, maximizing assigned incentives for each customer, and minimizing inconvenience level. In this model, two groups of assets are considered: time-shiftable assets, including electronic appliances and plug-in electric vehicle (PEV) charging facilities, and thermal assets such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters. For each group, specific energy consumption and inconvenience level models were developed. The designed model assigned the incentives to the participants based on their willingness to reschedule their assets. The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. Zoutendijk’s method is used to solve the nonlinear optimization model. This formulation helps capture the building collaboration to achieve the objectives. Illustrative case studies are demonstrated to assess the proposed model’s effect on building communities consisting of residential and commercial buildings. The results show the efficiency of the proposed model in reducing the total energy cost as well as increasing the participants’ satisfaction. The findings also reveal that we can shave the peak demand by 53% and have a smooth aggregate load profile in a large-scale building community containing 500 residential and commercial buildings.
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