Optimal energy flow in integrated energy distribution systems considering unbalanced operation of power distribution systems

Asl, Dariush Keihan; Seifi, Ali Reza; Rastegar, Mohammad; Mohammadi, Mohammad

doi:10.1016/j.ijepes.2020.106132

Cited by 26 publications

(6 citation statements)

References 31 publications

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“…Equation (37) models the output power of the PV units based on the air temperature and solar irradiation level at every year and in each scenario [5,57]. The calculated PV power in Equation (37) represents the total power of the PV unit at the maximum power point. Moreover, P PV ,C shows the nominal power of the PV unit at the maximum power point and the standard condition of R PV ,re f = 1000 W/m 2 , T PV ,re f = 25 • C and wind speed of one m/s.…”

Section: Pv/wt Modellingmentioning

confidence: 99%

“…These studies have focused only on the operational constraints and ignored the planning of different sectors. Next, [34][35][36][37][38][39][40][41][42] have studied the optimal operation of integrated electrical and heating DSs. These references neither considered the coordination between the TS and DSs nor the planning constraints.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal scheduling of the unbalanced integrated electricity‐heat networks has been solved in ref. [37] by applying a holomorphic‐embedding based optimal power flow approach. Risk assessment of the integrated electrical‐heating‐gas systems comprising PVs, WTs, CHPs, and boilers has been discussed in ref.…”

Section: Introductionmentioning

confidence: 99%

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A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Allahvirdizadeh

Shayanfar

Moghaddam

2022

IET Generation Trans & Dist

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This paper presents a tri‐level stochastic coordinated transmission and distribution system expansion (CTDS) planning considering the deployment of energy hubs (EHs) across the distribution systems (DSs). The first level of the proposed optimization approach deals with transmission system expansion planning (TEP), while the second level develops distribution system expansion planning (DEP). Market clearing is done in the third level to update the locational marginal prices (LMPs) for different distribution system operators (DSOs). The EHs include photovoltaic cells (PVs), wind turbines (WTs), combined heating and power units (CHPs), boilers, and electrical and absorption chillers. The proposed tri‐level approach is solved with an iterative algorithm based on the diagonalization approach and tested on the modified IEEE 30‐bus transmission systems (TS) with several DSOs. Each DSO includes 18‐bus electrical, heating, and cooling energy systems. The numerical analysis shows that the total cost of the transmission system operator (TSO) is not changed with implementing the uncoordinated approach. However, the total cost of the whole system is reduced by 6.54% with accommodation of the EHs in the uncoordinated approach. Moreover, the total cost of the TSO as well as the whole system are reduced by 41.11% and 16.96%, respectively, with the proposed (CTDS) planning.

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Section: Pv/wt Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Allahvirdizadeh

Shayanfar

Moghaddam

2022

IET Generation Trans & Dist

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“…However, the implementation of the numerical algorithm is complicated, and they may not converge when the objective function is discontinuous or contains multiple extreme points. Many intelligent optimization algorithms are used to solve the OEF problem of the IES, such as the genetic algorithm [16][17][18], teaching-learningbased optimization algorithm [19,20], whale optimization algorithm [21,22], particle swarm optimization (PSO) algorithm, etc. PSO, proposed by Eberhart and Kennedy [23], does not require the continuity and convexity of the objective functions and has a strong adaptability to the uncertainty of computational data.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm

et al. 2022

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The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs is an ideal method to solve this problem. In this study, a day-ahead optimal scheduling model for IES that included an electrical system, a natural gas system, and an energy hub (EH), was established. The proposed EH contained detailed models of the fuel cell (FC) and power to gas (P2G) system. Considering that the optimal scheduling of an IES is a non-convex complex optimal problem, a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on multistage chaotic mapping was proposed to solve it. The objective was to minimize the operating cost of the IES. Three operation scenarios were designed to analyze the operation characteristics of the system under different coupling conditions. The simulation results showed that the PCAPSO algorithm improved the convergence rate and stability compared to the original PSO. An analysis of the results demonstrated the economics of an IES with the proposed EHs and the advantage of cooperation between the FC and P2G system.

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“…Wang et al [6] constructed a user response model under peakvalley electricity prices, estimated the parameters of the model, and proposed a real-time update process for the model that adapts to the peak-valley TOU electricity price. Inspired by big data technology, Asl et al [7] predicted the parallel load based on random forest (RF) algorithm. Wang et al [8] put forward several application scenarios based on electricity consumption behaviors, such as decision support to electricity pricing, preparation of demand plan, and formulation of energy efficiency scheme.…”

Section: Introductionmentioning

confidence: 99%

A Smart and Safe Electricity Consumption Model for Integrated Energy System Based on Electric Big Data

Liu¹,

Tong²,

Li³

et al. 2020

IJSSE

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In the integrated energy system, the smart and safe electricity consumption requires complex computation and faces high safety risk. To solve the problem, this paper designs a smart and safe electricity consumption model for integrated energy system based on electric big data. Firstly, an aggregate return index was designed based on clustering degree and dispersion degree to automatically optimize the number of classes, and facilitate the k-means clustering (KMC). Next, the optimization criterion for the behavior features of smart and safe electricity consumption was proposed, in which the effectiveness and correlations of the features are measured by the amount of mutual information and the degree of correlation, respectively. After that, the authors put forward a feature optimization strategy for smart and safe electricity consumption behaviors. By this strategy, effective and independent features were selected to form a simplified feature set for the clustering of smart and safe electricity consumption behaviors. On this basis, a smart and safe electricity consumption model was presented for integrated energy system. The effectiveness of our model was confirmed through example analysis.

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Optimal energy flow in integrated energy distribution systems considering unbalanced operation of power distribution systems

Cited by 26 publications

References 31 publications

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm

A Smart and Safe Electricity Consumption Model for Integrated Energy System Based on Electric Big Data

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