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A novel methodology for optimizing thermal storage for biomass boiler heating systems is presented in this paper. Biomass boiler heating systems have gradually gained popularity in residential, commercial, and large-scale district heating plants due to the lower carbon footprint of biomass energy. One unanswered question in this field was the size of the thermal energy storage (TES) tank with respect to different boiler capacities and different types of heat demand profiles. Different standards and recommendations (based only on boiler capacities) have been proposed by various organizations around the world with large deviations on the sizing of the TES tank, but without a clear technical basis for these recommendations. These inconsistencies impede the effective design and deployment of biomass boiler heating systems. This study used a dynamic process simulation system based on field data from a 25-kW wood pellet boiler with radiant floor heating. By running the simulation for different boiler capacities (N) and different types of heat demand profiles (i.e. low, medium, high, intermittent, and variable), the optimum TES tank volume can be estimated by evaluating the TES tank discharge efficiency, boiler on-time, boiler off-time, and maximum boiler output temperature. Linear correlations between optimum TES tank volume and boiler nominal capacity for different types of heat demand profiles were obtained. The results showed that the medium building heat demand (45% N) required the smallest TES tank volume while intermittent building demand requires the largest. High building demand (60% N) generates the highest TES tank discharge efficiency (up to 99%) but also requires large storage volume. From the economic point of view, it is recommended to size the thermal energy storage system such that the average building heat demand is around 45% of boiler nominal capacity. [ABSTRACT FROM AUTHOR]

Abstract: District heating is used in many urban areas in Sweden. Almost always, the district heating utility is owned by the municipality and the municipality naturally encourages proprietors to connect their buildings to the grid, even if they cannot really force them to do so. The building owners are free to choose the best system, i.e. the cheapest one, for their need. Unfortunately, it is not always so easy to find the best solution. Mixed integer linear programming (MLIP) models might here come to help. By such computer programs it is possible to find the absolutely cheapest system of available alternatives, or even combinations among them. This paper shows how to design such a model and further how to closely depict the district heating, and electricity tariff. This is of course very important because the only interface between the proprietor of the building and the utility is found in this bureaucratic instrument. If the tariff is too high the building owners will choose other heating systems than district heating, or even worse, combining district heating with alternative base load sources. In Sweden, this has been of interest because ground-water coupled heat pumps can be profitable, operated by use of the relatively low electricity prices. In this paper we show that dual-fuel, and sometimes even triple-fuel systems, are of interest when the proprietor aims at minimising the cost for space and domestic hot-water heating. [Copyright &y& Elsevier]

This paper presents a practical method to optimize in-building section of centralized Heating, Ventilation and Air-conditioning (HVAC) systems which consist of indoor air loops and chilled water loops. First, through component characteristic analysis, mathematical models associated with cooling loads and energy consumption for heat exchangers and energy consuming devices are established. By considering variation of cooling load of each end user, adaptive neuro-fuzzy inference system (ANFIS) is employed to model duct and pipe networks and obtain optimal differential pressure (DP) set points based on limited sensor information. A mix-integer nonlinear constraint optimization of system energy is formulated and solved by a modified genetic algorithm. The main feature of our paper is a systematic approach in optimizing the overall system energy consumption rather than that of individual component. A simulation study for a typical centralized HVAC system is provided to compare the proposed optimization method with traditional ones. The results show that the proposed method indeed improves the system performance significantly. [Copyright &y& Elsevier]

Phase Change Materials (PCM) technology can be used for thermal storage and peak load shaving. This paper presents the optimization study of a PCM-Air heat exchanger as well as the development of load shifting solutions which take into account the thermal comfort of the occupants and the indoor air quality. The exchanger contains macroencapsumated PCM (paraffin) and is conceived in a way that facilitates its integration in a ventilation system. It is aimed to shift space heating electrical consumption from peak to off-peak period. The study is performed using an experimentally validated numerical model based on the heat balance approach and the apparent heat capacity method. Several parameters were investigated during the optimization processes, including heat exchanger dimensions, PCM quantity and properties, seeking the configuration with the optimal compromise between stored heat and the time needed for the charging/discharging process. The numerical model was coupled to a building simulation model with several control strategies investigating different scenarios over a one- month winter period. The scenarios vary with increasing complexity, first considering load shifting and thermal comfort, then adding the final price of electricity consumption and finally taking into account the indoor air quality. [ABSTRACT FROM AUTHOR]

