Computationally Efficient Modeling Approach for Evaporator Performance under Frost Conditions

Growth of a frost layer on an evaporator surface due to low evaporator temperature as well as moisture contained in surrounding air deteriorates performance of a refrigeration system significantly and requires significant energy for defrost. Many studies have been performed to model the heat and mass transfer phenomena in an attempt to have insight and accurate prediction. However, many models form nonlinear algebraic differential equations and thereby it is computationally demanding to include them into a typical building energy simulation environment for a cooler or freezer consisting of an enclosure, refrigeration equipment, defrost elements, and controls. Computationally efficient but reasonably accurate models are needed in order to evaluate overall system performance. The objective of this paper is to introduce a modeling approach to overcome the problem. A numerical solution strategy based on an enthalpy-based reformulation and linearization method will be presented. Comparisons of a proposed and detailed model results are provided

Econometric and Environmental Optimization of Combined Cooling, Heating and Power Plant Operation

Combined Cooling, Heat and Power (CCHP) systems have great potential to recover low-grade thermal energy, resulting in higher energy efficiency, reduced emission rates, lower operating costs and a higher level of energy security. In order to fully realize the benefits of CCHP systems in terms of reduced cost and carbon dioxide emissions, effective optimization and control strategies are required. This work presents an approach for optimizing the operation of the CCHP system using a detailed network energy flow model solved by genetic algorithm. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The performance of the system will be compared and evaluated with respect to economic and environmental benefits

Vapor compression cycle enhancements for cold climate heat pumps

In very low ambient temperature regions, both heating capacity and coefficient of performance (COP) of traditional air-source vapor compression heat pumps drop significantly with a decrease in outdoor air temperature. The current study investigates two alternative technologies for improving the efficiency of vapor compression cycles especially at very low ambient temperatures. The first method is liquid flooded compression with regeneration in which a significant amount of non-volatile liquid is mixed into the stream of refrigerant vapor entering the compressor in order to approach an isothermal compression process. The second method is multi-port refrigerant injection during compression and economizing which is similar to multi-stage compression in providing refrigerant cooling between compressor stages and a lower refrigerant quality entering the evaporator. Both technology improvements address the thermodynamic inefficiencies that are inherent within the simple vapor compression cycle that is employed in most heat pumping equipment. The benefits of oil flooding and saturated vapor injection are evaluated using experimental testing of an oil flooded R410A scroll compressor and a 2 port saturated vapor injection R410A scroll compressor and simulation. The results show that the performance of the compressor increases with oil flooding and saturated vapor injection especially at very low evaporation temperatures. The experimental compressor results were used for system analysis of liquid flooded compression with regeneration and saturated vapor injection. At -10°C ambient temperature, estimated COP (heating) of the flooded compression with regeneration cycle was 25% higher while that of the vapor injection cycle was 12.5% higher than the standard vapor compression cycle. For Minneapolis, the seasonal efficiency (heating) of a flooded compression with regeneration cycle was estimated to be 34% higher while that of the vapor injection cycle was 21% higher than the standard vapor compression cycle. Both technologies lead to higher energy efficiency and less degradation in heating capacity at low ambient temperatures. Also, a previously developed detailed flooded scroll compressor model has been validated with the oil flooding experimental data, for which good agreement was found

A Hybrid Network Flow Algorithm for the Optimal Control of Large-Scale Distributed Energy Systems

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for largescale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components. This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixedinteger linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization

Optimization Of Electricity Make/Buy Decisions For Purdue’s Wade Power Plant

The Wade power plant at Purdue University produces chilled water, steam and electricity using CCHP (Combined Cooling, Heating and Power) systems to meet the campus cooling, heating and electricity demands. Steam generated from utility boilers is not only distributed through a steam tunnel system for campus heating but also used for power generation, chilled water production and in-plant auxiliary usage. Chilled water is generated using both steam driven chillers and electric chillers and is delivered through a closed water circulation loop to campus to meet the time-varying cooling demand. The electricity generated using two steam turbine driven generators provide 30-50% of the electricity required to meet campus needs. The remainder of the electricity is purchased from the local electric utility and includes a real-time pricing component that varies with time. Plant primary energy use and costs depend upon decisions regarding generation and purchasing of electricity in response to time varying factors so as to keep the operating cost minimum and meet campus electricity, heating, and cooling demands subject to time-varying prices, loads, and environmental conditions. This paper presents an approach for optimizing the operation of the CCHP system using a multimodal genetic algorithm based on hourly load forecasts and fuel pricing with a joint characteristic for the energy components to minimize the total operating cost of the plant. The tool is used to evaluate the benefits of optimal control for the Purdue CCHP plant as a function of different (possibly future) utility rate incentives