Strategic offering of producers in the day-ahead coupled gas and electricity market including energy and reserve models

Baziar, Ali Asghar; Niknam, Taher; Simab, Mohsen

doi:10.1016/j.epsr.2021.107376

Cited by 8 publications

(22 citation statements)

References 29 publications

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“…e SP may not reach the optimal solution without the expression ω • |x − 􏽢 x| for􏽢 x values due to considering SDN operation limits. To compensate for this, the phraseω • |x − 􏽢 x| is added to the objective function of the second layer problem [60]. erefore, in this case, the determined value of x in this step is equal to the optimal values of P MS and Q MS in iteration v. Note also that the expression ω • |x − 􏽢 x| is nonlinear.…”

Section: Solution Methodmentioning

confidence: 99%

Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

Sabzalian

Pirouzi

Aredes

et al. 2022

International Transactions on Electrical Energy Systems

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With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.

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Section: Solution Methodmentioning

confidence: 99%

Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

Sabzalian

Pirouzi

Aredes

et al. 2022

International Transactions on Electrical Energy Systems

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show abstract

“…In Equations ( 34)-( 39), the OPF model is expressed in the heat network [5,16], which respectively represent the heat power balance in different nodes, the heat power passing through the pipeline, the temperature level in the slack node, the temperature limit in different nodes, the limitation of the heat power passing through the pipeline, and the limitation of the heat power produced by the heat production unit. In constraints ( 40)-( 48), the formulation of OPF in the gas network is stated [5,67]. Constraint (40) expresses the gas power balance in different nodes.…”

Section: Lower-level Problemmentioning

confidence: 99%

“…Constraint (40) expresses the gas power balance in different nodes. In the constraints ( 41)-( 44) of the linearized model, the calculation of the heat power passing through the pipeline based on the piecewise linearization technique [5,67] is stated. In fact, this power can be calculated from the nonlinear equation of G L g,l ,t ,w = 𝛿 g,l sign(𝜅 g,t ,w , 𝜅 l ,t ,w ) √ sign(𝜅 g,t ,w , 𝜅 l ,t ,w )((𝜅 g,t ,w ) 2 − (𝜅 l ,t ,w ) 2 ).…”

Section: Lower-level Problemmentioning

confidence: 99%

“…[72][73][74][75][76][77][78][79][80]. The characteristics of its transmission lines are presented in [67], where the resistance of each transmission line is 10% of its reactance. In this network, there are three EGUs, whose locations are shown in Figure 4a.…”

Section: Datamentioning

confidence: 99%

“…The total peak load in the gas network is 5000 MW. The hourly load is obtained from the product of the peak load and the load factor, and the daily curve of the load factor is drawn in the three networks in Figure 5a [5,67]. There are five EHs in these networks, whose characteristics and elements are shown in Table 2.…”

Section: Datamentioning

confidence: 99%

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Participation of energy hubs in the market clearing price‐based energy market considering generation and consumption uncertainty modelling

Farsani

Joorabian

Mortazavi

2023

IET Renewable Power Gen

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The market clearing pricing (MCP) model is used to operate electricity, gas, and heating networks (EGHNs) with flexible energy hubs (EHs) in the day‐ahead energy market. It's two‐level optimization. Its higher level refers to EHs' participation in the market and their profit maximization bound by the operational model of power sources, storage devices, and responsive loads in the form of EHs and their flexibility limit. In the lower‐level problem, the MCP model calculates energy price and evaluates EH performance's effects on the networks' technical and economic indices. It optimizes power flow in the networks to reduce centralized generator operating costs. This approach is linear approximation. Unscented transformation (UT) model load and renewable power uncertainties. This technique contains the fewest situations, reducing issue volume and computing time. Benders decomposition (BD) technique calculates energy prices, EHs, and networks. Finally, the numerical results show that the proposed scheme can extract the optimal economic and flexibility states of EHs. EHs' optimal performance enhanced energy networks' economic and operating status compared to power flow studies and promoted societal welfare by lowering energy prices.

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Reinforcement Learning-Based Bi-Level strategic bidding model of Gas-fired unit in integrated electricity and natural gas markets preventing market manipulation

Ren

Liu

et al. 2023

Applied Energy

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Strategic offering of producers in the day-ahead coupled gas and electricity market including energy and reserve models

Cited by 8 publications

References 29 publications

Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

Participation of energy hubs in the market clearing price‐based energy market considering generation and consumption uncertainty modelling

Reinforcement Learning-Based Bi-Level strategic bidding model of Gas-fired unit in integrated electricity and natural gas markets preventing market manipulation

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