• Energy Research
• US close
• IR close
• Energy close

Load forecasting has long been recognized as an important building block for all utility operational planning efforts. Over the recent years, it has become ever more challenging to make accurate forecasts due to the proliferation of distributed energy resources, despite the abundance of existing load forecasting methods. In this paper, we identify one drawback suffered by most load forecasting methods: neglect to thoroughly address the impact of input errors on load forecasts. As a potential solution, we propose to incorporate input modeling and uncertainty quantification to improve load forecasting performance via a two-stage approach. The proposed two-stage approach has the following merits. (1) It provides input modeling and quantifies the impact of input errors, rather than neglecting or mitigating the impact, a prevalent practice of existing methods. (2) It propagates the impact of input errors into the ultimate point and interval predictions for the target customer's load to improve predictive performance. (3) A variance-based global sensitivity analysis method is further proposed for input-space dimensionality reduction in both stages to enhance the computational efficiency. Numerical experiments show that the proposed two-stage approach outperforms competing load forecasting methods in terms of both point predictive accuracy and coverage ability of the predictive intervals. 9 pages, 4 figures, journal

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise. ; © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

In this paper, we have proposed a gas turbine combined cycle with the integration of low-temperature thermal energy and methanol decomposition, and also investigated a principle of the cascade utilization of chemical exergy of fuel. Here, the combustion of methanol fuel is divided up into two steps: the methanol is decomposed into the syngas with hydrogen and carbon monoxide through recovering the low-temperature thermal energy from an intercooler of a gas turbine, and then the syngas is combusted with air, namely, the indirect combustion of methanol. As a result, the exergy destruction in the combustion of syngas is expected to be decreased by 7.5 percentage points of the input energy of cycle; at the same time, the low-temperature thermal energy is upgraded to the chemical energy of fuel, and the thermal efficiency of this gas turbine cycle is expected to be about 6 percent points higher than that of a conventionally combined cycle with intercooling at the turbine inlet temperature of 1300 °C and at a given overall pressure ratio of 15. The promising results obtained here indicated that this gas turbine combined cycle could simultaneously accomplish the decrease of exergy destruction in combustion and the upgrade of low-temperature thermal energy levels, leading to the effective utilization of clean syngas fuel and the recovery of low-temperature thermal energy in power system.

Abstract In this paper, the concept of well array operating enhanced geothermal system (EGS) is proposed in response to the existing challenges in EGS commercialization. A well array operating EGS is characterized by well sharing, which substantially reduces drilling cost and maximizes mass flow rate. Following a thorough discussion of conditions for representing an EGS by a 2D model, the life time performance of a well array operating EGS is investigated based on 2D finite element modeling. The accuracy of the presented numerical results is quantified by systematic tests of mesh, element order and time step dependence, and by direct comparison with an analytical solution for 2D porous flow, which is derived in the paper as well. This paper demonstrates that 2D modeling is not only capable of accurately solving a wide range of EGS problems at low computational cost, but also a powerful tool for quantitatively estimating 3D numerical modeling errors, which are usually not adequately addressed in previous studies. The modeling results of the well array operating EGS presented in this paper may serve as a benchmark for testing future EGS models.

Abstract Average energy consumption per U.S. household has fallen by just under 20% in the last ten years. Much of this drop occurred after 1979, when gas and electricity prices as well as oil prices rose in real terms. The response of households to higher prices has involved physical modifications on and in the home and changes in behavior. Many actions have been taken by households, but the most important single factor has been a significant reduction in indoor temperatures. The greater energy efficiency of new homes and appliances has also helped to depress residential energy demand, although improvements have levelled off in the last few years. There are signs that the momentum of energy conservation is less now than it was 2 years ago, but it appears that energy prices will be high enough to discourage households from returning to former energy-using practices. Along with the continued replacement of homes and appliances with more efficient models, and other factors such as the migration to wanner regions and the movement to more apartments and smaller homes, this will probably keep U.S. residential energy consumption at about its present level through the 1980s.

Refrigerators have considerable share of residential consumption. They can be, however, flexible loads because their operating time and consumption patterns can be changed to some extent. Accordingly, they can be selected as a target for the study of Demand Side Management plans. In this paper, two experimental models for a refrigerator are derived. In obtaining the first model, following assumptions are made: the ambient temperature of refrigerator is assumed to be constant and the refrigerator door is remained closed. However, in the second model the variation of ambient temperature and door-opening effects are considered according to some general patterns. Further, two strategies are proposed to reduce the annual electricity cost and electric power consumption at peak-load times. These strategies together with the aforementioned models form an optimization problem which is, then, solved by Particle Swarm Optimization algorithm. Simulation results indicate a reduction of more than 28.61% in the annual cost. Also, the annual electricity consumption has decreased more than 20.46% and load shifting from the peak periods has achieved about 40%. In addition, these approaches are implemented in laboratory and their performance is confirmed by experimental results.

Abstract In this study, an industrial combined cooling, heat and power (CCHP) generation system in a tile factory was simulated and optimized by the genetic algorithm approach taking into account electricity, heating and cooling loads. Modeling and optimization were performed based on thermodynamic, environmental and economic analyzes. A multi-criteria function (energy, economic, and environmental) called relative annual benefit (RAB) with a gas engine (with partial load operation) as the prime mover was used in the optimization process. The analysis was performed for three different scenarios of the possibility of selling (selling scenario or SS) and impossibility of selling electricity (no-selling scenario or NS) to the grid and the possibility of selling electricity with similar capacities. The designing variables including the number of prime movers, nominal capacity of movers, backup boiler capacity and the capacity of compression and absorption chillers were optimized. The CCHP system for the tile factory showed the better performance of selling scenario using a gas engine with a capacity of 5000 and 700. However, the nominal capacity of the prime movers in the selling scenario was higher than that in the no-selling strategy. The results showed that the relative annual benefit decreased by choosing a similar capacities.

Abstract A novel integrated energy system based on a geothermal heat source and a liquefied natural gas heat sink is proposed in this study for providing heating, cooling, electricity power, and drinking water simultaneously. The arrangement is a cascade incorporating a flash-binary geothermal system, a regenerative organic Rankine cycle, a simple organic Rankine cycle, a vapor compression refrigeration cycle, a regasification unit, and a reverse osmosis desalination system. Energy, exergy, and exergoeconomic methods are employed to analyze the suggested system. A parametric study based on decision variables is carried out to better assess the system performance. Four different multi-objective optimization problems are also carried out. At the most excellent trade-off solution specified by the TOPSIS method, the system attains 29.15% exergy efficiency and 1.512 $/GJ total product cost per exergy unit. The main output products are consequently calculated to be 101.07 kg/s cooling water, 570.44 kW net output power, and 81.57 kg/s potable water.

Abstract A net energy analysis (NEA) of three different residential solar pond scenarios is performed. A single home, a complex of twenty homes and a community system with district heating are considered. The designs considered, Rabl-Nielsen and Krass-LaViale, are studied for locations in Columbus, Ohio and in Northampton, Massachusetts. The analysis reveals that economies of scale and design considerations influence the net energy ratio (NER).