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The hydraulic balance of heating network is considered as a pre-condition for the implementation of low temperature district heating (LTDH). Its imbalance result into high energy consumption and heat-losses in the network. In this study, a novel hydraulic model is presented which investigates hydraulic imbalance in the LTDH network, using real weather and hourly monitored operational heating data from an existing boiler based building. Analysis of delta t in space-heating system shows that the delta t is maximum when the outside air temperature is lowest and it decreases with increase in outside air temperature. Furthermore, the hydraulic imbalance is analysed for four different control scenarios with the aim to find an optimum scenario with minimum pumping power, energy consumption and heat-losses in the LTDH network. Results show that the hydraulic imbalance is due to the absence of flow-limiters and balancing valves on the return pipe and thermostatic radiator valves (TRVs) alone are unable to maintain hydraulic balance in the space-heating system of buildings. Moreover, the control scenario with variable flow-rate and fixed supply water temperature from the sub-station is found to be optimum. Compared to the constant flow-rate scenario, the pumping power, energy consumption and heat-losses in the LTDH network are reduced by approximately 2%, 63% and 14%, respectively.

The increasing depletion of natural resources, combined with a wider set of pressures on the environment, has, in recent years, highlighted the need for a more efficient use of energy and a development process that involves alternative energy sources. Energy efficiency has received much attention as a solution, implying both monetary and emissions savings. However, the latter may be partially offset by the income and demand effects of the former, both in more efficient sectors and in spreading to the wider economy. This is the problem of rebound effects. Taking Spain as a case study, and introducing an energy-related CGE model that develops the inclusion of renewables, this paper evaluates a combination of efficiency initiatives to deliver both reduced energy use by households and a more sustainable supply of energy. Our findings suggest that a package aimed at improving efficiency in household electricity and petroleum use, combined with a more competitive supply of energy from renewable sources, may be the only way to get reductions in all energy use, and thus benefit the economy. Specifically, we consider how this package may lead to positive economic impacts and associated rebound effects, where the latter are focused on a greener energy supply.

AbstractThe split cycle engine is a new reciprocating internal combustion engine with a potential of a radical efficiency improvement. In this engine, the compression and combustion–expansion processes occur in different cylinders. In the compression cylinder, the charge air is compressed through a quasi-isothermal process by direct cooling of the air. The high pressure air is then heated in a recuperator using the waste heat of exhaust gas before induction to the combustion cylinder. The combustion process occurs during the expansion stroke, in a quasi-isobaric process. In this paper, a fundamental theoretical cycle analysis and one-dimensional engine simulation of the split cycle engine was undertaken. The results show that the thermal efficiency (η) is mainly decided by the CR (compression ratio) and ER (expansion ratio), the regeneration effectiveness (σ), and the temperature rising ratio (N). Based on the above analysis, a system optimization of the engine was conducted. The results showed that by increasing CR from 23 to 25, the combustion and recuperation processes could be improved. By increasing the expansion ratio to 26, the heat losses during the gas exchange stroke were further reduced. Furthermore, the coolant temperatures of the compression and expansion chambers can be controlled separately to reduce the wall heat transfer losses. Compared to a conventional engine, a 21% total efficiency improvement was achieved when the split cycle was applied. It was concluded that through the system optimization, a total thermal efficiency of 53% can be achieved on split cycle engine.

Abstract Effect of inclination angle to the thermal performance of a heat pipe photovoltaic/thermal system (HP-PV/T) system was rarely reported. In the present study, a HP-PV/T system was firstly constructed in an Enthalpy Difference Laboratory, where inclination angle was experimentally managed as the only variable. Meanwhile, a comprehensive numerical model for the HP-PV/T system was developed to validate the experiments. Particularly, a 3D model for the inclined heat pipe was also firstly involved. The simulation results show that liquid film thickness within the condenser or the evaporator stabilizes at a constant value at inclining condition. The relative filmwise thermal resistance of the condenser decreases first and then increases with inclination angle; while the evaporator shows an opposite trend to the condenser. The overall thermal resistance of solar heat pipe is mainly determined by the evaporator while the evaporator is mainly determined by the effective height of the liquid pool. The experimental and simulation results both indicate that the optimum inclination angle is 40°. The proposed model agrees well with the experimental results at big inclination angles (≥20°), it is practicable to reveal the influence of inclination angle to the thermal performance of a HP-PV/T system.

Abstract Increasing electrification of final uses can be a viable solution towards low-carbon energy systems, when coupled with local renewable power generation. Mountain areas can already benefit from high shares of hydro-power generation, but, at the same time, rely on oil products for transport and for the heating sector in remote areas where natural gas infrastructures are not available. This research work evaluates potential scenarios for the electrification of transport and heating sectors, by coupling the simulation tool EnergyPLAN with a multi-objective optimization algorithm to analyse economic and environmental aspects. Results show that the largest benefits are expected from the electrification of the heating sector. Indeed, a CO2 emissions reduction up to 30% can be reached by acting on the transport sector alone, while up to 65% combining it with measures on heating, industry and agriculture sectors and additional electricity generation from photovoltaic systems. Moreover, the use of heat pumps can lead to significant CO2 emissions decrease with only to a slight increase in the overall annual costs thanks to lower variable costs that partly compensate the higher required initial investment and electricity storage deployment. The optimization analyses also highlight the effect of progressive penetration of electric vehicles in the private cars fleet and hydrogen trucks in the light-duty vehicles one.

Abstract In energy demand forecasting, the objective function is often symmetric, implying that over-prediction errors and under-prediction errors have the same consequences. In practice, these two types of errors generally incur very different costs. To accommodate this, we propose a machine learning algorithm with a cost-oriented asymmetric loss function in the training procedure. Specifically, we develop a new support vector regression incorporating a linear-linear cost function and the insensitivity parameter for sufficient fitting. The electric load data from the state of New South Wales in Australia is used to show the superiority of our proposed framework. Compared with the basic support vector regression, our new asymmetric support vector regression framework for multi-step load forecasting results in a daily economic cost reduction ranging from 42.19 % to 57.39 % , depending on the actual cost ratio of the two types of errors.

Abstract Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1–24 h) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24 h ahead.