23 research outputs found
Operation and planning of energy hubs under uncertainty - A review of mathematical optimization approaches
Co-designing energy systems across multiple energy carriers is increasingly attracting attention
of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the
energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities
featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing
an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy
demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques,
with mathematical optimization being the predominant family of decision-making methods proposed in
the literature of recent years. In this paper, we summarize, review, and categorize research studies that
have applied mathematical optimization approaches towards making operational and planning decisions
for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic
programming, and chance-constrained optimization. The results of the review indicate the increasing
adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of
energy hubs.Web of Science117228720
Stochastic MPC for energy hubs using data driven demand forecasting
Energy hubs convert and distribute energy resources by combining different
energy inputs through multiple conversion and storage components. The optimal
operation of the energy hub exploits its flexibility to increase the energy
efficiency and reduce the operational costs. However, uncertainties in the
demand present challenges to energy hub optimization. In this paper, we propose
a stochastic MPC controller to minimize energy costs using chance constraints
for the uncertain electricity and thermal demands. Historical data is used to
build a demand prediction model based on Gaussian processes to generate a
forecast of the future electricity and heat demands. The stochastic
optimization problem is solved via the Scenario Approach by sampling multi-step
demand trajectories from the derived prediction model. The performance of the
proposed predictor and of the stochastic controller is verified on a simulated
energy hub model and demand data from a real building.Comment: 6 pages, 5 figures. Submitted to IFAC World Congress 202
Risk-Aware Stochastic Scheduling of Hybrid Integrated Energy Systems with 100% Renewables
Recently, ambitious endeavors have been carried out to facilitate the transition from traditional grids to hybrid interconnected energy networks in the form of grid modernization. Align to such efforts, this article aims at developing a novel framework for satisfying techno-economic-environmental goals in the grid modernization process. To this end, a detailed examination is conducted for the optimal exploitation of energy hubs (EHs) equipped with 100% renewables to pursue the environmental goal alongside intending technical and economic constraints. The energy conversion technology is adopted to enable the power-to-gas system for establishing multi-energy interactions among electricity and gas networks. Fully benefiting from renewable units has exposed the system to uncertain fluctuations that necessitate the modeling of uncertainties to achieve near-reality results. Hence, risk-averse and seeker strategies are developed based on robustness and opportunistic modes of the information gap decision theory (IGDT) method to deal with stochastic fluctuations of uncertain parameters. The integrated electricity and gas test system is considered to analyze the applicability of the proposed framework in modeling efficient multi-energy interactions. Given the obtained results, 43.68% more energy cost is reached for EHs when they adopted a robust strategy against uncertainties under the risk-averse strategy. Moreover, the proposed framework procured a rational decision-making model for balancing multi-energy in the hybrid energy grid with 100% renewables