Two-stage stochastic framework for energy hubs planning considering demand response programs

Mansouri, Seyed Amir; Ahmarinejad, Amir; Javadi, Mohammad Sadegh; Catalão, João P.S.

doi:10.1016/j.energy.2020.118124

Cited by 121 publications

(34 citation statements)

References 36 publications

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“…RTP-DRP for electrical demand (RTP-EDRP) can be formulated as Equations ( 34)- (38). 55 Equation (34) shows the total amount of electrical demand.…”

Section: Rtp-drpmentioning

confidence: 99%

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

Energy Science & Engineering

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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.

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“…RTP-DRP for electrical demand (RTP-EDRP) can be formulated as Equations ( 34)- (38). 55 Equation (34) shows the total amount of electrical demand.…”

Section: Rtp-drpmentioning

confidence: 99%

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

Energy Science & Engineering

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“…Nowadays the uncertainties in the energy supply-side and demand-side pose a significant challenge to the stable and reliable operation of the multi-energy system. Several approaches can be used to address those impacts, like stochastic optimization [13]- [14], chance-constrained stochastic optimization [15]- [16], fuzzy optimization [17], and robust optimization [18]. Zhang et al [19] proposed a bi-stage stochastic model to optimize both facility capacity allocation and the electric cooling ratio as well to improve the integrated performance under several uncertainties in energy supply and demand sides.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Demand-Controlled Operation Strategy for a Multi-Energy System

et al. 2021

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The multi-energy system is a promising energy-efficient technology to supply electric and thermal energy to end-users simultaneously, which can realize the energy cascade utilization. However, it is challenging to optimize the operation of multi-energy systems due to their inherent structural complexity, as well as the highly coupled nature of multiple energy flows and the uncertainty of renewable energy generation. This paper proposed a collaborative demand-controlled operation strategy for a multi-energy system, which consists of an upper-level model and a lower-level model. In the upper-level model, a robust linear optimization method is adopted to optimize the system operation in a day-ahead stage. In the lowerlevel model, a stochastic rolling optimization method is applied to achieve a dynamic adjustment to cope with the fluctuation in both renewable electricity generation and electric load. The multiple energy demandcontrolled strategy is also applied in the optimal operation strategy to achieve load shifting and to create flexibility in energy demand despite the "source-load" imbalance power fluctuation. A case study is carried out and simulation results verify the effectiveness and correctness of the proposed model of the coordinated operation framework.INDEX TERMS Multi-energy system; robust linear optimization; indoor temperature control; demand response; optimal operation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…The lower layer is a single-objective nonlinear operation optimization model, some of which are solved directly by intelligent algorithms [12], and others convert nonconvex into convex [13], using mathematical methods [11] or solvers [14] to solve. Some references further consider the uncertainty of the system, and use robust and multi-scenario methods for planning [15].…”

Section: Introductionmentioning

confidence: 99%

“…The upper layer is a multi‐objective nonlinear equipment optimization configuration model, and intelligent algorithms are often adopted for this layer, such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) [11]. The lower layer is a single‐objective nonlinear operation optimization model, some of which are solved directly by intelligent algorithms [12], and others convert nonconvex into convex [13], using mathematical methods [11] or solvers [14] to solve. Some references further consider the uncertainty of the system, and use robust and multi‐scenario methods for planning [15].…”

Section: Introductionmentioning

confidence: 99%

Optimal device capacity planning and strategy determination in an integrated energy system to promoting IES integration considering reliability value

Xueqian

Liu

2021

IET Generation Trans & Dist

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This paper proposes a novel framework for optimal planning of integrated energy system (IES) that takes into account its reliability value, which aims at promoting the power grid and IES to share the responsibility of the user's energy-supply reliability by market mechanism, thus promoting IES integration. First, an IES grid-connected electricity purchase price estimation model considering reliability is established, based on price theory and reliability theory, to quantify the reliability incremental value of IES. Then, a two-layer collaborative planning model, containing optimal equipment capacity configuration and strategy determination in the upper layer and optimal operation simulation and IES incremental reliability value in the lower layer, is established to coordinate the reliability guarantee capability of distribution network and IES while maximize the profit of IES. Among them, a practical approach of IES probabilistic reliability calculation is studied, considering double uncertainty and time-series matching of generation and demand, aiming at reducing the complexity of electricity price calculation embedded in planning model. Next, the intelligent krill herd algorithm and MILP are implemented to solve the model. Finally, a case is studied to verify the effectiveness of the model, and analyse the impact of load growth rate and user reliability requirements on the planning results.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Two-stage stochastic framework for energy hubs planning considering demand response programs

Cited by 121 publications

References 36 publications

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

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

A Collaborative Demand-Controlled Operation Strategy for a Multi-Energy System

Optimal device capacity planning and strategy determination in an integrated energy system to promoting IES integration considering reliability value

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