Optimization of microenergy grid including adiabatic compressed air energy storage by considering uncertainty of intermittent parameters

In this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively.

Section: Rtp-drpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs

Allahvirdizadeh

Galvani

Shayanfar

et al. 2022

“…To handle the uncertainty, they implemented the information gap theory (IGTD) method. In another research, 22 power and thermal distribution networks are studied from the view of a compressed air storage system (CAES) as the EH. In Arabkoohsar and Andresen, 23 energy storage is modeled that is able to produce both electricity and thermal energy.…”

Section: Introductionmentioning

confidence: 99%

Bi‐level optimization of the integrated energy systems in the deregulated energy markets considering the prediction of uncertain parameters and price‐based demand response program

Toolabi

Soheyli

Sanei

et al. 2022

The proliferation of multienergy systems (MESs) in recent years has led to the higher efficiency of energy consumption in different sectors. MESs are generally studied in the context of energy hubs (EHs). Most of the previous works in the field of EH operation and scheduling are at the scale of residential households or communities. There is a research gap in proposing a framework in which the EH actively interacts with different energy networks. This article, however, is focused on the optimal operation and scheduling of the EH and two integrated energy grids such as power and heat distribution systems. The EH consists of combined heat and power (CHP), electric heat pump (EHP), thermal and electric energy storage systems, which are operated by an independent entity. The proposed optimization problem is based on the bi-level optimization method in which the EH is the leader and two networks are the followers. Nonlinear variables are also linearized using binary expansion and Big M methods. As another research novelty, uncertainties of load demand and photovoltaic (PV) systems' output power are taken into account using a mixed machine learning (ML)-based forecasting method. To enhance the flexibility of the system, a price-based demand response (DR) program is proposed based on the predicted load pattern of the mixed ML method. Simulation results show the efficiency of the optimization model. Also, the DR program has a significant impact on the system's cost by 40% improvement in the profit of the system.

Hybrid photovoltaic‐liquid air energy storage system for deep decarbonization

Chen

Feng

et al. 2022

Nowadays most photovoltaic (PV) plants usually use battery energy storage technology to smooth fluctuant power, but batteries have the drawbacks of a short lifetime and environmental pollution. The existing renewable power networks have serious problems with decarbonizing electricity on the end-user side. This paper investigates a new hybrid photovoltaic-liquid air energy storage (PV-LAES) system to provide solutions for the low-carbon transition for future power and energy networks. In this article, a local PV power plant cooperates with its maximum power point tracking (MPPT)-based boost converter, to generate low-carbon electricity with some uncertain fluctuations.Then a zero-emission-air-based LAES unit is used to absorb the surplus power from the PV plant, and also compensate power for the local load with inadequate power level. This makes the new system save a large proportion of power from main grids, which can also indirectly reduce greenhouse gas emissions. For the MW-class PV-LAES case, results show that the surplus renewable electricity (6.73 MWh) generates 27.12 tons of liquid air for energy backups during the day time, and then the LAES unit has a round-trip efficiency of 47.4% that can discharge a flexible power compensation to the load in the night. Accordingly, the grid power demand reduces significantly from 12.78 to 3.33 MWh in a day. In this way, the annual power savings is estimated to be 3449.25 MWh, and the corresponding carbon emission can be reduced by 2607.63 tons. Regarding the economic performance, the PV-LAES system presents a dynamic payback period of 9.33 years and an accumulated life-cycle net profit of $2,260,011. Overall, the proposed PV-LAES scheme is economically feasible from a life-cycle perspective, and can potentially realize flexible energy interaction with local renewables to achieve an integrated lowcarbon power generation and storage system.