Assessment of electricity price uncertainty impact on the operation of multi‐carrier energy systems

Zarif, Mahdi; Khaleghi, Sadegh; Javidi, Mohammad Hossein

doi:10.1049/iet-gtd.2015.0837

Cited by 33 publications

(21 citation statements)

References 17 publications

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“…To solve this operation problem, 1000 scenarios for market price, energy demands and solar radiation parameters, each with the probability of 0.001, are generated with the Monte Carlo method. The standard deviation used for generating the scenarios of electricity price is assumed to be 5% [19], standard deviation used for generating the scenarios of demands is considered to be 2% [35] and the standard deviations of hourly solar radiation are shown in Table 1.…”

Section: Scenarios Datamentioning

confidence: 99%

Risk‐constrained energy management of PV integrated smart energy hub in the presence of demand response program and compressed air energy storage

Jadidbonab

Dolatabadi

Mohammadi‐Ivatloo

et al. 2019

IET Renewable Power Generation

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Multi-carrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among different energy hubs' facilities and various operational parameters on the scheduling of the energy hub systems. This paper deals with the problem of optimal scheduling of smart residential energy hub (SREH) considering the different uncertain parameters. The effect of the market prices, demands and solar radiation uncertainties on the SREH scheduling problem is characterised through a risk-constrained two-stage stochastic programming model. The objective of the proposed scheduling problem is to determine the least-cost 24 h operation of the facilities that would cover the cooling, thermal and electrical demands. The Monte Carlo simulation method is applied to model the inaccuracies of solar radiation, energy demands, and electricity market prices. Additionally, a proper scenario-reduction algorithm is employed to reduce the number of scenarios and simulation burden. The proposed approach evaluates the impacts of different values of risk aversion parameter and the utility of the demand response program on the optimal solution of the proposed PV integrated SREH scheduling. Finally, an illustrative example is provided to confirm the efficiency and the applicability of the proposed approach. DR participation factor of demand response program Demand E, ADR amount of demand after applying demand response program Demand E, TDR amount of increased demand Demand E, shifted shifted demand from other hours TD percentage of increased demand η s auxiliary variable which is used to compute the CVaR ζ value at risk

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Section: Scenarios Datamentioning

confidence: 99%

Risk‐constrained energy management of PV integrated smart energy hub in the presence of demand response program and compressed air energy storage

Jadidbonab

Dolatabadi

Mohammadi‐Ivatloo

et al. 2019

IET Renewable Power Generation

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“…Energies 2018, 11,2834 3 of 18 sorting genetic algorithm II (NSGA II) based on a system which contains integrated electrical and natural gas networks [34]. However, in most of these studies, the optimal operation model was built and solved without considering the system structure and the interest bodies in the MECS.…”

Section: Modeling Of Mecsmentioning

confidence: 99%

“…According to [10], a framework is presented for optimal planning of energy hubs considering the physical constraints of the electricity and natural gas networks. In [11], suitable operation modes for the energy hub are presented and the roles of the energy hub in electricity price uncertainty are investigated. In addition, an optimization program is proposed to schedule the renewable distributed energy resources to achieve the load management for minimizing the customer payment cost [12].…”

mentioning

confidence: 99%

Hierarchical Energy Management for the MultiEnergy Carriers System with Different Interest Bodies

et al. 2018

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Multi-energy carriers system (MECS), in which diverse energy carriers and different energy systems interact together, has drawn the interest of many researchers in recent years. However, the optimal economic operational model of the MECS is a nonlinear, multi-variable, and multi-period problem, of which it is difficult to find the solution because several different energy flows are integrated in the system. To this end, three interest bodies in the MECS were investigated, which included the energy provider, the energy facilitator, and the energy consumer, and a hierarchical optimal economic operation strategy was then presented. A hybrid optimization strategy combining the swarm intelligence algorithm and interior point method was developed taking advantage of the merits of each method. Case studies were conducted to verify the effectiveness of the proposed hierarchical optimal economic operation strategy, whereby demonstrating that the proposed strategy can achieve rational energy allocation and decrease the energy cost in the MECS compared with traditional energy systems.

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“…Due to the detailed stipulation of land utilization types for each functional district in the city planning map, and given the different energy utilization characteristics, the weight ratios of the energy loads in each functional district are determined and analysed. Combined with the load density of each energy load, the integrated load density of the functional district is obtained with Equation (6).…”

Section: The Integrated Load Densitymentioning

confidence: 99%

“…Considering the diversity of a new district's load types and the relative closeness of the traditional electric/thermal/cold industry, the independent planning operation of the electric/ thermal/cold load is not conducive to improving the overall energy efficiency of a system. Multi-energy flow coordination planning is an effective strategy for energy efficient utilization [4,5], and the location of a reasonable integrated energy supply centre is particularly critical [6]. Some research on the optimal location of an integrated energy supply centre has been performed by domestic and foreign scholars [7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density

Wang

Duan

et al. 2018

Energies

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In view of the obvious distinctions among energy utilization characteristics in various functional districts, a method for optimizing the location of an integrated energy system for a typical new district is proposed. This method integrates the spatial location and energy load density of each functional district. Based on the timing characteristics and seasonal division of electric/thermal/cold load fluctuations, the improved fuzzy C means (FCM) algorithm combined with the density-based spatial clustering of applications with noise (DBSCAN) is applied to build different multi-scenarios in various functional districts, and a more representative 'typical maximum load value' is obtained. The architectural characteristics and the different energy utilization habits are combed on the weight of the electric/thermal/cold load for each functional district. By taking the minimum improved integrated load moment of the system and the minimum partition inner interval ratio as the objective functions, the location optimization model of an integrated energy supply centre in a typical new district is established, and the improved adaptive evolutionary immune algorithm is applied to the nested model. Finally, a typical new district in Northern China is taking as an example to verify the correctness and feasibility of the planning method proposed in this paper.

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Assessment of electricity price uncertainty impact on the operation of multi‐carrier energy systems

Cited by 33 publications

References 17 publications

Risk‐constrained energy management of PV integrated smart energy hub in the presence of demand response program and compressed air energy storage

Risk‐constrained energy management of PV integrated smart energy hub in the presence of demand response program and compressed air energy storage

Hierarchical Energy Management for the MultiEnergy Carriers System with Different Interest Bodies

Locating Optimization of an Integrated Energy Supply Centre in a Typical New District Based on the Load Density

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