Phase change materials can be integrated in building walls to reduce heating energy consumption. However, proper design of the composite material requires a lot of complex calculations. In this paper, we present a methodology based on metamodeling in order to properly design phase change material composite solutions. A low-energy house example is chosen to develop our methodology, simulations being carried with TRNSYS. Box–Behnken design of experiments is used for data sampling in order to be able to produce a polynomial metamodel. First, the metamodel allows to have a profound understanding of the parameters influence: melting temperature, thickness, latent heat, density and thermal conductivity. Second, it is possible to extract from the polynomial form the optimal material properties. Third, the metamodel can be used to design or test different material solutions. Based on this methodology, it is possible to build a simple tool that can be used by architects and engineers. [ABSTRACT FROM AUTHOR]

For military and disaster relief housing, rapidly deployable shelters must be lightweight, be packaged in a small volume for transportability, and be erected without heavy lifting equipment. A critical design criterion is also energy efficiency in heating and cooling. To meet these priorities, the research team has utilized origami as inspiration for a thermally insulated rigid wall deployable shelter that can be erected manually through counterweighting. To enhance energy efficiency, improvements in the shape of a structure (i.e., member lengths and angles) at a design stage can lead to savings throughout its lifecycle. This is magnified in the context of mass-production of deployable shelters, where any improvements are multiplied. Structural efficiency is also critical to achieve lightweight design. This paper presents a multi-objective shape optimization methodology which balances the priorities of structural performance (i.e., minimum deflections) and energy efficiency (i.e., minimum thermal energy load). This is demonstrated for the case study of a deployable shelter. Design variables include geometric parameters. Constraints relate to the package size and capability of interfacing with existing technologies. Structural analysis is performed using a parametric finite element program. Thermal energy is calculated using EnergyPlus. An optimized solution is found and compared against existing military solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for an optimal control of the heating system at the building scale. This control is a new approach of energy planning that aims to decrease the heating consumption/expenses over a defined prediction horizon while respecting the occupants’ thermal comfort. It employs a simplified building thermal model to simulate the building thermal behavior taking into consideration the weather predictions. This approach is based on the application of Monte Carlo method, i.e., a random generator for the heating system scenarios. The aim is to determine the optimal heating plan for the prediction horizon that fulfills the constraints regarding the thermal comfort of occupants and the minimization of the energy consumption/expenses together with achieving a load shedding. [ABSTRACT FROM AUTHOR]

Abstract: Trigeneration systems, also known as Combined Heat, Cooling and Power (CHCP) systems, are interesting alternatives to supply different energy services in urban districts and in large buildings, particularly in warm areas such as Mediterranean countries. These systems can provide substantial benefits from economic, energetic, and environmental viewpoints, since the cogenerated heat can be used for heating in winter as well as cooling in summer with an absorption refrigerator. This paper develops an optimization model using Mixed Integer Linear Programming (MILP) to determine the type, number and capacity of equipment in CHCP systems installed in the tertiary sector as well as to establish the optimal operation mode for the different plant components on an hour-by-hour basis throughout the year. The objective function to be minimized is the annual total cost. The optimization model considers the legal constraints imposed to feed the surplus autogenerated electricity into the grid at a regulated feed-in tariff. The optimization model is applied to design a system providing energy services for a hospital located in the city of Zaragoza (Spain). The effects of the financial market conditions and energy prices in the optimal structure of the system are analyzed. [Copyright &y& Elsevier]