• Energy Research
• 2021-2025close
• CN close
• DE close
• AU close
• Applied Energy close

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Abstract The urge to increase renewable energy penetration into the power supply mix has been frequently highlighted in response to climate change. South Korea was analyzed as a case study for which the government has shown motivation to increase renewable energy penetration. Herein, a hybrid renewable energy system (HRES) including solar and wind energies were selected due to their relatively stable and mature technology. In addition, Power-to-X has been incorporated to cover other renewable energy options such as hydrogen and synthetic natural gas (SNG). Therefore, an approach of forecasting the weather characteristics and demand loading over a relatively long timeframe was implemented via deep learning techniques (LSTM and GRU) and statistical approaches (Fbprophet and SARIMA), respectively. A deployment strategy incorporating HRES and Power-to-X is then proposed in correspondence to the forecasted results of the 15 regions considered in this study. An extension of this, the reliability of the designed system is further assessed based on the probability of the demand losses with the aid of Monte-Carlo simulation. With the proposed deployment strategy, a total annual cost of 9.88 × 1011 $/year and a greenhouse gas reduction of 1.24 × 106 tons/year are expected for a 35% renewable energy penetration. However, only SNG shows relatively competitive cost (at 23.20 $/m3 SNG), whereas the average costs of electricity (0.133 $/kWh) and hydrogen (7.784 $/kg H2) across the regions are yet to be competitive compared to the current market prices. Nonetheless, the priority of deployment across regions has been identified via TOPSIS.

Abstract To transform the original centralized scheduling framework of integrated community energy systems (ICES), a decentralized scheduling method is proposed for the optimal operation of ICES. Firstly, linearized mathematical models of the electric distribution system and natural gas distribution system are established to eliminate the nonconvexity and nonlinearity of the multi-energy systems. Meanwhile, an improved energy hub model is proposed by introducing the state variable, which is able to obtain the optimal solution with better accuracy and higher efficiency. Secondly, a decentralized scheduling framework is developed via an improved consensus-based alternating direction method of multipliers (ADMM). By introducing several coupling links as consensus variables, the optimal operation of the ICES is decoupled according to the physical interactions among each sub-system, in which the original model can be solved parallelly in a decentralized manner. Only the consensus variables are exchanged among sub-systems so that the privacy and confidential items of each sub-system operated by different entities can be ensured. Finally, two different ICES test cases with different system scales are utilized to demonstrate the effectiveness of the proposed method, which is able to provide scheduling scheme same as the centralized method. Dynamic step size modification is further considered in the consensus-based ADMM. By updating the step size during each iteration, the computation expense of optimization process is reduced with less consuming time, iteration and apparent oscillation, by which the convergence performance of ADMM is improved with higher computation efficiency.