A stochastic integrated planning of electricity and natural gas networks for Queensland, Australia considering high renewable penetration

This study presents a security-constrained optimisation problem for co-expansion planning of integrated electric power and natural gas (SCEP-EPNG), taking into account multiple uncertainties of wind power generation and electric system load demand. The proposed model is a bi-level and bi-directional approach, which considers the flow of electric power to the natural gas network and vice versa through the power to gas and gas to power technologies, respectively. The security of the system is investigated from the static voltage stability point of view using the L-index approach. Also, the information-gap decision theory technique is adopted for modelling the uncertainties. The model aims at minimising the investment and operation costs as well as voltage stability index in a multi-objective optimisation problem, solved by the ɛ-constrained method, and the best compromise solution is calculated using the fuzzy-based min-max method. The proposed SCEP-EPNG model is implemented on the integrated Garver six-node electric power and seven-node natural gas networks, as well as integrated IEEE 24-node electric power and 12-node natural gas networks. Simulation results indicate the importance of voltage stability constraints in long-term energy planning. Furthermore, the model shows the effects of multiple uncertainties on co-expansion planning decisions. S i, j, d, t V, θ power flow of transmission lines, MVA V i, d, t voltage magnitude, p.u θ i j, d, t voltage angle, p.u χ i, j, a, t TL binary variable indicating installation status of transmission lines (1/0: installed/otherwise) χ n, m, z, t PL binary variable indicating installation status of gas pipelines (1/0: installed/otherwise) χ g, t GEN binary variable indicating the installation status of electric generation units (1/0: installed/otherwise) χ i, t WF binary variable indicating the installation status of wind farms (1/0: Installed/otherwise) α t uncertainty radius of uncertain parameters

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“…pr min ≤ pr n, d, t ≤ pr max (23) The natural gas demand (i.e. E n, d, t L ) in (19) consists of different consumers, represented as follows:…”

Section: Ngmentioning

confidence: 99%

Security constrained multi‐objective bi‐directional integrated electricity and natural gas co‐expansion planning considering multiple uncertainties of wind energy and system demand

Aldarajee¹,

Hosseinian²,

Vahidi³

et al. 2020

IET Renewable Power Generation

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“…) is obtained by (1) for each day d of year y assuming expansion plan k is added to the network. Investment cost includes cost of both transmission lines and generation units of expansion plan k. This problem is subjected to the constraints of electricity operation subproblem (1), which ensures feasible operation of the electricity system and the investment budget of electricity network as defined in (24).…”

Section: Cost Minimization Model Of Expansionmentioning

confidence: 99%

“…A centralized planning model for gas and electricity networks considering a joint N-1 and probabilistic reliability criterion is presented in [23]. A static stochastic cost minimization model is provided in [24] which considers renewable uncertainties as well as load growth and gas price uncertainties in expansion planning of gas and electricity networks. The model presented in [25] provides an integrated mixed-integer linear programming approach to security-constrained expansion planning of gas and electricity networks.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Attribute Expansion Planning Model for Integrated Gas–Electricity System

et al. 2018

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Gas-fired power plants are environmentally friendly because of their high efficiency rates and low CO2 emissions. On the other hand, the output power of renewable generators is stochastic, meaning that additional capacity must be held in reserve throughout the system. Gas-fired power plants are ideally suited to mitigate renewable uncertainties as they are more flexible and can easily be fired up in just a few minutes, and subsequently be shut down. Increased use of gas-fired power plants makes gas and electricity networks more dependent, so that adequacy in fuel supply of electricity network becomes a majority. However expansion planning of gas and electricity systems is accomplished by private gas and electricity companies, having no effective data exchange mechanism together. So there is a need to provide a model that coordinates the expansion planning of gas and electricity networks. On the other hand, expansion cost of either gas or electricity network and risk criteria of integrated energy system may have priority in decision-making process. With different challenging attributes, there is a gap in the literature to provide a model that takes into account the privacy of energy parties with a minimum data exchange, while considering different attributes in decision-making process. In this paper a multi-attribute decision-making (MADM) method for co-expansion planning of gas and electricity systems is introduced. The proposed MADM method supposes that a central entity as Ministry of Energy (ME) is responsible for coordinated expansion planning of gas and electricity networks, while taking into account the privacy of gas and electricity energy parties. Decision-making attributes are conflicting and the proposed method selects the best plan based on a compromise among the attributes. Different attributes including gas expansion cost (GEC), electricity expansion cost (EEC), minimum of maximum regret (MMR) and β-robustness (β_R) are considered to find the best plan with regard to the preferences of independent gas and electricity network operators. In this regard, two multi-attribute decision analysis methodologies are employed: analytical hierarchy process (AHP) is used as a simple way to weight and rank all the attributes objectively and find the relative importance of various plans, and the weighted sum method to provide a general composite index and finding the final appropriate plan. A real case study in the Khorasan province of Iran, which has a high penetration level of gas-consuming generation units (GCGU), is utilized to demonstrate the effectiveness of proposed MADM method. Results are compared with a Pareto optimal method to qualify the accuracy of proposed method.

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“…Shen et al [9] proposed a stochastic programming planning method for IES based on energy hub model, in which energy price uncertainties from bulk energy systems are considered. Nunes et al [10] developed an integrated planning approach for electricity and gas considering the effects of new policies toward less-intensive carbon technologies, in which a static stochastic cost minimisation model was formulated.…”

Section: Introductionmentioning

confidence: 99%

IES configuration method considering peak‐valley differences of tie lines and operation costs of power grids

Zhang

et al. 2020

IET Energy Systems Integration

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The peak-valley difference of power grid will be enlarged significantly with the increasing number of integrated energy systems (IESs) connecting to power grids, which may cause a high operation cost and voltage violations. This study proposes an IES configuration method considering the peak-valley difference of the tie line between IES and power grid. An optimal configuration model of IES is established, which takes the sum of the annual operating cost of the power grid and annual cost of IES as objectives, while the tie line transmission power between IES and power grid are considered as constraints. The elite energy reserve genetic algorithm and branch and bound method are used to solve the proposed model, while the optimal power flow is used to calculate the grid operation cost under a given transmission power of the tie line. The simulation results of an improved IEEE30 node system verify the validity of the proposed method.

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A stochastic integrated planning of electricity and natural gas networks for Queensland, Australia considering high renewable penetration

Cited by 33 publications

References 31 publications

Security constrained multi‐objective bi‐directional integrated electricity and natural gas co‐expansion planning considering multiple uncertainties of wind energy and system demand

Security constrained multi‐objective bi‐directional integrated electricity and natural gas co‐expansion planning considering multiple uncertainties of wind energy and system demand

A Multi-Attribute Expansion Planning Model for Integrated Gas–Electricity System

IES configuration method considering peak‐valley differences of tie lines and operation costs of power grids

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